Automation of university library operations: an analysis of the COVID-19 pandemic experience in the United Kingdom and Nigeria

Gbenga Adetunla, Diane Rasmussen Pennington, Gobinda Chowdhury

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This study examined how automation systems enhanced the operations of university libraries in Nigeria and the United Kingdom (UK) during the COVID-19 pandemic. It reviewed literature on the extent of automation, effectiveness of the use of automation technology and ascertained the challenges of automation in the operations of university libraries during COVID-19 lockdown in Nigeria and the United Kingdom. Findings revealed that most university libraries in the UK are fully automated while university libraries in Nigeria are either partially automated or not automated. During the COVID-19 lockdown, university libraries in the UK were able to provide services, while Nigerian libraries were unable to render services. UK and Nigeria were both faced with challenges during the lockdown. Some UK libraries could not optimally provide services due to insufficient licenses for digital content. In Nigeria, there were several socio-technical issues such as inadequate staff access to computers and the internet, poor power supply, and a lack of ICT skills. The study concluded that university libraries in Nigeria should embrace more technology if they want to be effective in their operations and be able to compete favourably in the global space.
Original languageEnglish
Title of host publicationInformation for a Better World
Subtitle of host publicationNormality, Virtuality, Physicality, Inclusivity
EditorsIsaac Sserwanga, Anne Goulding, Heather Moulaison-Sandy, Jia Tina Du, António Lucas Soares, Viviane Hessami, Rebecca D. Frank
Place of PublicationCham, Switzerland
PublisherSpringer
Pages238-244
Number of pages7
ISBN (Electronic)9783031280320
ISBN (Print)9783031280313
DOIs
Publication statusPublished - 12 Apr 2023

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13972
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Covid-19
  • university libraries
  • automation

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