Authors:Zsolt Tibor Kosztyán, Vivien Valéria Csányi, and András Telcs
We present an application preference, list-based framework to Hungarian universities, which allows different type of flexible aggregation, and hence, analysis and clustering of application data. A novel mathematical method is developed by which preference lists can be converted into scored rankings. The proposed approach is demonstrated in the case of Hungary covering the period of 2006–2015. Our method reveals that the efforts to leverage the geographical center–periphery differences did not fulfil the expectations of policy makers. Also, it turns out that a student's top preference is very difficult to influence, while recruiters may build their strategy on the information of the first but one choice.
The hegemony of the Western higher education institutions in the global university market is being challenged by China. The top Chinese universities have significantly improved their international ranking positions. When it comes, however, to the ability of universities to attract foreign students and faculty, the Chinese higher education institutions' performance raises questions. The International Outlook scores of these universities, although showing an increasing trend, are still lacking behind the U.S. or Western European top universities. China is primarily a student ‘exporter.’ It also became a leading destination country for students from Asia or Africa, but it is still far from reaching the ‘international openness’ level of the U.S. or the UK universities. The publication networks of the top Chinese higher education institutions indicate that these universities prefer to publish with other Chinese institutions or the U.S. universities.
The aim of this case study is to analyze the historical and current state of the education and practice of futures studies (FS) in a country that was once a member of the Soviet Union: Estonia. There are other countries in Eastern Europe which used to be or currently are in a similar situation to Estonia, but futures studies developed in different ways, because politics and economies were driven by different strategies or interests, and futurists emphasized different aspects of their research (either the theoretical or the practical, along different paradigms). In certain countries, like in Estonia, FS fi rst achieved scientifi c (and educational) success after the political change of the early 90s, but this was followed by a long way down to a secondary (backing) position. It seems that an optimal share between education and practice may lead FS out from the pit in Estonia, and in other countries, too.
Throughout the reform process of the European university system, the importance of collaboration between actors at the academy and other areas of the economy and society are ever increasing, as evidenced by a growing number of co-authored articles and the number of citations to such works.This article analyses the characteristics of publications co-authored by Hungarian university researchers with non-academic partners. Scientometric indicators are used as primary methodological tools. Our sample was the publication output of 12 universities, which covers 90% of the university sphere’s publications, between 2001 and 2005 and was taken from the publications of Hungarian institutions of higher education appearing in the Web of Science database. The authors employed a new, important aspect in the cooperation activity of Hungarian universities: their connection with the non-academic partners. The selection and the institutional location of the co-authors resulted in an important database for further analysis. Based on the empirical analysis of the publication and citation performance data of 12 such universities the authors concluded that the proportion of citations to publications co-authored with either academic or non-academic partners is significantly higher for international partners than it is for Hungarian ones. For one publication, the proportion of citations to articles co-authored with foreign non-academic partners, such as firms or health care institutions, was five times higher than the number relating to papers co-authored with Hungarian firms or health care institutions. Higher citedness of the joint articles with the foreign country institutes than domestic partners are in harmony with observation in other countries. Generally the rate of the co-authored articles with non-academic partners is rather low. However it scatters to a great extent concerning the different universities. The presence or absence of medicine in the profile of the universities seems an important factor of that difference.
This paper offers some ammunition to better understand Hungary’s position in the IMD World Talent Report 2015 (IMD WTR 2015). First, it gives a brief overview of the methodology of the IMD WTR by highlighting its main features. Second, it presents the 2015 ranking and puts the focus on Hungary’s withering talent competitiveness. The paper conveys the message that an overarching and consistent reform package is a must in the education system to foster talent utilisation. However, such a package is likely to be insufficient unless economic policy addresses the relevant shortcomings of the Hungarian innovation ecosystem.
The forces of globalization are creating fundamental upheaval in the market for management education. As a consequence, international relevance has generally become a prerequisite for a business school’s ability to maintain a leading market position domestically. This article examines how business schools of the CEE region are coping with the pressures to widen the geographical scope of their activities in the face of tight resource constraints and still unresolved governance issues. While most business schools around the globe are struggling with similar issues, they manifest themselves in unique ways within the CEE region. Their resolution may ultimately enable regional business schools to lead the intellectual response to the fall-out of the still ongoing financial crisis.
( R ) Drop ( D ) α 14.08 (0.147) 0.31 (0.742) GDPpc i –1.32 (0.202) –0.00 (0.994) UR i –0.03 (0.238) –0.00 (0.619) M i –4.94* (0.056) –0.21 (0.392) C i –23.06** (0.010) 1.34* (0.098) S i 7.06* (0.055) –0.40* (0.093) HC i –3.60 (0.302) –0.78*** (0