KM/OM Implementation and Other Issues

Discussions at previous HICSS conferences have shown that there is no general agreement on definitions of knowledge management (KM) and knowledge management systems (KMS) success. A deep understanding of these concepts would help to design and implement efficient KM initiatives and systems. We present an exploratory research study to begin and facilitate a debate that will hopefully lead to a consensus definition of KM and KMS success. We chose an expert panel approach followed by two exploratory surveys to approach the KM and KMS success definition. The research shows only a few points of consensus. We present areas of agreement as well as of disagreement, which serve as a good starting point for further discussions on KM and KMS success

PurposeThe purpose of this paper is to propose a definition of KMS success.Design/methodology/approachThe paper uses a consensus‐building approach to derive the definition. An expert panel was used to generate a list of possible KM success definitions. A survey was used to identify a likely definition from this list. A second survey was used to further refine the proposed KM success definition. Finally, analysis of the survey comments was used to finalize the proposed definition.FindingsKM success is a multidimensional concept. It is defined by capturing the right knowledge, getting the right knowledge to the right user, and using this knowledge to improve organizational and/or individual performance. KM success is measured by means of the dimensions: impact on business processes, impact on strategy, leadership, and knowledge content.Research limitations/implicationsAn additional survey should be performed that tests the constructs of the proposed KM success definition. Additionally, future research should focus on identifying a set of measures that can be used to measure KM success and determining whether KM and KM System (KMS), success are the same or different constructs.Practical implicationsThe proposed definition of KM success provides practitioners with four dimensions that can be used to construct organization‐specific measures for indicating when their KM initiative is successful.Originality/valueThis is important, as the literature, while providing much support for identifying KM critical success factors, does not provide a definition of when KM can be considered successful. Knowing when a KM initiative is successful is important for organizations and practitioners.

The implementation of Knowledge Management Systems (KMS) has gained significant attention in recent years due to its potential to improve organizational effectiveness and competitive advantage. However, despite the benefits that KMS can bring, many organizations face challenges when attempting to implement these systems successfully. This literature review aims to explore and analyze the main challenges that organizations encounter during identifying and implementing the appropriate Enterprise Knowledge Management (EKM)for an organization. By examining existing research and theoretical frameworks, this study seeks to provide an in-depth understanding of the technologies, tools and techniques that impact the successful deployment of EKM in organizations. Through synthesizing the literature, this research will contribute to the body of knowledge on KMS implementation challenges, offering valuable insights for practitioners and researchers seeking to enhance their understanding of this complex and critical organizational process. The research will study the literature available in the area of EKM system identification and implementation.

Despite considerable academic and practitioner interest in knowledge management, success of knowledge management systems is elusive. This chapter provides a framework which suggests that KM success can be achieved by designing sustainable communities of practice. Communities of practice have proven to have significant economic and practical implications on organizational practices. A growing body of literature in KM recognizes the importance of communities that foster collaborative learning in organizations and almost all KMS have a ‘network’ component that facilitates connecting people in communities of practice. Evidence has shown that communities have been a key element in KMS of many companies including Xerox PARC, British Petroleum Co., Shell Oil Company, Halliburton, IBM, Proctor and Gamble, and Hewlett Packard.

This article will describe the background, basic infrastructure, and future trends of collaborative knowledge management (CKM) and enterprise knowledge management (KM) systems. CKM is about capturing the information that is in people’s heads and sharing it in a collaborative manner (e.g., an open, application-independent framework that allows all to have access to it) across the enterprise. In order to take advantage of the knowledge that exists in organizations, an authoring process must be undertaken that categorizes and places important information into a shared resource (often referred to as a “knowledge repository”) that can be assessed by all relevant stakeholders. Collaboration should be across departments, various types of employees, and different types of projects within the enterprise. Some of the important topic areas that will be discussed in this article are the focus on the benefits of CKM and enterprise KM systems and the recent attempts to define the return-on-investment (ROI) of sharing knowledge across organizations.

Most organizations face difficult challenges in managing knowledge for crisis response, but it is crucial for response effectiveness that such challenges be overcome. Organizational members must share the knowledge needed to plan for emergencies. They also must be able during an emergency to access relevant plans and communicate about their responses to it. This article examines the role and relevance of knowledge management (and knowledge management systems therein) in support of crisis response. We begin by discussing what knowledge management and crisis response mean. We move on to suggest why crisis response efforts within an organizational context, might benefit from knowledge management initiatives. Specific examples of how knowledge management efforts have supported crisis response in the past are then presented. We end by offering researchers with some suggestions for future research work in light of this subject domain.

Introduction. Today, in the era of the intellectualization of the economy, knowledge management plays an important role in building an effective strategy for the enterprise. The effective application of the knowledge management system at the enterprise reduces business risks, improves the speed and quality of decision-making processes, promotes innovative activity, and leads to an increase in the competitiveness of the enterprise. By applying an effective knowledge management system, an enterprise can achieve significant savings of resources, which is difficult to achieve using traditional management models and methods. The purpose of the paper is to research and analyze the knowledge management system of the enterprise, as well as to formulate recommendations for the development of effective technology for implementing the knowledge management system at the enterprise. Results. The knowledge management system of the enterprise today occupies a central place in the research of many domestic and foreign scientists. Its main elements are objects, subjects of knowledge, knowledge management goals and tasks, knowledge management processes, knowledge management functions and principles, approaches to knowledge management, as well as knowledge management culture. To improve the process of development and implementation of the knowledge management system at the enterprise, the authors proposed a recommended technology that consists of 9 consecutive stages, the priority of which are: identification of the business bottlenecks, analysis of the knowledge available at the enterprise, development of the enterprise’s knowledge management strategy, creation of a department at the enterprise that will be responsible for knowledge management, as well as the development of knowledge management tools. Conclusion. Implementation and application of the recommended technology for the development and implementation of the knowledge management system will allow the enterprise to clearly organize the knowledge management system and thus improve the efficiency of operations and achieve resource savings that cannot be achieved using traditional management mechanisms.

Literature on KM has greatly emphasized the role of IT as a decisive element in the success of KM while other capabilities are ignored or are not taken into account together. How these capabilities work together to support KM is a subject under-explored in the literature. The present study makes an attempt to bridge this gap and tries to answer the following research questions: (a) how organizational capabilities manifest themselves in the knowledge management processes; (b) how do the capabilities support the KM initiatives; and (c) how similar knowledge management systems can lead to different KM success patterns with difference in capabilities. Paper makes use of qualitative case study research design to explore the above questions in two mid-sized Indian IT Companies. Findings of the study show that companies operating in the same industry and having similar knowledge management systems can exhibit different level of success and acceptance based on their organizational capabilities.

The concept of Knowledge Management concerns the creation of structures that combine the most advanced elements of technological resources and the indispensable input of human response and decision making. This chapter addresses a very interesting topic—Knowledge Management (KM) and e-business initiatives within global organizations. It compares and contrasts the experiences of two global corporations as they have conceived and implemented KM initiatives. After in-depth secondary research on the subject, both companies developed their own KM in e-business strategy. The two case studies discussed in this chapter highlight Business-to-Business (B2B), Business-to-Consumer (B2C), and Business-to-Employee (B2E) initiatives at these two global organizations. This provides concepts and viewpoints related to the drivers of KM, the hurdles to KM in an organization, the elements of KM philosophy and process, how KM can come to be understood as a critical part of a company’s competitive strategy, and how to deploy a sustainable KM system that suits the business needs of an organization.

This paper considers the current problems of knowledge and information systems management, which are solved with the help of digital institutes for the purpose of the development of high-tech production in the conditions of Industry 4.0. The paper identifies the role of knowledge in the innovative development of industrial production, characterises the specifics of the influence of digital technologies on the technological development of companies and production processes, and focuses on the sensitivity of high-tech production to the level of intellectual support of production processes. Emphasis is made on the necessity of using comprehensive digital institutes, which can expand the boundaries of production companies and use the potential of digital platforms and systems in full. The research methodology combines traditional and new scientific directions and institutional theories in disciplines that study the research problem in the context of an increase in the intellectualisation of labour in the conditions of Industry 4.0. The research methodology covers general scientific methods of synthesis, analysis, the monographic method, observation, generalisation, and comparison. The paper contains important thoughts regarding the features of digital institutes from the position of their formation and influence on knowledge management in high-tech companies. It considers the structure and functions of the digital institutes of knowledge and information systems management in the conditions of Industry 4.0 and characterises selected institutes in the context of their influence on the digital transformation of the economy and the improvement of the state of knowledge management.

The increasing importance given towards knowledge management (KM) implementation for organisational success is hardly questionable resulting in significant attention directed towards KM systems. The extended IT industry has been quick to promote the notion that KM and in turn KM systems not only lowers cost structures and increases strategic flexibility but also facilitates knowledge creation and utilization. Conversely, many KM systems have proven unsustainable to date, exhibiting low quantity and quality of knowledge, with systems falling into disuse. Knowledge is generally assumed to be largely cognitive and therefore, highly personal and dynamic. Knowledge workers too do not like to be ‘managed’ and have the tendency to resist systematic attempts to manage and dictate contributions. KM systems on the other hand are seen to require systematization and control of knowledge. Such a mismatch has seen most KM systems today being reduced to nothing more than a management fad and an illustrious information management system failing to achieve its intended aims. This position paper espouses important tenets when operationalising KM. The paper highlights the need to contextualise KM initiatives by studying human practices as a development process with the introduction of Activity Theory. The paper also examines the need to co-create knowledge workers by understanding human consciousness and motivation as value with key drivers. The authors have also espoused the need to foster a conducive KM environment to spur KM implementation.KeywordsKnowledge management (KM)Knowledge management system (KMS)Activity theory (AT)Co-creation of value

BackgroundMental health and addictions (MHA) care is complex and individualized and requires coordination across providers and areas of care. Knowledge management is an essential facilitator and common challenge in MHA services.ObjectiveThis paper aimed to describe the development of a knowledge management system (KMS) and the associated processes in 1 MHA program. We also aimed to examine the uptake and use, satisfaction, and feedback on implementation among a group of pilot testers.MethodsThis project was conducted as a continuous quality-improvement initiative. Integrated stakeholder engagement was used to scope the content and design the information architecture to be implemented using a commercially available knowledge management platform. A group of 30 clinical and administrative staff were trained and tested with the KMS over a period of 10 weeks. Feedback was collected via surveys and focus groups. System analytics were used to characterize engagement. The content, design, and full-scale implementation planning of the KMS were refined based on the results.ResultsSatisfaction with accessing the content increased from baseline to after the pilot. Most testers indicated that they would recommend the KMS to a colleague, and satisfaction with KMS functionalities was high. A median of 7 testers was active each week, and testers were active for a median of 4 days over the course of the pilot. Focus group themes included the following: the KMS was a solution to problems for staff members, functionality of the KMS was important, quality content matters, training was helpful and could be improved, and KMS access was required to be easy and barrier free.ConclusionsKnowledge management is an ongoing need in MHA services, and KMSs hold promise in addressing this need. Testers in 1 MHA program found a KMS that is easy to use and would recommend it to colleagues. Opportunities to improve implementation and increase uptake were identified. Future research is needed to understand the impact of KMSs on quality of care and organizational efficiency.

Alavi and Leidner (2001, p. 114) defined knowledge management systems (KMSs) as “IT-based systems developed to support and enhance the organizational processes of knowledge creation, storage/retrieval, transfer, and application.” They observed that not all KM initiatives will implement an IT solution, but they support IT as an enabler of KM. Maier (2002) expanded on the IT concept for the KMS by calling it an ICT system that supported the functions of knowledge creation, construction, identification, capturing, acquisition, selection, valuation, organization, linking, structuring, formalization, visualization, distribution, retention, maintenance, refinement, evolution, access, search, and application. Stein and Zwass (1995) define an organizational memory information system (OMIS) as the processes and IT components necessary to capture, store, and bring to bear knowledge created in the past on decisions currently being made. Jennex and Olfman (2004) expanded this definition by incorporating the OMS into the KMS and adding strategy and service components to the KMS.

This paper discusses a three‐level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities. Practitioner notes What is already known about this topic Numerous learning theories exist with significant cross‐over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning. Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified. Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices. What this paper adds A three‐level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education. A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels. The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for practice and policy Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems. Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

This issue's look at learning organizations begins with a brief overview of the basic components of a learning organization – learning, memory, and organizational learning – and how they can be supported by today's information technologies.