Adoption of artificial intelligence to improve metadata services at Institutions of Higher Education in Namibia

Abstract

The emergence of artificial intelligence and the potential it has, has caused changes in a number of industries by promising efficiency, precision, and creativity. Artificial intelligence has the potential to completely transform metadata services in library systems by providing more efficient methods for creation, extraction, and management. Metadata specialists have a critical opportunity to improve their processes and make library collections easier to access and manage by embracing artificial intelligence. The cooperation of artificial intelligence and metadata specialists is becoming more crucial as technology advances, leading libraries into a new phase of unparalleled information retrieval and efficiency. The purpose of this study is to investigate the adoption of artificial intelligence to improve metadata services in libraries at Institutions of Higher Education in Namibia. This study adopted the Technology Acceptance Model, Metadata Theory and Digital Transformation Model as the theories grounding this study. The researcher adopted pluralistic ontological and pragmatic epistemological worldviews as research paradigms in this study. The study deployed a mixed methods research approach as well as a convergent research design and opted for parallel sampling as the type of sampling method. The population of this study was made up of metadata specialists from the University of Namibia, Namibia University of Science and Technology, and International University of Management libraries, which are the largest Institutions of Higher Education in Namibia with metadata services. Data were collected through interviews, questionnaires and observation by the researcher. The findings were integrated to ensure the study's objectives were met. The findings of the study indicated that the Institutions of Higher Education in Namibia used manual and electronic means to manage metadata services, which produced inconsistencies, duplications, and errors. This study recommends a framework to adopt artificial intelligence to improve metadata services at Institutions of Higher Education in Namibia. The recommended artificial intelligence framework contributes significantly to the knowledge of metadata services in libraries. Furthermore, the study discusses the practical implications this study could have on IHEs in Namibia, such as library policies adopting AI for metadata services and theoretical implications, such as AI in metadata services being integrated in the education curriculum.