Leveraging Potentials of Big Data for Better Decision-Making and Value Creation in Nonprofit Organisations

Abstract

In Nonprofit Organisations, analysing and understanding donor behaviour remain critical and challenging. While big data and machine learning techniques promise technical solutions to address this problem, how to design and build an intelligent decision support system based on these technologies remains unclear. The literature reveals certain challenges for analysing donor behaviour. The researcher adopted a design science framework which helped to create an artefact (an intelligent decision support system) to analyse donor behaviour. The results show that (by analysing public big data sets of donors from different sources) certain variables are essential to analyse donor behaviour in nonprofit organisations. These variables are the total amount of donations, the number of donations, gender, age, social level of income, educational level, and the frequency of donations which assist the researcher in choosing the appropriate analysis model, from classification to predictions, and deciding the most beneficial machine learning techniques. The researcher aims to provide a theoretical foundation design for developing an intelligent decision support system for analysing donor behaviour. The research contributes to decision support and data analytics research by presenting the capabilities of data analytics and machine learning techniques in the context that face the difficulty of understanding donor behaviour.