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This paper provides an empirical characterization of elicitation purposes related to search tasks during mediated information retrieval. During retrieval, a human search intermediary consults with an information seeker about their information problem, and then conducts a search on the elicited topic, both elicit information from each other during the mediated search process. Elicitations during 30 mediated IR searches were examined. A total of 1,241 search intermediary elicitations within 17 IR elicitation categories and 485 information seeker elicitations within ten IR elicitations categories were identified. The paper provides an enhanced IR task model, including sub tasks, derived from our empirical findings. The implications of the findings are discussed.

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 report the design and implementation of the enhanced participant-driven photo elicitation method in a qualitative interview study, to assess the performance of the method to investigate a research topic in everyday life health information behaviour and to provide insights on how to effectively use this method in future research.

The author embedded the enhanced participant-driven photo elicitation in a qualitative interview study to examine people’s everyday life health information behaviour with activity tracking technology. The author assessed the types of visual data collected by the method, categories of elicitation enabled by the method and how the method contributed to key research findings of the interview study.

The enhanced participant-driven photo elicitation generated rich, unique and meaningful data that would be otherwise difficult to collect through conventional qualitative interviews. The method also elicited explanation, rationalisation and reflection during the interviews, which enriched and triangulated key research findings. This work validated the benefits of the general photo elicitation method such as aiding participants’ recall of experiences, enriching research findings and improving research validity. It also demonstrated that the enhancement techniques used in this study could generate rich and even research data across interviews.

This paper describes the design and implementation of the enhanced participant-driven photo elicitation method to augment a qualitative interview study with activity tracker users. The author provides recommendations for researchers to take full advantage of the method in future everyday life health information behaviour research.

The requirements engineering (RE) process constitutes the earliest phase of the information system development life cycle. Requirements elicitation is considered as one of the most critical activities of this phase. Moreover, requirements elicitation is still a challenge, especially in the distributed environment of so‐called inter‐company cooperative information systems (ICISs). The purpose of this paper is to propose a methodology to elicit requirements for an ICIS.

An analytical research approach was conducted. The current RE approaches, which are based either on goal, scenario or viewpoint were evaluated. Then the role of the elicitation technique selection step within the requirements elicitation process was examined. Finally the factors that affect this step in a distributed environment were studied. An example from the textile industry is used to illustrate the applicability of the proposed methodology.

Though existing requirements elicitation approaches based either on goal, scenario or viewpoint are effective techniques, they do not fit exactly to a cooperative distributed environment: more issues are created by inadequate communication, time difference between sites, cultural, language and characteristics diversity of stakeholders which affect the elicitation technique selection step and thus the requirements elicitation process. In order to tackle these issues, this paper presents a methodology called MAMIE (from Macro‐ to Micro‐level requirements Elicitation) to elicit requirements for an ICIS. A prototype tool has been developed to support the operation of the methodology.

The major limitation of the paper is that has not yet been tested in an existing organization.

To provide the analyst with well‐defined steps in order to elicit requirements of an ICIS. To understand the role of the elicitation technique selection step within the requirements elicitation process and identifying the factors which have an impact on this step. To select an appropriate elicitation technique according to these factors.

MAMIE integrates the three notions of goal, scenario and viewpoint to elicit requirements for an ICIS. The paper argues that these concepts may be used simultaneously and in a complementary way to improve the requirements elicitation process. Moreover, in order to increase the quality of the elicited requirements and thus the quality of the system‐to‐be, selecting an elicitation technique in MAMIE is not based on personal preferences but on situation assessment.

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.

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.

The purpose of this chapter is to explore the potential of photo-elicitation as a data generating method. Photo-elicitation is rarely used for data generation, despite the considerable promise of this method. Our empirical investigation focused on people's emotions and experiences of dual systems in Kazakhstan, a country currently undergoing change from the old Soviet system to a new market economy. In addition to semistructured interviews, we use photographs in order to enhance emotional connection and recall. We use the imagery as a device to generate data, and more specifically, data on individual and social perspectives that are integral to particular experiences. We argue that photo-elicitation can bring out peoples' lived experiences of the social context being investigated. We explain why and how to use the method in practice.

Despite the benefits of delegation, anecdotal and survey-based evidence suggests that firms do not optimally delegate decision-making authority. However, to date, no quantifiable evidence supports this claim.

We design an experiment to explore the superior’s choice between delegation and information elicitation. We also examine the effect of the superiors’ choice on the amount of effort provided by subordinates to gather decision-facilitating information.

We find that, compared to economic predictions, superiors delegate less often than they should. Subordinates exert lower effort when superiors elicit information than when superiors delegate the decision to them. As a result, superiors earn lower profit when they elicit information than when they delegate decision-making authority.

Our empirical evidence supports two main tenets espoused in the literature on the allocation of decision rights. First, the evidence of under delegation contributes to the literature which maintains that superiors’ tendency to under-delegate leads firms to become overly centralized.

By designing a novel experimental, we identify systematic ways in which behavior deviates from economic theory and contribute to the discussion on how firms utilize information. In particular, under delegation prevents firms from exploiting economies that arise from local capabilities and task specialization, and results in forgone profits.

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.

This paper presents a recommendation on how one requirement elicitation technique supports the other techniques in defining system requirement for a case-based system. A case-based lesson planning system aims to assist teachers in constructing quality lesson plans through its cycle which begins with case retrieval. To retrieve relevant lesson plans, appropriate inputs should be used and the intended output needs to be identified via suitable requirement elicitation techniques. The use of a single technique might result in inadequate requirement specification, thus affecting the quality of the output requirements as well as quality of the final information system.

Requirement elicitation was carried out in three phases: phase I involved document review, phase II was an interview and phase III used a survey. Respondents of the study comprised experienced teachers as well as new teachers. This research used both qualitative and quantitative approaches to answer the research questions, which involved semi-structured interviews, document review and survey to collect the relevant data. Documents were reviewed by analysing lesson plans from three different countries. In addition, a review of lesson plans prepared by teachers and the standard syllabus were carried out. Findings from the document review were used in structured interviews using a teach-back technique, sorting and matrix of attribute-values. A questionnaire was then constructed based on the interviews and document review.

The findings of this initial study, as part of a larger research investigation, would help in knowledge modelling and representation. This will contribute to effective case retrieval via good design of the system input and output. The study identifies important elements of a lesson plan according to their ranking. Keywords that were used by teachers as input for retrieval were identified together with the expected output.

The main goal of requirement elicitation is to specify complete and detailed requirements of the proposed system. There are two main types of requirement: functional and non-functional requirements. This paper only focuses on functional requirements – specifically case retrieval with appropriate input and output.

Various requirement engineering (RE) techniques can be applied in different phases of requirement elicitation. Suitable technique should be chosen at different phases of RE, as it is important for triangulation purposes. Incomplete RE will affect the modelling part of system development, and, thus, affect the design and implementation of an information system.

Software engineer or anybody involved in system development should plan accordingly for the RE process. They should be creative and reasonable in selecting suitable RE techniques to be applied.

This study aims to gain understanding of the various aspects of lesson planning. Crucial knowledge in lesson planning that was gathered from the elicitation phase is modelled to have a good understanding of the problems and constraints among teachers. The findings of this initial study, as part of a larger research investigation, would help in knowledge modelling and representation. This will contribute to effective case retrieval via a good design of the system input and output.