Leveraging Potentials of Big Data for Better Decision-Making and Value Creation in Nonprofit Organisations
- Publication Type:
- Thesis
- Issue Date:
- 2023
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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 that nonprofit organisations are deficient in using various data analytics due to a lack of expertise, low financial budgets and insufficient awareness of data analytics capabilities that enable those organisations to be data-driven and decision-making beneficiaries. Therefore, analysing and understanding donor behaviour remain critical challenges for nonprofit organisations. To address these research gaps, the researcher adopted a design science framework which helped to create an artefact (an intelligent decision support system) to analyse donor behaviour in nonprofit organisations. In addition, the framework led to the creation a design theory of the artefact which guides the design process and generalises the design requirements of such analytical and decision-making solutions for NPOs. 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. Furthermore, these variables assist the researcher in choosing the appropriate analysis model, from classification to predictions, and deciding the most beneficial machine learning techniques that generate a useful analysis for nonprofit organisations. 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. Finally, it contributes to the literature by producing descriptive and predictive analytics models to support nonprofits for leveraging applications of data analytics and big data awareness.
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