Accelerated Digital Transformation of Higher Education in the Wake of COVID-19: A Systematic Literature Review

The COVID-19 pandemic has accelerated digital transformation (DT) across various industries, including higher education (HE). In response to the dynamic demands of contemporary society, higher education institutions (HEIs) must swiftly adapt and transform. However, existing research has revealed a prevalent lack of strategic vision regarding DT in HE, often limited to the mere integration of technology. This study employs a systematic literature review (SLR) as a methodological framework to identify and categorize DT challenges and strategies within HE accelerated after the pandemic event. Findings from this SLR highlight four distinct categories of challenges and strategies in DT: Strategic-Administrative, Teaching-Learning, Technical-Technological, and Social-Cultural. Notably, the literature tends to focus more on identifying challenges, revealing an unbalanced emphasis compared to analyzing how HEIs are actively progressing in their DT efforts. Furthermore, there is a significant absence of impact analysis regarding these DT strategies within HE. To address these gaps, recommendations for future research are proposed, including (i) Exploration of strategic processes in HE toward DT, (ii) Empirical analysis of the Digital Maturity of HEIs, and (iii) Assessment of the impact of the strategic responses of HE toward DT. In conclusion, this study underscores the urgency for a more strategic approach to DT in HE, emphasizing the need to shift the focus from technology integration toward holistic, effective, and outcome-driven strategies. These recommendations aim to guide future research toward a more interdisciplinary and comprehensive understanding of DT within the realm of HE. Received: 15 April 2024 | Revised: 6 June 2024 | Accepted: 2 September 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in SLR Data selection and extraction.xlsx. Author Contribution Statement María Luisa Nieto-Taborda: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization. Rocci Luppicini: Conceptualization, Validation, Writing - review & editing, Supervision.