Adopting qualitative data in conceptual system dynamic modelling

Abstract

Qualitative data plays an important role in system dynamics (SD) modelling, particularly in model conceptualization. Among the various forms of data, information from the mental database of stakeholders is considered the most important which can be accessed through stakeholder engagement using interviews. However, formal, and systematic process of interview and data analysis is required. Existing approaches used for systematically analysing qualitative data were based on grounded theory approach. In this study, we introduced a coding approach that is formulated based on the principles of thematic analysis, saliency analysis (an extension of thematic analysis) framework and case study approach, and key strengths of existing methods. This alternative or formulated approach is focused on (i) coding the data from all stakeholder groups, (ii) establishing causal relationships from causal attributions of stakeholders and transform the causal relationships into causal maps and (iii) establishing and maintain strong links between causal maps and data source using data source reference table and software. We then demonstrate an application of the coding approach in a study about cost performance of road infrastructure projects in Nigeria to analyse qualitative data collected from 16 semi-structured interviews.