Knowledge Mapping Process Model for Risk Mitigation in Software Management

Abstract

As software organizations try to mitigate operational and technical risk that occurs when using software, there is need to develop a knowledge intensive system to assist team members in mitigating both operational and technical risk. Knowledge mapping in risk mitigation context is in its infancy and has the potential to address both operational and technical risk faced by software organizations. However, as the amount and depth of organizational knowledge increases, it poses some challenges to software organizations. The key challenges for knowledge intensive organizations are how to identify, assimilate, disseminate, and apply these risk knowledge; particularly between different team members in same software development project. Thus this paper proposes a knowledge mapping process model to assist in mitigating risk (operational and technical) that occurs in software organizations. Knowledge mapping is the field within Knowledge Management (KM) that aims to optimize the efficient and effective use of the organization's knowledge. The mapping process model can support software management teams in measuring and treating risk, thus aiding decision making in software management. Data was collected using semi-structured interview through case study. The interview transcripts were coded and categorized using Nvivo software.

Mapping the field of knowledge management: a bibliometric analysis using R

“…The studies in the past argue that it is challenging to keep track of articles published in the area of KM, and as a result, various methods and techniques have been introduced, such as bibliometrics, Scientometrics and meta-analysis. The most important responsibilities of knowledge mapping are to visualize knowledge for knowledge seekers (Jnr et al , 2017). However, bibliometric analysis in the field of KM is at the nascent stage, and very few authors have applied bibliometric analysis in the field of KM (Gu, 2004; Gaviria-Marin et al , 2018; Gaviria-Marin et al , 2019; Pellegrini et al , 2020).…”