Viva Voce |Master's | Matildah Muchinga | Approaches to Improve Uptake of Institutional Repositories

Postgraduate oral examination involving Matildah Muchinga [1], a member of the DataLab research group [2] at The University of Zambia [3].
Viva Voce Details
Candidate: Matildah Muchinga
Dissertation Title: Identifying Effective Approaches for Improving the Uptake of Institutional Repositories Content in the Higher Education Institutions in Zambia
Date of Oral Examination; March 21, 2024
Supervisor: Dr. Lighton Phiri
Abstract
Higher Education Institutions (HEIs) are essential for academic research in Zambia. Despite the active research conducted by students and academic staff within these HEIs, the resulting output faces challenges in terms of visibility and uptake. There are 11 HELs in Zambia (six public and five private) with functional Institutional Repositories (IRs) that conduct a lot of research; however, a substantial disparity exists between the content in these IRs and academic staff's Google Scholar profiles. Additionally, the annual trend and uptake in the IRs are very low as compared to the publications indexed on Google Scholar profiles of the academic staff of these HEIs. The expectation is that when you deploy the IR, it will result in a gradual increase in uptake because more and more people are going to be using it. Therefore, this study aimed to identify effective approaches for improving the uptake of IR content in HEIs in Zambia. The study identified academic staff with Google Scholar profiles from the HEIs, and the Publish or Perish software was employed to extract author publications from these profiles. Simultaneously, the Open Archive Initiative Protocol for Metadata Harvester (OAI-PMH) was used to extract publications from IRs. In cases where OAI-PMH was not activated, Octoparse, a web data extraction solution, was utilised to extract the data from the IRs. The study further employed a descriptive survey research design to collect and analyse the data for objectives two and three by conducting interviews. The study found a significant disparity between the content available in IRs and on Google Scholar profiles across HEIs, with 90% of available publications on Google Scholar profiles missing on the IRs. The study also revealed that IR uptake rates are generally low, with fluctuating trends over the years. At UNZA, the average IR uptake rate is very low: 11%, ZCAS is 37%, UNILUS is 0.8%, CU is 6%, ICU is 20%, CHAU is 7%, MU is 4%, and LAMU is 28%, respectively. The research revealed that academic staff were not aware of the IRs and their responsibility to submit publications. However, upon becoming aware, they expressed their willingness to submit the missing publications to the librarians through emails, submitting their Google Scholar profile IDs. The findings suggest that providing academic staff members with training and support about the IRs and their benefits, implementing clear IR deposit policies, and automating the deposit process would be effective strategies for ensuring that missing content or publications are uploaded to the IR. The study recommends that IRs should focus on training and supporting academic staff members, implementing clear policies, automating the deposit process, and collaborating with departments and research units to streamline the self-archiving process and improve the uptake of IR content. The research also recommends that HEIs should establish clear guidelines, responsibilities, communication channels, and support mechanisms to foster collaboration, compliance, and contributions from academic staff, departments, librarians, and other stakeholders. The study concludes that by implementing these recommendations, HEIs can improve the uptake of IRs, increase the visibility and uptake of scholarly research output, and enhance the reputation and success of academic work in HEIs.
Timeline
00:00:00 Dissertation Overview
00:00:15 Presentation Outline
00:00:25 Background
00:01:14 Problem Statement
00:02:12 Research Objectives and Questions
00:02:42 Significance of the Study
00:02:58 Theoretical Framework
00:03:36 Literature Review
00:03:32 Methodology
00:06:37 Limitations
00:06:52 Results and Discussions
00:08:41 Conclusion
00:09:05 Recommendations
[1] datalab.unza.zm/people/matild...
[2] datalab.unza.zm
[3] www.unza.zm