Artículo

University rankings disclosure and efficiency in higher education: A bibliometric and systematic analysis

Resumen

Whereas new public management models urge universities to manage available resources more efficiently and effectively, the pressure exerted by the phenomenon of university rankings – referents of the quality and excellence of higher education institutions-has driven universities to strive for better positions in these rankings with the aim of maximizing their reputation. While the effects of the disclosure of university rankings on the one hand and the measurement of efficiency in universities on the other have been widely analysed, little is known about the variables involved in university rankings and their relationship with the efficiency levels of higher education institutions. Therefore, this study aims to identify evidence and/or disagreements within the relevant scientific literature regarding universities’ efficiency levels and their position in the rankings as a causal relationship between efficiency, reputation and market perception. Given this approach, the present study has the objective of exploring what the scientific literature offers regarding these two areas, with the aim of increasing our knowledge on the topic and presenting research opportunities. To this end, a structured intervention process known as the Knowledge Development Process – Constructivist (Proknow-c) is used. As a result, an BP Bibliographic Portfolio is obtained from the most relevant scientific publications, composed of 77 items covering the period of 1995-2016. This portfolio is achieved through a bibliometric and systematic analysis that presents gaps and research opportunities relevant to the proposed subject of interest.
Hidayat, Erwin Yudi (57205439013); Hastuti, Khafiizh (56485990500); Muda, Azah Kamilah (23390362900)
Artificial intelligence in digital image processing: A bibliometric analysis
2025
10.1016/j.iswa.2024.200466
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212984349&doi=10.1016%2fj.iswa.2024.200466&partnerID=40&md5=14c2a82f6cb46e9efa80c050fc79c24f
Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Dinus Research Group for AI in Medical Science (DREAMS), Universitas Dian Nuswantoro, Semarang, 50131, Indonesia; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, 76100, Malaysia
All Open Access; Gold Open Access
Scopus
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