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Government agencies carry out many events each year designed to determine future requirements and capabilities. These events include field experiments, surveys, interviews, simulations and workshops. Similar themes are evident across many of these events. Unfortunately, mechanisms for passing information from one event to the next, or for developing bodies of knowledge in the topical areas they address, have yet to be fully developed. The task is difficult on two fronts. In response to this need a knowledge management capability was developed to help provide structure for dynamic and static data and thereby, aid in the analysis of complex experimentation. The system warehouses qualitative and quantitative data and supports mining operations through a number of traditional and artificial intelligence‐based techniques. Described are the information architecture of the system, the knowledge processing methodologies, and the structure of the thematic data sets that form the knowledge ontologies.

The purpose of this paper is to utilize “Wuli‐Shili‐Renli (WSR)” system methodology to analyze complexity of knowledge and knowledge management and create models for knowledge management system (KMS) synthetically.

As a complex system, knowledge management should be analyzed and established by system methodologies. There are many system methodologies. Owing to WSR is characteristic of oriental culture; the paper introduces concept and working process of WSR system methodology in knowledge management from WSR perspective.

WSR system methodology can be used to analyze contents of WSR in KMS and to establish an effective KMS.

How to define WSR in KMS is a little difficult for users.

A new approach for system analysis and establishment of a successful KMS.

The paper shows how the WSR system approach can integrate quantitative approach and qualitative analysis to analyze and establish a KMS synthetically.

Knowledge management is one of the hottest topics in organizations today. Much of what is being proposed and accomplished is not novel by any means. Techniques, tools, concepts, and methodologies can easily be borrowed from the expert systems and artificial intelligence disciplines. This paper emphasizes that expert systems need to be an integral part of knowledge management if knowledge management is to succeed, and not simply be a fad!

The application of information technology (IT) to corporate environments is now widespread. Recent years have seen IT successfully applied to a wide range of corporate activities. Artificial intelligence, in the guise of knowledge‐based systems is on the point of delivering its long awaited potential. With many successful systems developed over the past three or four years, the time is now ripe for the corporation to develop a coherent strategy to investigate and exploit this technology. In this, the second of two articles looking at knowledge‐based systems, we examine how the corporation can exploit this technology for strategic and competitive advantage. It discusses the organisational implications of KBS as well as the impact and pay‐off that can be expected.

The purpose of the article is to identify and define an internal communications plan that may assist in the successful deployment of a knowledge management system.

This article focuses on communications strategies that may be adopted by organizations deploying knowledge management systems where resistance to the implementation of such systems may exist. Such resistance may be a result of issues such as trust, organizational culture and misunderstandings as to the nature of knowledge management and knowledge management systems. An internal communications plan is proposed with a focus on knowledge management systems which details target audiences, timing strategies and suggests possible media solutions including traditional collaterals and conversational support technologies to facilitate improved deployments of such systems.

The article proposes a framework of communications methodologies, timings and target audiences to be considered during the deployment, pilot and pre‐pilot phases of a KM system implementation. It concludes by discussing the opportunities to use conversational technologies in deploying KM systems to knowledge workers.

The deployment issues raised in this article are expected to be of value to KM system developers, project managers, systems analysts, business executives and academic researchers.

Online question-and-answer (Q&A) communities serve as important channels for knowledge diffusion. The purpose of this study is to investigate the dynamic development process of online knowledge systems and explore the final or progressive state of system development. By measuring the nonlinear characteristics of knowledge systems from the perspective of complexity science, the authors aim to enrich the perspective and method of the research on the dynamics of knowledge systems, and to deeply understand the behavior rules of knowledge systems.

The authors collected data from the programming-related Q&A site Stack Overflow for a ten-year period (2008–2017) and included 48,373 tags in the analyses. The number of tags is taken as the time series, the correlation dimension and the maximum Lyapunov index are used to examine the chaos of the system and the Volterra series multistep forecast method is used to predict the system state.

There are strange attractors in the system, the whole system is complex but bounded and its evolution is bound to approach a relatively stable range. Empirical analyses indicate that chaos exists in the process of knowledge sharing in this social labeling system, and the period of change over time is about one week.

This study contributes to revealing the evolutionary cycle of knowledge stock in online knowledge systems and further indicates how this dynamic evolution can help in the setting of platform mechanics and resource inputs.

The goal of this paper is to re‐evaluate the role of knowledge‐based systems (KBS) in knowledge management (KM). While knowledge‐based systems and expert systems were widely used in the past, they have now fallen from favor and are largely ignored in the knowledge management literature. This paper aims to argue that several factors have changed and it is now time to re‐evaluate the contribution that such systems can make to knowledge management.

The role of KBS in KM is explored through a comprehensive analysis of both the management and the technical literature on knowledge. The literature on KBS and expert systems is reviewed and some of the problems faced by them are highlighted. Some of the probable causes of these problems and some of the solutions that might be used to overcome them are indicated. The paper describes how knowledge systems (KS) could be used as an effective tool for managing knowledge.

The lack of success of KBS technologies for managing knowledge is mainly due to organizational and managerial issues. These problems can be solved through feasibility studies before system development activities. KS technology is now being successfully applied in a variety of newer domains that exploit its capabilities.

Some conclusions are drawn concerning integration of knowledge systems with knowledge management, problems of the early implementation of knowledge systems technology, and possible solution to overcome these problems.

The main contribution of the article is in re‐evaluating the role of knowledge‐based systems as a tool for knowledge management.

Inter-organizational knowledge sharing systems (IOKSS) are crucial for scientific, social and economic development especially in knowledge-intensive sectors. Knowledge sharing processes and systems will not only be challenged by individual and organizational factors but also by social, technical and political inter-organizational factors. This paper aims to investigate the impact of knowledge worker, peer, IOKSS, organization and sector factors on knowledge workers’ intention to share knowledge through IOKSS. Knowledge workers are the key stakeholders that enable the survival of IOKSS.

Data were collected, through a questionnaire, from knowledge workers in the education and the health sectors.

This study found that the human factors (related to knowledge workers and their peers) have significant direct impact on intention to share knowledge through IOKSS. Other factors, such as IOKSS system, organization and sector factors showed indirect impacts on knowledge workers’ intention to share knowledge through IOKSS. Such investigation can be very valuable for developing countries as technological innovations such as IOKSS can be crucial for training and building human resources, and national knowledge management.

Little is empirically known about the enablers of sharing knowledge in systems that connect organizations in horizontal linkage in a specific sector or industry. The study also adds value to under investigated region.

This paper aims to bring together the existing theory from knowledge management (KM), competitive intelligence (CI) and big data analytics to develop a more comprehensive view of the full range of intangible assets (data, information, knowledge and intelligence). By doing so, the interactions of the intangibles are better understood and recommendations can be made for the appropriate structure of big data systems in different circumstances. Metrics are also applied to illustrate how one can identify and understand what these different circumstances might look like.

The approach is chiefly conceptual, combining theory from multiple disciplines enhanced with practical applications. Illustrative data drawn from other empirical work are applied to illustrate some concepts.

Theory suggests that the KM theory is particularly useful in guiding big data system installations that focus primarily on the transfer of data/information. For big data systems focused on analytical insights, the CI theory might be a better match, as the system structures are actually quite similar.

Though the guidelines are general, practitioners should be able to evaluate their own situations and perhaps make better decisions about the direction of their big data systems. One can make the case that all the disciplines have something to add to improving how intangibles are deployed and applied and that improving coordination between KM and analytics/intelligence functions will help all intangibles systems to work more effectively.

To the authors’ knowledge, very few scholars work in this area, at the intersection of multiple types of intangible assets. The metrics are unique, especially in their scale and attachment to theory, allowing insights that provide more clarity to scholars and practical direction to industry.