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A significant part of knowledge and experience in an organization belongs not to the organization itself, but to the individuals it employs. Therefore, knowledge management (KM) tasks should include eliciting knowledge from knowledgeable individuals. The paper aims to argue that the current palette of methods proposed for this in KM discourse is limited by idealistic assumptions about the behavior of knowledge owners. This paper also aims to enrich the repertoire of methods that can be used in an organization to extract knowledge (both tacit and explicit) from its employees by bridging KM and knowledge engineering and its accomplishments in the knowledge elicitation field.

This paper is based on extensive literature review and 20 years of experience of one of the authors in applying various knowledge elicitation techniques in multiple companies and contexts.

The paper proposes that the special agent (analyst) might be needed to elicit knowledge from individuals (experts) in order to allow further knowledge sharing and knowledge creation. Based on this idea, the paper proposes a new classification of the knowledge elicitation techniques that highlights the role of analyst in the knowledge elicitation process.

The paper contributes to managerial practice by describing a systemic variety of knowledge elicitation techniques with direct recommendations of their feasibility in the KM context.

The paper contributes to a wider use of knowledge engineering methodologies and technologies by KM researchers and practitioners in organizations.

The purpose of this paper is to develop a knowledge elicitation strategy to elicit and compile home energy retrofit knowledge that can be incorporated into the development of an intelligent decision support system to help increase the uptake of home energy retrofits. Major problems accounting for low adoption rates despite well-established benefits are: lack of information or information in unsuitable and usable format for decision making by homeowners. Despite the important role of expert knowledge in developing such systems, its elicitation has been fraught with challenges.

Using extensive literature review and a Delphi-dominated data collection technique, the relevant knowledge of 19 industry experts, selected based on previously developed determinants of expert knowledge and suitable for decision making was elicited and compiled. Boolean logic was used to model and represent such knowledge for use as an intelligent decision support system.

A combination of comprehensive knowledge elicitor training, Delphi technique, semi-structured interview, and job shadowing is a good elicitation strategy. It encourages experts to describe their knowledge in a natural way, relate to specific problems, and reduces bias. Relevant and consensus-based expert knowledge can be incorporated into the development of an intelligent decision support system.

The consensus-based and relevant expert knowledge can assist homeowners with decision making and industry practitioners and academia with corroboration and enhancement of existing knowledge. The strategy contributes to solving the knowledge elicitation challenge.

No previous study regarding a knowledge elicitation strategy for developing an intelligent decision support system for the energy retrofit industry exists.

The purpose of this paper was to build the MACTAK methodology, which aims to transform collective tacit knowledge into the explicit one using knowledge elicitation techniques, associated to quality tools structured by systemography, represent it in a symbolic language and production rules and model it in two expert systems which can assist the investigation of defect causes during the metal packaging production process.

The method applied in the research was classified as exploratory, because a preliminary study was conducted, to better suit the mapping methodology for eliciting collective tacit knowledge, to the reality which was intended to be known. Through studies and the application of the systemography technique, a methodology for the elicitation of collective tacit knowledge has been developed. It suggests a systematic sequence of activities, to map and transform collective tacit knowledge into the explicit one, on the production process which was studied.

The types of tacit knowledge were mapped and became explicit through the application of the methodology proposed. A knowledge management system was created, as such knowledge was validated by other mechanics during their training on the shop-floor, which resulted in a structure of unique and shared knowledge. They became explicit for being stored in a knowledge base and presented to its users through the expert system. It is concluded that the methodology of acquiring collective tacit knowledge helped on the reduction of rework index by standardizing the way used to investigate the cause of the defect, in the studied company.

The MACTAK methodology was developed for exclusive use in industrial processes where the following elements are presented: process, method, environment, raw materials, labor work, measurement and machine. In this method, the detection of the problem occurs from statistical data.

The methodology began in August 2010, and in October 2011, obtained as a result a reduction in rework cost equivalent to US$17,780.95.

The methodology is unique, as it refers to the systematic use of knowledge acquisition techniques and tools of quality, and the methodology has a characteristic of direct application in manufacturing processes. The beneficiaries, in this case, are mechanicals of production and quality inspectors that work at the operation level in the company. For the organizational and tactical level, the beneficiaries are engineers of production.

The purpose of this paper is to present an automatic Medical Knowledge Elicitation System (MediKES), which is designed to improve elicitation and sharing of tacit knowledge acquired by physicians. The system leverages the clinical information stored in electronic medical record systems, by representing the acquired information in a series of knowledge maps.

The system architecture of the proposed MediKES is first discussed, and then a case study on an application of the proposed system in a Hong Kong medical organization is presented to illustrate the adoption process and highlight the benefits that can be realized from deployment of the MediKES.

The results of the case study show that the proposed solution is more reliable and powerful than traditional knowledge elicitation approaches in capturing physicians' tacit knowledge, transforming it into a machine‐readable form, as well as enhancing the quality of the medical judgment made by physicians.

A prototype system has been constructed and implemented on a trial basis in a medical organization. It has proven to be of benefit to healthcare professionals through its automatic functions in representing and visualizing physicians' diagnostic decisions.

Knowledge is key to improving the quality of the medical judgment of physicians. However, researchers and practitioners are still striving for more effective ways of capturing tacit knowledge and transforming it into a machine‐readable form so as to enhance knowledge sharing. In this paper, the authors reveal that the knowledge retrieval and the visual knowledge representation functions of the proposed system are able to facilitate knowledge sharing among physicians. Thus, junior physicians can use it as a decision support tool in making better diagnostic decisions.

After setting the stage by briefly surveying knowledge elicitation techniques, this article presents a description of an iterative approach to the elicitation and representation of organizational knowledge called PreSERVe, which stands for prepare, scope, elicit, render, and verify. The method involves an initial process of preparing for knowledge elicitation, followed by an iterative process of assessing the scope of the endeavor, knowledge elicitation and rendering, and, verification. Use of the PreSERVe method is illustrated by a case study involving work with six senior engineers at NASA Glenn Research Center (NASA GRC), Cleveland, OH, USA.

This paper aims to explore the requirements of organizational knowledge management initiatives using requirements engineering techniques, identifying the optimal techniques configuration and serving as a management tool for knowledge engineers.

The method is selection attributes. Knowledge management enablers are characterized and mapped with the coverage capabilities of requirements engineering techniques, using the attributes of the elicited object and a box-plot analysis. The information is gathered from 280 references, 32 companies and 16 experts in requirements engineering.

Requirements of organizational knowledge management initiatives are got optimally by combining interviews, use cases, scenarios, laddering and focus group techniques. The requirements of structure and processes are more complex to identify, while culture requirements are the best covered.

Knowledge management enablers are analyzed according to the current studies and comprehension of engineering techniques.

Knowledge engineers need to consider the coverage capabilities of engineering techniques to design an optimal requirement identification and meet the objectives of organizational knowledge acquisition initiatives. Requirement engineers can improve the requirements identification by a staged selection process.

The requirements of knowledge management initiatives that impact the community can be identified and traced to ensure the knowledge objectives. Requirements related to culture and people, like shared values, beliefs, and behaviors, are also considered.

To the best of the authors’ knowledge, this is the first study about formal requirement identification of knowledge management initiatives in the organizational context, providing the optimal configuration. A novel staged process is proposed for requirements engineering techniques selection, analyzing the enablers at component level and identifying the attributes associated with the elicited object.

The purpose of this paper is to present various knowledge‐acquisition methods and to show how existing empirical research can be used for mapping between marketing problem domains and knowledge acquisition techniques. The key to doing this is to create a taxonomy of marketing problem domains.

This paper combines a thorough literature review with prima facie conceptualization to map a generic problem domain, and thereby provide guidance in the choice of knowledge‐acquisition technique for developers of expert systems in the field of marketing.

Recent empirical research in the field of expert systems shows that certain knowledge‐acquisition techniques are significantly more efficient than others for the extraction of certain types of knowledge within specific problem domains. It is found that protocol analysis, while fairly commonly used, is relatively inefficient for analytic problems. In the synthetic problem domain, interviewing proves to perform better for simple problems and worse for more difficult‐to‐model synthetic domains.

The findings suggest that it may be worth exploring some of the non‐traditional knowledge‐acquisition techniques when working on some types of applications. Further research could offer guidance in choosing the appropriate technique, with the aim of improving the quality, efficiency and development of the resulting system.

Designers of expert systems for marketing should consider interviewing and card sorting as the main means of knowledge acquisition for analytic problem domains, rather than protocol analysis as the main knowledge‐acquisition technique for analytic problem domains.

This paper is the first to suggest mapping between knowledge‐acquisition research and marketing problem domains.

Knowledge is a key driver for the competitive success of organizations, but about 90 percent of organizational knowledge is inside employees’ minds with personal essence; therefore, this paper provides valuable vision for managers by exploring knowledge acquisition (KA) techniques and personality type. The purpose of this paper is to examine KA techniques and explore the impact of personality type on the KA process in the aerospace industry.

This paper examines KA techniques through an empirical study involving 83 participants to take part in KA sessions. For exploring techniques, a questionnaire was used, and also the Myers-Briggs Type Indicator was used to identify participants’ personality type. The impact of personality type on KA processes was determined by correlation analysis.

Analyses confirmed some association between the type of personality and KA process. In addition, the findings of exploring questionnaire items showed that participants gave the laddering technique the highest rating.

The paper may be of high value to researchers in the field of KA, especially in aerospace industries, because there is very little experimental investigation of KA, and it also provides valuable information and guidelines that hopefully will help researchers to select appropriate KA techniques.

Experienced firefighters often make important decisions in fast-paced fire ground environments characterised by uncertainty and evolving conditions, mostly under considerable time-pressure. The nature of these environments inadvertently presents firefighters with novel situations that occasionally challenge their expertise, subsequently necessitating a reliance on intuitive as opposed to rational decisions. The purpose of this study is to elicit the tacitly held knowledge and intuitive thought processes that were used by 31 experts while managing a range of complex, non-routine fire incidents.

The study used a formal knowledge elicitation technique known as the critical decision method (CDM). CDM is a qualitative strategy that applies a set of cognitive probes to explore the cognitive processes that aid the performance of a complex task. This method was preferred to other cognitive task analysis methods as it specifically favours the use of retrospective incident accounts and incidents that were both challenging and memorable. Using the full CDM protocol, 31 experienced firefighters were interviewed across various fire stations in the UK and Nigeria (UK = 15, Nigeria = 16). The interview transcripts were coded, categorised and analysed using the emergent themes analysis approach.

The results from the study identified 134 decision points across the 31 incident accounts. A total of 42 salient cues sought by experts at each decision point were revealed and organised into a critical cue inventory. The identified cues were subsequently categorised into five distinct types based on the type of information each cue relayed to an incident commander. The study further developed a decision-making model – information filtering and intuitive decision-making model – that describes how experienced firefighters made difficult fire ground decisions amidst multiple informational sources. The model ultimately showed experts’ preferences for intuitive decisions as the default-thinking mode, with deliberation only required on few instances as conditions warranted. The study also compiled and indexed the cognitive strategies elicited from the expert firefighters into a competence assessment framework.

In light of existing debate about the accessibility of expert knowledge, the current study not only provides empirical evidence detailing the practical application of the CDM as a formal knowledge elicitation method but also delineates a range of cognitive outputs from the elicitation process that ultimately holds relevance for knowledge transfer from expert to novices. The study identified a range of training needs and discussed the practical implications of transferring expert knowledge into learning tasks that could subsequently aid the cognitive development of novices. In particular, the study proposed adopting the four-component instructional design model in organising the CDM outputs for training purposes.

While it is generally taken that experts, because of their extensive domain knowledge and well-developed schema, often perform considerably (and sometimes exceptionally) well when solving complex problems, finding a credible and objective method to model what experts know and do continues to pose a challenge, particularly when such revelation is crucially required for training purposes. This study is therefore timely since its tacit and intuitive knowledge outputs can now be applied to enhance the development of training curricula for novices. The learning tasks developed from the CDM outputs are hoped to facilitate organisational learning not only within the firefighting domain but also across other high reliability organisations. It is extremely important that expert knowledge is preserved in these domains especially in countries such as the UK, where the rate of real fires has been on decline, which in turn suggests that the quality of experiential knowledge required to manage complex non-routine fire cases may also be on decline. The current study also presents and discusses insights based on the cultural differences observed between the UK and the Nigerian fire services.

This paper aims to contribute towards understanding how safety knowledge can be elicited from railway experts for the purposes of supporting effective decision-making.

A consortium of safety experts from across the British railway industry is formed. Collaborative modelling of the knowledge domain is used as an approach to the elicitation of safety knowledge from experts. From this, a series of knowledge models is derived to inform decision-making. This is achieved by using Bayesian networks as a knowledge modelling scheme, underpinning a Safety Prognosis tool to serve meaningful prognostics information and visualise such information to predict safety violations.

Collaborative modelling of safety-critical knowledge is a valid approach to knowledge elicitation and its sharing across the railway industry. This approach overcomes some of the key limitations of existing approaches to knowledge elicitation. Such models become an effective tool for prediction of safety cases by using railway data. This is demonstrated using passenger–train interaction safety data.

This study contributes to practice in two main directions: by documenting an effective approach to knowledge elicitation and knowledge sharing, while also helping the transport industry to understand safety.

By supporting the railway industry in their efforts to understand safety, this research has the potential to benefit railway passengers, staff and communities in general, which is a priority for the transport sector.

This research applies a knowledge elicitation approach to understanding safety based on collaborative modelling, which is a novel approach in the context of transport.