![]() ![]() NPOs are expected to gather and analyse information from stakeholders to implement effective strategies for achieving their missions’ objectives. ![]() Museums, colleges, libraries, research centers, health agencies, human welfare, human rights organisations, religious organisations, and charitable foundations are examples of NPOs. NPOs differ from for-profit organisations as they are private, independent and self-generated funds. However, there are recent calls to apply such analytics to NPOs’ activities such as analysing donors behaviours. The professionals and practitioners from for-profit organisations are being urged to consider data analytics applications to maximise their potential and increase productivity. Keywordsĭata technology is evolving rapidly to address global needs. Results: The findings revealed three key conclusions may be drawn from the findings: (1) In the sphere of NPOs, robust and conventional statistical methods-based data analysis procedures are dominantly common at all times (2) Healthcare and public affairs are two crucial sectors that involve data analytics to support decision-making and problem-solving (3) Artificial Intelligence (AI) based data analytics is a recently emerging trending, especially in the healthcare-related sector however, it is still at an immature stage, and more efforts are needed to nourish its development.Ĭonclusion: The research findings can leverage future research and add value to the existing literature on the subject of data analytics. Methods: We characterise the associations between data analysis techniques and NPOs using, Bibliometrics R tool, a co-term analysis and scientific evolutionary pathways analysis, as well as identify the research topic changes in this field throughout time. This article presents a bibliometric study of 2673 to discover the use of data analytics in different NPOs and understand its contribution. ![]() However, using data analysis in non-profit organisations (NPOs) can help understand and identify more patterns of donors, volunteers, and anticipated future cash, gifts, and grants. Aim: Profitable companies that used data analytics have a double gain in cost reduction, demand prediction, and decision-making. ![]()
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