IDENTIFYING AN OPTIMAL ECOSYSTEM MODEL OF REFUGEE-RELATED BUSINESSES VIA AN ONLINE SYSTEMS-BASED EVOLUTIONARY LEARNING LABORATORY: A CASE IN UGANDA

The number of refugees in the world peaked at 26.3 million as of mid-2020. More than 75 percent of these refugees are in a protracted situation, one in which refugees find themselves in a long-lasting and intractable state of limbo. However, the budget for refugee protection and care has not been sufficient for years. Due to the limited humanitarian and developmental budget, the role of refugee-related businesses is gaining more attention. The aim of this study is to show the feasibility of the partially online systems-based Evolutionary Learning Laboratory (ELLab) approach in the COVID-19 era via a case study of Uganda and to identify the current systems model of refugee-related businesses, their leverage points, and the action plans necessary for the development of an optimal systems model for refugee-related businesses. The authors suggested the efficacy of the online system-based ELLab and provided new ways for the application of the ELLab method in the COVID-19 era. They also managed to identify the current systems model of refugee-related businesses, their leverage points, and their action plans through the ELLab process.