INFLUENCE FACTORS OF THE TACIT KNOWLEDGE ELICITATION PROCESS: SYSTEMATIC LITERATURE REVIEW

Goal: This systematic review aimed at highlighting and performing an integrated discussion of the factors of the tacit knowledge elicitation process. Design/Methodology/Approach: The research method adopted in this study was (PRISMA) Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The databases, Science Direct, Web of Science, Scopus, and Emerald Insight were chosen based on the possibility of finding articles from collections such as Elsevier, Springer e Taylor & Francis. Search terms related to “knowledge Elicitation” are the following: "knowledge Elicitation" OR "knowledge acquisition" AND technique AND “Tacit Knowledge". Results: The main research findings listed by the article were the inclusion of a knowledge engineer considering the SECI model, the indication of predictability in the perception of episodic knowledge in a tacit knowledge elicitation process, and the hybrid adoption of knowledge elicitation techniques. Limitations of the research: The selection criteria were based only on articles written in the English language and taking into consideration the period from 2008 to 2020. Originality/value: The structure of this article was based on the indication of theoretical gaps and the need to deepen the themes underlying the process of eliciting tacit knowledge, which allowed a systematic exposure of a broad scenario that represents the scope and complexity.

A Survey of Domain Knowledge Elicitation in Applied Machine Learning

Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields.

Knowledge Elicitation with Aboriginal Australian communities

This paper provides techniques for engagement and data collection in researching with Aboriginal Australian cultures, acknowledging significant differences in forms of communication and usage of Information Systems to the mainstream Australian culture. An approach to trust in relationships is developed by interpreting cultural aspects arising from the diverse relationships to technology developed by Aboriginal users. This work uses the existing Honeycomb model for social media development as a base framework for collaborative web systems and online knowledge sharing in the Indigenous domain. We present a series of product development research projects based in universities in NSW Australia, in particular user experience studies, to explain the relationship between the researchers and users and the products that are created in terms of the model. Some concepts and processes fundamental to engagement with Aboriginal Australian communities in the supply of appropriate information sharing technology are discussed in this context. For if Aboriginal people are to engage in IS development, we are sharing the knowledge or the culture that is embedded in the technology which can have detrimental effects. Either we are asking them to enter the culture that created the technology, that is assimilate, or we use engagement in design to change that technology to suit the culture.

Investigating the role of Non-Technical Skills in Railway Traffic Operations through Expert Knowledge Elicitation

Many high-risk industries identify non-technical skills as safety-critical abilities of the operational staff that have a protective function against human fallibility. Based on an established non-technical skills classification system, methods for expert knowledge elicitation were used to describe non-technical skills in the specific context of train traffic control in the Netherlands. The findings offer insights regarding the skill importance for good operational outcomes, skill difficulty, categorization, and attitudes based on subject matter experts’ opinions. Substantial overlap between the employed non-technical skills framework and the observed expert classification was found, which might indicate that the experts utilize a mental model of nontechnical skills similar to the one used. Furthermore, considerations concerning the organizational culture and the attitudes towards change provide a promising outlook when introducing novel solutions to non-technical skill training and assessment.