DIGITAL TRANSFORMATION IN PUBLIC RECORDS MANAGEMENT
This study examines the digital transformation around the world in the records management of public entities between 2019 and 2024, focusing on advancements, challenges, and best practices. Following a systematic review guided by PRISMA standards, 327 articles were analysed from databases including Scopus, Web of Science, Google Scholar. The findings reveal increased research activity since 2022, highlighting improvements in transparency and administrative efficiency enabled by technologies such as artificial intelligence and e-government. However, significant challenges persist, such as organisational resistance to change, digital exclusion in rural areas, and technological infrastructure limitations. Despite being based on secondary data, this analysis offers a practical and theoretical framework to guide public sector digitalisation policies, fostering more efficient and inclusive administrative environments. Key practices identified include continuous digital training and public-private partnerships, facilitating more sustainable technology integration. This study contributes to understanding digital exclusion and its impact on vulnerable populations, proposing strategies for equitable access to public services. Its theoretical and systematic approach provides a unique perspective on enhancing inclusive and sustainable digitalisation in the public sector.
Authors
- Ricardo Rafael Díaz Calderón (ORCID: https://orcid.org/0000-0002-4901-9322)
- Carmen Graciela Arbulú Pérez Várgas (ORCID: https://orcid.org/0000-0002-8463-6553)
- Hipatia Arlet Torres Serna (ORCID: https://orcid.org/0000-0003-2426-648X)
- Grimaldo Heredia Pérez (ORCID: https://orcid.org/0000-0001-5235-2786)
Institutions
- Office of the Comptroller of the Currency (US)
- Universidad César Vallejo (PE)
- Catholic University of Trujillo (PE)
Publication Details
- Journal
- Public Administration Issues
- Published
- 2025-09-27
- DOI
- https://doi.org/10.17323/1999-5431-2025-0-6-79-96
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00