Journal of Information & Knowledge Management

Managing knowledge of plant maintenance in a power utility company is vital to the provision of a safe and reliable electricity supply to two million domestic and commercial customers. The business is built on a lot of expertise, techniques and experience of all of its employees from engineering, maintenance, safety and environmental control, to quality assurance. Knowledge auditing is usually carried out as the first critical step in the implementation of any Knowledge Management programmes in power utility companies. Although various knowledge auditing approaches have been proposed by some researchers and practitioners, there is a lack of a systematic approach in the way it is conducted, and the audit practice varies with different industries and companies. This paper presents a systematic knowledge audit approach, which has been successfully trial-implemented in a power plant.

The conventional way of using the Enterprise Resources Planning (ERP) applications is inadequate for the highly dynamic, complex and demanding business environment. The efficient use of the ERP system relies heavily on the know-how, experience and quality of the users. Since the knowledge of operating ERP system is difficult to be acquired, shared and diffused among the users, it takes time and money to train well-experienced users. As a result, this paper presents a framework of Knowledge-based Simulation System for Enterprise Resources Planning named KBSimERP. The KBSimERP provides an integrated learning environment for achieving three perspectives, which includes the knowledge acquisition, knowledge diffusion and performance measurement perspectives respectively. Hence, a prototype of the KBSimERP is built for production planning and control, and the performance of the system is preliminarily evaluated. Satisfactory results are obtained.

Bioinformatics is the science of managing, mining and interpreting information from biological sequences and structures. In this paper, we discuss two data-mining techniques that can be applied in bioinformatics: Neural Networks (NN) and Support Vector Machines (SVMs), and their application in gene expression classification. First, we provide a description of the two techniques. Then, we propose a new method that combines both SVM and NN. This way, we provide an effective knowledge management technique by utilising machine-learning techniques within the data-mining process. The knowledge obtained from the process is valuable as it is not possible to discover the same kind of knowledge using classical query processing or knowledge management techniques. Finally, we present the results obtained from our method and the results obtained from SVM alone on a sample data set.

Knowledge Management (KM) is an important issue in the 21st century and its execution has become a necessary process for enterprises to reach a level of sustainable development. Enterprises should value their own KM activities and accomplish the goal of sustainable operation through the promotion of effective KM activities. This research studies the impact of the critical success factors of KM on the implementation of KM activities, and its impact on new product development performance. The two intermediate variables, industry groups and the enterprise scales, are considered. After reviewing the literature, this research developed a theoretical model of the factors influencing these relationships. The research results found that the executive degree of critical KM success factors (five dimensions) revealed a significant influence on the executive degree of KM activities. The executive degree of KM activities also revealed a significant impact on new product development performance. As to the intermediate variables of industry groups and enterprise scales, this research proved that industry groups and the enterprise scales do not have any significant influence on the executive degree of KM activities or the new product development performance.

Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.

Distributed work environment suffers issues such as lack of mutual knowledge, ineffective knowledge sharing, lack of trust and coordination and interpersonal conflicts. The author conducted an empirical investigation in two projects each employing a hybrid offshore software development model to gain a deeper understanding of the underlying issues. The case study approach allowed for the study of phenomenon in a real-work context. Open-ended semi-structured interviews were conducted as a primary means of data collection. An interpretive analysis using a framework of social identity theory revealed that the in-group/out-group effect generated by a geographical faultline was further severed or diluted by two factors: individual mobility — a realistic opportunity to become a member of other group and the common expertise between two sub-teams. The study concluded with an observation that the absence of these factors resulted in strong group stereotypes, which in turn caused stronger inter-group behaviour.

This paper examines the causes of knowledge loss in a company undergoing a process of radical change. Using a methodology based on a single case study, the research highlights the critical role of middle managers in facilitating knowledge transfer. Middle managers facilitate both socialisation mechanisms for knowledge transfer and the maintenance of knowledge transfer systems based on information technology. The findings of the research suggest that eliminating layers of middle management will inevitably lead to knowledge loss, with consequential implications for competitive advantage. Based on the findings, a typology linking knowledge transfer to organisational forms is proposed.