Computational social science for nonprofit studies: Developing a toolbox and knowledge base for the field

Abstract

How can computational social science (CSS) methods be applied in nonprofit and philanthropic studies? This paper summarizes and explains a range of relevant CSS methods, and highlights key applications in our field. Based on a typical design of empirical social science research, we define CSS as a set of computationally intensive empirical methods for data organization, concept representation, data analysis, and visualization. What makes the computational methods “social” is that the purpose of using these methods is to serve empirical social science research, such that theorization can have a solid ground. We illustrate the promise of CSS in our field by using it to construct the largest and most comprehensive database of scholarly references in our field so far, the Knowledge Infrastructure of Nonprofit and Philanthropic Studies (KINPS). Furthermore, we show that through the application of CSS in the analyses of the KINPS, our field’s knowledge and knowledge producing activities can be advanced, which is a core requisite for the development of our field as a discipline. We conclude the article with cautions for using CSS and suggestions for future research directions implementing CSS and the KINPS.

Disciplinary Contributions to Nonprofit Studies: A 20-Year Empirical Mapping of Journals Publishing Nonprofit Research and Journal Citations by Nonprofit Scholars

“…Bibliometric research has a well-established tradition, although bibliometric methods designed for large data sets have only recently been introduced to certain social science fields, including nonprofit studies (Ma et al, 2020;Ma & Konrath, 2018). Bibliometric analysis is diffusing across scientific domains because it can be used to help assess the state of research in a field of study or the impact of particular journals (Wan Utap et al, 2009).…”