Journal of Information & Knowledge Management

This qualitative research study examined perceptions and current practices of structuring innovation in the knowledge management platform by questioning employees of companies in the greater New York metropolitan area. The 69 participants involved were enrolled in graduate-level distance learning courses offered by a fully-accredited higher education institution. Many participants in the study agreed with the premise of this paper that innovation policies can and should be structured into knowledge management systems. However, it was stated by a minority in the study group that officially setting up the innovation polices with the KM system is not truly feasible, although it is a worthy goal. Nevertheless, the concepts of innovation and knowledge are closely related, and within organisations today it is a tremendous missed opportunity if knowledge management systems are not developed and structured from the start with innovation in mind as a continual outcome.

This paper investigates how unstructured knowledge transfer is managed in ICT teams. In this context, unstructured knowledge refers to knowledge that is not supported by organisational strategy or any formal structure due to its ad hoc and versatile nature. The research utilises empirical material produced for use by the organisation, thus offering a realistic picture of transferring knowledge. Before knowledge management can be supported, it is necessary to understand how it is transferred and externalised in teams. To increase required understanding, this qualitative research investigated how team members in practice utilised knowledge that was created in the team, and how they attempted to support each other to benefit from existing knowledge. In the analysis, three frameworks were used, namely SECI, CIP and ba.

Several variables can influence the adoption of innovation. Human Computer Interaction research has focused on ease of use as the prime determinate of user satisfaction and adoption. This study explores the issues that influence the adoption, and further examines the knowledge of perceived risk as an influence. The study examines existing theory as it pertains to the diffusion of innovation from the perspective of the Internet, and assesses the extent to which Internet technology can be used in building long-term consumer relationships. Despite the high volume of shopping done on the Internet each day, many consumers fail to make online purchases because of continued reluctance to engage in transactions with intermediaries that are not familiar and trusted. Based on the previous studies, this study posits that perceived risk for security of online transactions is one of the factors influencing online purchasing behaviour, and has an effect on the continued use.

This paper explains the impacts of knowledge management (KM) on organisational levels in Sudanese small and medium sized organisations. Based on the processes and methods of organisational learning, this paper presents the impact of KM on organisational levels, namely processes, product, workers, and general performance. The paper is based on a questionnaire and the data is collected from 255 people within a medium size company case study in Sudan. There are various kinds of impacts that KM have on organisations and organisational levels; these effects appear in different sectors, including processes, product, workers, and general performance. The paper finds that the interrelated impacts of KM on organisations are positive at several sectors. The impact on one element might lead to synergistic impacts on another element as well. These findings tend to corroborate, in line with the global KM goals.

Associative classification is a branch in data mining that employs association rule discovery methods in classification problems. In this paper, we introduce a novel data mining method called Looking at the Class (LC), which can be utilised in associative classification approach. Unlike known algorithms in associative classification such as Classification based on Association rule (CBA), which combine disjoint itemsets regardless of their class labels in the training phase, our method joins only itemsets with similar class labels. This saves too many unnecessary itemsets combining during the learning step, and consequently results in massive saving in computational time and memory. Moreover, a new prediction method that utilises multiple rules to make the prediction decision is also developed in this paper. The experimental results on different UCI datasets reveal that LC algorithm outperformed CBA with respect to classification accuracy, memory usage, and execution time on most datasets we consider.

International business (IB) and management information system (MIS) literature have focused respectively on the adoption process in multinational corporations (MNCs) and on the adoption of technology in organisations. This paper investigates the process of technology adoption in MNCs and draws on the actor–network theory (ANT) in order to understand the process of technology adoption in MNCs. A case study of a French-owned subsidiary in Brazil is utilised for the conclusions. First, through data analysis, it is discovered that people established connections that differ in being either beneficial or detrimental to technology adoption. Next, it is proposed that the decision to adopt is constantly being renegotiated, whereas previous research assumes a single adoption choice for individuals and organisations. Finally, this paper shows how boundary objects may help MNCs to cope with the conflicting pressures of global integration and national responsiveness.

Matrix decomposition methods: Singular Value Decomposition (SVD) and Semi Discrete Decomposition (SDD) are proved to be successful in dimensionality reduction. However, to the best of our knowledge, no empirical results are presented and no comparison between these methods is done to uncover latent structures in the data. In this paper, we present how these methods can be used to identify and visualise latent structures in the time series data. Results on a high dimensional dataset demonstrate that SVD is more successful in uncovering the latent structures.