This study uses an innovative tourism product development approach, based on co-creation or customer involvement, related to Lake Balaton, a mass tourism-based destination in Hungary, from the point of view of the market segment of active cycling tourists. The investigation of opportunities for the development of cycling tourism first of all relies on the new approach of attraction and product development around the destination, in which it is important to take into consideration the consumer preferences of the most important related group of tourists—active cycling tourists. The sustainable approach of tourism product development also provides an opportunity to decrease the spatial and temporal concentration of tourism, which is largely concentrated on the summertime season. The aim of this study is to explore aspects of the customers’ demand for tourism development in terms of cycling tourism with the help of primary data collection, in order to provide adequate directions for sustainable tourism development in the destination. Revealing the demand side of active cycling tourism related to Lake Balaton, the authors used both qualitative (focus group discussions and structured interviews) and quantitative questionnaire survey (computer-assisted data collection) research methods. The latter online surveys were carried out in November and December, 2019, and resulted with an appraisable sample of 809 questionnaires. As for the method, descriptive statistics and relationship analyses were applied. More than five thousand (5050) possible relationships were examined between the closed answers of the questionnaire by Kendall’s rank correlation coefficient (τ) and Cramer’s V, depending on whether they could be measured on a nominal or ordinal scale. The results show that the content analysis of the primary research provides well determined directions for the sustainable tourism development of cycling tourism at Lake Balaton, so customer involvement seems to be a win-win situation both for the customers (tourists) and the decision makers.

There are several well-known rankings of universities and higher education systems. Numerous recent studies question whether it is possible to compare universities and countries of different constitutions. These criticisms stand on solid ground. It is impossible to create a onedimensional ordering that faithfully compares complex systems such as universities or even higher education systems. We would like to convince the reader that using well-chosen elements of a family of state-of-the-art data mining methods, namely, bi-clustering methods, can provide an informative picture of the relative positions of universities/higher education systems. Bi-clustering methods produce leagues of comparable entities alongside the indicators, which produce a similar grouping of them. Within leagues, partial rankings could be specified and furthermore can serve as a proper basis for benchmarking.Tertiary Education and Management (2019) 25:289-310 comparison of universities or countries with very different financial backgrounds, scope and social environments. There is an ongoing effort (Downing 2013;Salmi 2013) to define different and well-tailored leagues for benchmarking universities or countries instead of ranking them in a single group. However, there is no generally accepted method for identifying such leagues.The authors agree with Benneworth (2010) and Liu (2013) that universities that belong to similar higher education systems should be compared according to a given set of criteria that is also in accordance with the common features of the higher education systems. The present work hinges on a fundamental principle: leagues (of countries) constitute both the set of countries and the set of indicators. Specifically, the set of criteria might differ from league to league; however, some of the criteria may be common. This may also apply to universities. The primary challenge in specifying leagues is to simultaneously define a set of criteria and countries/universities that are similar according to the criteria.The purpose of this study is to answer the following research question: How can leagues of comparable higher education systems be defined? The study does not aim at expanding the criticisms of the indicators of rankings, nor replacing them with other (better) indicators, nor developing new indicators.In the following, for the case of higher education systems, we show how leagues, as a new basis for comparing higher education systems, can be developed. We use the available indicators as they are, acknowledging that some of them may impose some type of bias, while they are the result of enormous efforts of data acquisition and cleansing, which we surely cannot reproduce. In addition, the usage of well-known indicators follows the ceteris paribus principle; we introduce a new method forming groups of objects to compare, but we do not introduce new indicators at the same time. As a result, the gain of forming leagues can be demonstrated without the effect of new indicators.In this paper, we focus on creating leagues of ...

Many studies deal with the determinants of countries' culture or efficiency of microfinance institutions (MFIs). The purpose of this chapter is to fill the gap in the literature, namely, to analyze the connection between national cultural features and indicators of countries' microfinance institutions. The authors summarize the mission and operation of microfinance institutions, the six dimensions of national culture and three knowledge strategies. 35 countries representing the subject of the research, the statistics of which are available for both MFIs' side and national cultural side. The rank correlation and the investigation of TOP 5 countries show that the MFIs are successful in countries characterized by high level of power distance, collectivistic culture, high level of uncertainty avoidance and restraint culture. The chapter also lists few countries where MFIs could be successfully introduced based on their national cultural features and recommends certain knowledge strategies for effective operation.

University rankings can both orient and disorient potential students. In rankings, universities with very different characteristics are compared, which makes interpretation difficult. We propose the application of a clustering method, which creates groups of universities that are close to each other with respect to a subset of indicators, but the indicators also show homogeneity with respect to the universities in that group. We call such groups leagues. These leagues are defined by the data themselves and are not based on subjective criteria. We demonstrate our proposition using one member of the family of the two-way clustering method, namely, biclustering. The case we present is based on the Round University Ranking (RUR) 2020 dataset. The use of leagues could provide better guidance not only for potential applicants but also for university funding organizations and policy-makers. Our case study led to a somewhat surprising observation. In the top league (based on the RUR data and indicators), the three most important indicators measure reputation, not scientific or educational performance.

A felsőoktatásban lévő hallgató mobilitását befolyásoló tényezőket számos kérdőíves kutatás vizsgálta. A jelen tanulmány egyik újdonságát az adja, hogy az összes, felsőoktatásba jelentkező és ott végzett hallgató jelentkezési és elhelyezkedési adatait tartalmazó adatbázisokra épít. Azt vizsgáljuk, hogy milyen gazdasági tényezők, illetve maguk a felsőoktatási intézmények hogyan és mennyire befolyásolják a hallgatók országon belüli vándorlását. Feltevésünk szerint igen erősen. Ennek alátámasztására kvantitatív megközelítést alkalmazunk. Gravitációs modellek segítségével, a hálózatelméletet is segítségül hívva igazoljuk, hogy a felsőoktatás döntő szerepet játszik a fiatalok országon belüli mobilitásában. Megerősítjük azt a feltevést, hogy a viszonylag alacsony földrajzi mobilitású magyar társadalomban a felsőoktatás a földrajzi mobilitás fontos katalizátora.