Multiple Correspondence (MCA) and Thematic Analyses of Quality Assurance in Higher Education Literature Landscape: Consequences for Future Research Opportunities and Practice

This research utilized sophisticated quantitative bibliometric analytical methods, encompassing thematic, network, and factorial analyses, to delineate the literature landscape concerning quality assurance in higher education (QAHE). The study was initiated due to reports indicating that developing regions encounter ongoing difficulties in providing quality education. Simultaneously, current research presents various suggestions on how higher education institutions can foster sustainable educational quality. Moreover, prior studies have not employed meta-analysis techniques to investigate the influence of the recent COVID-19 pandemic on the research domain concerning quality assurance and the future of education. This paper fills this gap in the literature by presenting strong and thought-provoking evidence from a review of the SCOPUS database's existing literature from 1992 to 2025. Time-variance thematic and multiple correspondence analyses (MCA) provided additional validation of the results from the primary sample analyses. The keyword analysis showed that terms that do not come up as often point to new ideas and possible areas for more research. These include policy and governance, technology and systems, evaluation and results, and regional and cultural contexts. The cluster analysis showed that even though more people are interested in artificial intelligence (AI), not much research has been done on how AI affects teaching, learning, and the quality of education. Future research ought to investigate the impact of technological advancements on quality within transnational contexts.