A model for assessment of uncertainty in tacit knowledge acquisition

Abstract

Purpose Expert systems have come to the forefront in the modeling of problems. One of the major problems facing the expert system designers is to develop an accurate knowledge base and a meaningful model of uncertainty associated with complex models. Decision-making is based on knowledge, and decision system support needs a knowledge base as well. An adequate knowledge acquisition (KA) process leads to accurate knowledge and improves the decision-making process. To manage the risk of a medical service (twin pregnancy in this case) a knowledge management system was created. The captured knowledge may be associated with an uncertainty. This study aims to introduce a method for evaluating the reliability of a tacit KA model. It assisted engineering managers in assessing and prioritizing risks. The study tried to use this method in risk management and new case in the health domain. Design/methodology/approach In this study, relevant variables were identified in the knowledge management literature reviews and the domain of expertise management. They are validated by a group of domain experts. Kendall’s W indicator was used to assess the degree of consensus. On the basis of combined cognitive maps, a cognitive network was constructed. Using Bayesian belief networks and fuzzy cognitive maps, an uncertainty assessment method of tacit KA was introduced. To help managers focus on major variables, a sensitivity analysis was conducted. Reliability of model was calculated for optimistic and pessimistic values. The applicability and efficacy of the proposed method were verified and validated with data from a medical university. Findings Results show that tacit KA uncertainty can be defined by independent variables, including environmental factors, personality and acquisition process factors. The reliability value shows the accuracy of the captured knowledge and the effectiveness of the acquisition process. The proposed uncertainty assessment method provides the reliability value of the acquisition model for knowledge engineers, so it can be used to implement the project and prevent failures in vital factors through necessary actions. If there is not a satisficed level of reliability, the KA project reliability can be improved by risk factors. The sensitivity analysis can help to select proper factors based on the resources. This approach mitigated some of the disadvantages of other risk evaluation methods. Originality/value The contribution of this study is to combine the uncertainty assessment with tacit KA based on fuzzy cognitive maps and the Bayesian belief networks approach. This approach used the capabilities of both narrative and computational approaches.

Research on the Barrier Analysis and Strength Measurement of a Prefabricated Building Design

“…If the error between the ANP ranking and the approximate ranking of a barrier in a questionnaire exceeds two, then the expert corresponding to the questionnaire needs to be contacted again. Kendall's W test is suitable for assessing the degree of consensus among different experts [47]. The values of this test range from 0 to 1 [48], and values higher than 0.7 indicate an acceptable degree of consensus [49].…”

How does contract completeness affect tacit knowledge acquisition?

“…Contracts contain routines that support complex forms of interaction and communication by triggering a set of sequenced actions and activities (Li et al, 2010). Fourth, a complete contract defines key terms accurately and thereby helps overcome any misunderstanding linked with their respective terminology, firm jargon, and mental models (Vlaar et al, 2006); hence, it allows firms to effectively manage risks in alliances and reduces equivocality (Akhavan et al, 2018). As such, a complete contract facilitates sensemaking, and partners in alliances come to share synthesized knowledge, implicit assumptions and mental models (Kumar and Patriotta, 2011).…”

Conceptualising the moderating role of knowledge management within supply chain risks and supply chain risk management

“…All these are common terms for the acquisition of knowledge (Abd Rahman et al, 2013). As further elaborated by Akhavan et al, (2018), extracting and sharing expertise and experiences from different knowledgeable workers to solve a particular problem and develop understanding for its prevention is another definition. Modifying existing expertise and efficient acquisition of new knowledge are major implications for acquisition (Inkpen & Dinur, 1998).…”

“…The development of KM in SCRM is linked with efficient risk governance, and firms need to change their behaviour and systematically support SCRM. Akhavan et al, (2018) provided that effective risk management faced issues with implementation, for instance, "environmental complexity" and "individual cognitive constraints". Environmental complexity happens because of a lack of knowledge management, with the concerned trading partners, about the identification of the risks and the firm's aptitude to manage them.…”

“…The study of (Stranieri, Orsi, and Banterle, 2017) showed that there is an inconsistent relationship between uncertainty or risk and risk management. In a more recent study, (Akhavan, Shahabipour, and Hosnavi, 2018) argued that environmental complexity and individuals' perceptions are constrictions to the implementation of effective risk management in the supply chain. These scenarios happen because of insufficient knowledge about the appetite of risk and consequence.…”