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Knowledge loss resulting from an aging workforce continues to be a management nightmare that has obliged many organizational leaders to develop strategies aimed at documenting mission-critical knowledge. Yet knowledge captured and stored in repositories continues to lack quality. The purpose of this study was to explore the impact of integrating quality management into the tacit knowledge capture process. Further studies on the effects of personality in the knowledge capture processes could lead to insights on reasons for diminished knowledge quality. From a social perspective, the implementation of a model that encompasses personality and quality management could improve decision-making processes.
Complex networks have been found to provide a good representation of knowledge. In this context, the discovery process can be modeled in terms of a dynamic process such as agents moving in a knowledge space. Recent studies proposed more realistic dynamics which can be influenced by the visibility of the agents, or by their memory. However, rather than dealing with these two concepts separately, in this study we propose a multi-agent random walk model for knowledge acquisition that integrates both these aspects. More specifically, we employed the true self avoiding walk modified to incorporate a type of stochastic flight. Such flights depend on fields of visibility emanating from the various agents, in an attempt to model the influence between researchers. The proposed framework has been illustrated considering a set of network models and two real-world networks, one generated from Wikipedia (articles from biology and mathematics) and another from the Web of Science comprising only the area of complex networks. The results were analyzed globally and by regions. In the global analysis, we found that most of the dynamics parameters do not affect significantly the discovery process. Yet, the local analysis revealed a substantial difference in performance, depending on the local topology. In particular, dynamics taking place at the core of the networks tended to enhance knowledge acquisition. The choice of the parameters controlling the dynamics were found to have little impact on the performance for the considered knowledge networks, even at the local scale.
Coordinate Measuring Machines (CMM) have been widely used as a means of evaluating product quality and controlling quality manufacturing processes. Many techniques have been developed to facilitate the generation of CMM measurement plans. However, there are major gaps in the understanding of planning such strategies. This significant lack of explicitly available knowledge on how experts prepare plans and carry out measurements slows down the planning process, leading to the repetitive reinvention of new plans while preventing the automation or even semi-automation of the process. The objectives of this paper are twofold: (i) to provide a review of the existing inspection planning systems and discuss the barriers and challenges, especially from the aspect of knowledge capture and formalization; and (ii) to propose and demonstrate a novel digital engineering mixed reality paradigm which has the potential to facilitate the rapid capture of implicit inspection knowledge and explicitly represent this in a formalized way. An outline and the results of the development of an early stage prototype - which will form the foundation of a more complex system to address the aforementioned technological challenges identified in the literature survey - will be given.
Due to the difficulty of analysis of raw data, themes, and categories which is necessary in the process of formulating and constructing the theories in organizational science, the "cognitive mapping method" is introduced. This approach is designed as a method of understanding phenomena through people's senses making and eliciting the structure and content of the mental process with minimal interferences and potential biases of the researcher. This technique is a part of the interpretive paradigm and its basic concepts derived from George Kelly's "personal construction theory". Despite the importance of cognitive mapping method in structuring qualitative data, various aspects of this method is little known in our country, Iran, and rarely has been used in analyzing managerial and organizational studies. Therefore, this paper intends to introduce the cognitive mapping method, its origin, types, techniques, and implementations in the field of organizational studies. Moreover, for better understanding of the method, practical examples including cases and applications, and its strengths and weaknesses as well reliability and validity are explained in this paper.
A key highlight of this study is generating evidence of children ‘making aware the unaware’, making tacit knowledge explicit. The research explores the levels of awareness in thinking used by eight 7–8 year-old children when engaged in school-based genre writing tasks. The focus is on analysing children's awareness of their thought processes, using a framework originally devised in 1989 to investigate ways in which children can transform their tacit knowledge to explicit within the writing process. Classroom ‘think aloud’ protocols are used to help children ‘manage their knowledge transfer’, to speak the unspoken. The 1989 framework distinguished between four levels of thought that was viewed as hierarchical and ‘increasingly metacognitive’. However, there is little evidence in this study to show that levels of awareness in thinking are increasingly progressive and observations made during the study suggest that young writers move in and out of the suggested levels of thinking during different elements of a writing task. The reasons for this may depend on a number of factors that are noted in this paper. Evidence does suggest children in this age group are consciously aware of their own and others' thought processes both with and without adult prompting. By using collaborative talk, their awareness of these thought processes is highlighted enabling the co-construction and integration of new ideas into their existing knowledge base.
Purpose ‐ The goal of this paper is to present the main critical failure factors extracted from analyzing ten case studies of failure in knowledge management (KM) systems and projects and present a framework in which the failure factors are linked to the different stages in the cycle of KM implementation. Design/methodology/approach ‐ "Grounded theory" as a qualitative research technique has been applied to explore the main failure factors. Data was collected reviewing scholarly articles containing KM case studies (failure in KM implementation) and after an in-depth study ‐ applying grounded theory method ‐ the results of main critical failure factors categorized and analyzed in specific stages of implementing KM systems. Findings ‐ Through review and analysis of ten case studies, two main results were obtained. First, the main critical failure factors of KM projects were identified. Second, identified critical failure factors were traced along the KM implementation cycle. A framework is proposed that shows the critical failure factors' effect in each specific stage of the KM cycle. Research limitations/implications ‐ The results of this research are generalized based on cases from prior literature. However, the authors have tried to be as inclusive as possible to ensure a representative sample of reported KM failures. In addition, organizations do not provide accurate reports of failure in their projects because of their policies, the image of their organization, and privacy, which could affect the identification of all failure factors. Practical implications ‐ The findings are incorporated into a framework of failure factors in KM projects and the proposed framework can help practitioners in organizations to avoid factors that lead to the demise of KM systems in each stage of the KM project development cycle. This multi-case study research and its suggested framework are also useful for academics to gain a comprehensive view of KM critical failure factors for future studies. Originality/value ‐ To the authors' knowledge this study is the first of its kind to provide an integrated perspective of critical failure factors for the implementation of KM through the inspection of ten case studies and maps the failure factors on KM implementation cycle. It provides valuable information and guidelines that will hopefully pave the way for managers to avoid failure in implementation of a KM.
Manufacturing activities are characterised by high repetitiveness and the generation of vast amounts of data, which however remain underutilised and in most cases completely unexploited. However, new design and planning projects share numerous commonalities in their processes, materials and resources with past cases. Leveraging information, which is created and distributed among workers, departments and partners of a company through its transformation into knowledge can enable the coordination between the phases of product design and manufacturing, especially in the context of engineered-to-order products. Knowledge intensive industrial sectors that manufacture one-of-a-kind products or several product variants, such as the mould-making, can benefit from the reutilisation of past knowledge. This research work aims to the systematic capturing, storage and retrieval of knowledge about engineering projects and enable its reuse in the design and planning phases. The case-based data model, the components of the knowledge reuse engine and a set of knowledge enriched apps are presented. The application of the framework is demonstrated through a real-life case study with data coming from the mould making industry.
The current demographic development will lead to changing working conditions. The paper at hand presents methods for the investigation of expert knowledge within the company and how to document these information, widely independent from individuals, within a technology based learning environment, using virtual reality (VR) technologies and other media. Expert knowledge is often coded as tacit knowledge [1] and can be hardly verbalized by experts. The extraction of tacit knowledge requires methods that are based on stories. Beyond facts these stories also include tacit knowledge driven by emotions. One of the narrative methods presented in the paper will be the »triad interview« [2]. The documentation of these interviews within texts is not very sustainable and other methods for the documentation of expert knowledge are required. Especially in the field of maintenance that is focused in the paper, virtual reality is a suitable method for the documentation as technicians are familiar with drawings and visual content representations. Virtual reality learning environment can visualize working processes and keep the narrative structure that is required for transferring tacit knowledge. Besides, interactions as a typical characteristic of virtual reality applications allow the design of problem solving tasks. The paper presents the »triad interview« and the »working process analysis« [3] that are used for extracting the relevant information of a working process. For the didactical design of the learning tasks the method of the »complete action« [4] is applied. The paper will present the design of the several phases and their benefit for the learning process. Finally the results and experiences from an evaluation study within the organization will be presented.
This paper aims to provide the organizational roles to implement knowledge management. The focus is on how to devise and implement knowledge management where it is required and provides a common understanding about KM in implementation area. This paper also provides a general review on KM systems, KM architecture, KM process, KM strategy and KM critical success factors.
Purpose : The main propose of this study is determine appropriate knowledge acquisition techniques to extract tunnel expert’s knowledge.Design/methodology/approach: A proposive sampling method was used and data were collected via face-to-face interview based on a validated Knowledge Acquisition Questionnaire (KAQ). A total of 33 experts in tunnel industry who were presented by Iranian Tunnel Associated were identified and selected.Findings: The result of this study showed that semi-structured interview, timeline, think aloud problem-solving, commentary, teach back, concept map, process map, repertory grid technique, composition ladder, decision ladder , process ladder interview, matrix, Observational techniques have meaningful effects on elicitation of tunnel experts’ knowledge. Limitation: Briefly, problem of this project were large number of experts, Limited time for interviews,( In accordance to these experts’ avocations and huge responsibility) , and outspread geographical distribution (from Tehran, Khorasan, Khoozestan). Inadequate many experts know little about KM and it's advantagesOriginality/value : The innovation of this research is the first time this kind of research has been done in Iran. Until now, in Iran any working has not been done in the field of management and extraction knowledge experts.
In the artificial intelligence field, knowledge acquisition and reasoning are important areas for intelligent systems, especially knowledge base systems and expert systems. Knowledge acquisition is not an easy task since transferring expert knowledge required different methodologies and techniques based on expert domain, type of knowledge, knowledge engineer and expert domain. The success of the project depends on good knowledge management (KM). This paper presents a framework for manual knowledge acquisition. The proposed framework is for both types of knowledge (tacit and explicit). Using proposed framework allow organization to acquire knowledge from experts and get useful from it.
Purpose
This paper attempts to understand the reasons for knowledge management (KM) project failure.
Design/methodology/approach
Five well‐documented cases of KM project failure in the current literature are reviewed. For each case, the authors examine the circumstantial elements of the failure, including the rationale and intended objectives of the KM project, the outcomes of the project and the reasons that led to project failure.
Findings
From the review, two observations are made. First, KM failure factors fall into four distinct categories, namely, technology, culture, content, and project management. Second, KM projects can be traced along a three‐stage lifecycle, comprising initiation, implementation, and integration.
Research limitations/implications
The findings are discussed and finally synthesized into a model of KM project failure. The model serves as a starting‐point for future research in KM project implementation.
Practical implications
Practitioners may use the model as a risk identification tool for KM project implementation.
Originality/value
This paper highlights the fact that KM project failure is a reality with which both practitioners and researchers have to reckon. Additionally, it leverages on the experiences of five KM failure cases and develops a model that allows KM failure factors to be pre‐empted.
Purpose
The purpose of this paper is to look at how knowledge management (KM) has entered into a new phase where consolidation and harmonisation of concepts is required. Some first standards have been published in Europe and Australia in order to foster a common understanding of terms and concepts. The aim of this study was to analyse KM frameworks from research and practice regarding their model elements and try to discover differences and correspondences.
Design/methodology/approach
A total of 160 KM frameworks from science, practice, associations and standardization bodies have been collected worldwide. These frameworks have been analysed regarding the use and understanding of the term knowledge, the terms used to describe the knowledge process activities and the factors influencing the success of knowledge management. Quantitative and qualitative content analysis methods have been applied.
Findings
The result shows that despite the wide range of terms used in the KM frameworks an underlying consensus was detected regarding the basic categories used to describe the knowledge management activities and the critical success factors of KM. Nevertheless regarding the core term knowledge there is still a need to develop an improved understanding in research and practice.
Originality/value
The first quantitative and qualitative analysis of 160 KM frameworks from different origin worldwide.
Work towards the development of a new computer-assisted methodology for psychological study and intervention is described. This is referred to as the Personal Knowledge Methodology since it focuses on the elicitation and presentation of personal knowledge. Personal knowledge includes the knowledge individuals have of their life history, their behaviours, their moods, their relationships, their ambitions, and so on. Principles and techniques used in Knowledge Engineering form the basis of the design of the Personal Knowledge Methodology. At the heart of the methodology is the use of a suite of knowledge acquisition and modelling techniques. These are referred to as Personal Knowledge Techniques. Based on a review of a wide-range of literature, eight techniques were selected to be assessed for their possible use as Personal Knowledge Techniques. These included interview-based techniques, repertory grid techniques and diagram-based techniques. Two in-depth studies took place involving 18 participants and a total of 100 knowledge acquisition sessions. The results revealed that each of the eight techniques showed promise at efficiently capturing and structuring aspects of an individual’s personal knowledge. In addition, the techniques showed potential for providing help in allowing reflection and revealing insights. In particular, a technique based on the construction and use of a state transition network was found to be the most highly rated by the participants. A content analysis of the knowledge acquired formed the basis of an ontology of personal knowledge that would underpin many uses of the Personal Knowledge Methodology. The empirical work and analysis led to a number of ideas for future developments of the methodology and uses for the Personal Knowledge Techniques.
Data-limited fisheries are a major challenge for stock assessment analysts, as many traditional data-rich models cannot be implemented. Approaches based on stock reduction analysis offer simple ways to handle low data availability, but are particularly sensitive to assumptions on relative stock status (i.e., current biomass compared to unperturbed biomass). For the vast majority of data-limited stocks, stock status is unmeasured. The present study presents a method to elicit expert knowledge to inform stock status and a novel, user-friendly on-line application for expert elicitation. Expert opinions are compared to stock status derived from data-rich models. Here, it is evaluated how experts with different levels of experience in stock assessment performed relative to each other and with different qualities of data. Both "true" stock status and expert experience level were identified as significant factors accounting for the error in stock status elicitation. Relative stock status was the main driver of imprecision in the stock status prior (e.g., lower stock status had more imprecision in elicited stock status). Data availability and life-history information were not identified to be significant variables explaining imprecision in elicited stock status. All experts, regardless of their experience level, appeared to be risk neutral in the central tendency of stock status. Given the sensitivity to stock status misspecification for some popular data-limited methods, stock status can be usefully elicited from experts, but expert subjectivity and experience should be taken under consideration when applying those values.
Recent years have seen an upsurge of interest in knowledge. Leading organisations now recognise the importance of identifying what they know, sharing what they know and using what they know for maximum benefit. Many organisations employ knowledge engineers to capture knowledge from experts using the principles and techniques of knowledge engineering. The emphasis is on a structured approach built on a sound understanding of the psychology of expertise and making use of knowledge modelling methods and the latest web technologies.
Knowledge Acquisition in Practice is the first book to provide a detailed step-by-step guide to the methods and practical aspects of acquiring, modelling, storing and sharing knowledge. The reader is led through 47 steps from the inception of a project to its successful conclusion. Each step is described in terms of the reasons for the step, the required resources, the activities to be undertaken, and the solutions to common problems. In addition, each step has a checklist which lists the key items that should be achieved during the step.
Knowledge Acquisition in Practice will be of value to knowledge engineers, knowledge workers, knowledge officers and ontological engineers. The book will also be of interest to students and researchers of AI, computer science and business studies.
In order to better study team behavior and performance, much attention has focused on obtaining team members' mental models for the purposes of analysis and prediction. This paper describes a knowledge capture approach and a supporting graphical tool that together allow for direct acquisition of team mental models in the form of conceptual graphs. We performed acquisition experiments on over one hundred teams where team members used the tool to specify their team process. In addition to acquiring team members' models, non-team observers created “expert” models of the team process for comparison. We obtained good results on accepted measures of recall and precision compared to the “expert” models.
This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers’ and practitioners’ efforts in knowledge acquisition for more than a century.
The argument proffered in this paper is that use of enterprise social networking technologies can increase the accuracy of people's metaknowledge (knowledge of "who knows what" and "who knows whom") at work. The results of a quasi-natural field experiment in which only one of two matched-sample groups within a large financial services firm was given access to the enterprise social networking technology for six months revealed that by making people's communications with specific partners visible to others in the organization, the technology enabled observers to become aware of the communications occurring amongst their coworkers and to make inferences about what and whom those coworkers knew based on the contents of the messages they sent and to whom they were sent. Consequently only individuals in the group that used the social networking technology for six months improved the accuracy of their metaknowledge (a 31% improvement in knowledge of who knows what and an 88% improvement in knowledge of who knows whom). There were no improvements in the other group over the same time period. Based on these findings, how technologically enabled "ambient awareness" - awareness of ambient communications occurring amongst others in the organization - can be an important antecedent for knowledge acquisition is discussed.
The paper develops a framework and an understanding of the external knowledge absorption in nature-based tourism companies. The concept of potential absorptive capacity is the starting point for exploring how external knowledge (tacit and explicit) is absorbed and assimilated in tourism-innovation processes. Although knowledge is the engine that drives innovation, tourism firms can have problems and challenges when trying to absorb external knowledge for innovation. The main challenge is to access and absorb tacit knowledge. This type of knowledge is personal and sticky and therefore difficult to acquire and assimilate into the existing knowledge pool of organizations. Tacit knowledge is also difficult to imitate and is therefore important for developing original and competitive innovations. In this study, knowledge acquisition and assimilation processes and how knowledge challenges are handled are investigated by adopting a multiple, qualitative case-study strategy that focuses on three Icelandic whale-watching firms. This study contributes to the tourism-innovation discussion by identifying tourism-specific abilities for the acquisition and assimilation of knowledge. The sense-making and strategic interpretation of this knowledge are considered to be important subsequent steps in the innovation process.
As we celebrate the 50th knowledge acquisition conference this year it is appropriate to review progress in knowledge acquisition techniques not only over the quarter century since the conference series began but backwards through the millennia to the beginnings of knowledge capture and forwards through the foreseeable future to speculate on reasonable expectations, appropriate targets and potential surprises in the next quarter century.“Time present and time past are both perhaps present in time future, and time future contained in time past. What might have been is an abstraction remaining a perpetual possibility only in world of speculation.” (T.S. Eliot, Four Quartets).
This special issue celebrates 25 years of Knowledge Acquisition and centers around a breathtaking essay by Brian Gaines, the founder and former Editor-in-Chief of our journal, who discusses the processes of knowledge acquisition, knowledge representation and knowledge sharing across the millennia, and emphasizes the essential role that these processes have played throughout history, as engines of human evolution. In addition to Gaines' paper, this issue also comprises ten short contributions from a number of other prominent Knowledge Acquisition researchers. These provide a broad range of perspectives on the field and reflect the vivacity and diversity of the research in this area. While the Knowledge Acquisition area is by and large healthy and thriving, the shift in the past decade from intelligent problem solving to data acquisition and management can be seen as somewhat ‘reductionist’ with respect to the original ambitions of the community. This special issue provides a timely reminder of the ambitious goals of the field, its interdisciplinary ethos, and the sheer fun that the community has had in the past 25 years in pursuing the objective of building intelligent and symbiotic systems. I trust that our readers will be inspired and excited by this set of brilliant essays.
It is recognized by academics and the community of practice that the management of people plays an important role in project management. Recent people skills research expresses the need to develop a better understanding of what good people management is. This paper proposes what project management practitioners consider to be skills and behaviours of an effective people project manager. A combination of literature review, face to face interviews and focus group meetings was applied to complete the research objective. Six specific skills and associated behaviours were identified as being most important. The results suggest that project managers would benefit from adopting these skills and behaviours to strengthen their managing people skills and behaviours to improve the successful delivery of projects. The findings also suggest that some skill sets and behaviours may be more appropriate for application in certain project environments such as IT or the construction industry.
The ability to manage knowledge has become increasingly important in today’s knowledge economy. Knowledge is considered a valuable commodity, embedded in products and in the tacit knowledge of highly mobile individual employees. Knowledge management (KM) represents a deliberate and systematic approach to cultivating and sharing an organization’s knowledge base. It is a highly multidisciplinary field that encompasses both information technology and intellectual capital. This textbook and professional reference offers a comprehensive overview of the field of KM, providing both a substantive theoretical grounding and a pragmatic approach to applying key concepts. Drawing on ideas, tools, and techniques from such disciplines as sociology, cognitive science, organizational behavior, and information science, the text describes KM theory and practice at the individual, community, and organizational levels. It offers illuminating case studies and vignettes from companies including IBM, Xerox, British Telecommunications, JP Morgan Chase, and Nokia. This second edition has been updated and revised throughout. New material has been added on the information and library science perspectives, taxonomies and knowledge classification, the media richness of the knowledge-sharing channel, e-learning, social networking in KM contexts, strategy tools, results-based outcome assessments, knowledge continuity and organizational learning models, KM job descriptions, copyleft and Creative Commons, and other topics. New case studies and vignettes have been added; and the references and glossary have been updated and expanded.
Knowledge is becoming much more important for individuals and organizations than before. Knowledge management (KM) has been proposed as a methodology that can manage knowledge in organizations. However, KM may also have a nature, knowledge inertia (KI), stemming from the use of routine problem solving procedures, stagnant knowledge sources, and following past experience or knowledge. It may enable or inhibit an organization's or an individual's ability on problem solving. In order to explore to what extent, this research investigates several issues. First, types of knowledge have been specified. Second, knowledge from problem solving has been classified and understood. Third, inertia from knowledge is illustrated with some cases. Fourth, circulation of knowledge types in terms of avoiding KI is described. Finally, a case study of a military training institute implementing training revolution and overcoming KI is demonstrated. The proposed knowledge-based architecture investigates the mechanism of case base, heuristic base, and rule base that incorporates explicit knowledge, tacit knowledge, and procedural knowledge in support of managing knowledge and dealing with inertia.
Analyzing and Documenting Organizational Knowledge Acquisition (A Case Study
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The Lessons Learned Handbook: Practical Approaches to Learning from Experience
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