Exploring the Evolution of Digital Transformation Research in Non-profit Organisations: A Bibliometric Analysis

Michele Cipriano () and Stefano Za ()
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Michele Cipriano: University “G. D’Annunzio” of Chieti-Pescara
Stefano Za: University “G. D’Annunzio” of Chieti-Pescara

A chapter in Technologies for Digital Transformation, 2024, pp 215-238 from Springer

Abstract: Abstract In recent years, research on Digital Transformation (DT) has increased significantly in relation to the need to manage uncertainty and unexpected events, such as the COVID-19 pandemic. In particular, among the studies examining economic, societal, and industry-related implications, DT initiatives emerge as a global priority in the “new normal” toward resilience, well-being, and Sustainable Development Goals (SDGs). Accordingly, although a significant number of reviews summarising the DT debate populating the literature, the IS community clamour for deepening the current knowledge of the DT process by advancing theoretical diversity. Among other, DT initiatives in non-profit organisations (NPOs) seems to be only partially explored. For this reason, our aim is to advance the comprehension of the DT process in NPOs by performing a bibliometric analysis of the literature debating this specific phenomenon. To this end, we extracted 586 contributions published from 1983 to 2022 using the Scopus database. Then, we investigate the conceptual structure of the DT discourse concerning NPOs. The outcome of this paper is twofold: (1) to quantitatively describe the existing research and its evolution by considering different sources; (2) to recognise the main topics characterising the DT discourse in NPOs by performing a co-word analysis. This preliminary study draws a more comprehensive overview of the debate compared to previous qualitative research, exploring some novel discussions that could not be still addressed in journal articles.

Keywords: Digital transformation; Non-profit organisations; Bibliometric analysis; Co-words analysis; Social network analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-52120-1_13

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