Bibliometric Analysis of Existing Knowledge on Digital Transformation in Higher Education

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Abstract

Higher Education Institutions (HEIs) have been feeling great pressure to advance in digital transformation. This pressure has been intensified with the outbreak of the COVID-19 pandemic at the end of 2019. Because the digital transformation of HEIs has been attracting a growing number of publications, the present study sought to carry out a bibliometric analysis of such titles. For this purpose, 643 relevant documents were identified from the Scopus database in January 2022. The descriptive results show an accelerated growth of the relevant literature, with conference papers being the main form of publication, followed by articles, conference reviews, and book chapters. The areas with which the majority of documents were associated were computer science, followed by social science, engineering, and business and management. An analysis of the co-occurrence of terms based on the titles and abstracts enabled the identification of three thematic areas of interest: 1) digital transformation in teaching, particularly under the pressure exerted by COVID-19; 2) environmental influences on the digital transformation of HEIs; and 3) enabling technologies for digital transformation. A longitudinal analysis also based on titles and abstracts allows us to see how the primary focus shifted from the economic issue (in 2019) to the COVID issue (in 2021). This study concludes by discussing the theoretical and practical implications of the findings, demonstrating as a particularly interesting area for future research the study of the digital transformation of HEIs in a future post-COVID scenario.

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APA

Cruz-Cárdenas, J., Ramos-Galarza, C., Guadalupe-Lanas, J., Palacio-Fierro, A., & Galarraga-Carvajal, M. (2022). Bibliometric Analysis of Existing Knowledge on Digital Transformation in Higher Education. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13517 LNCS, pp. 231–240). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-22131-6_17

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