The 5 reference contexts in paper Z. Bujdosó, J. Pénzes (2017) “TOURISM COMPETITIVENESS AND TOURISM DEVELOPMENT IN THE BORDER REGIONS OF HUNGARY” / spz:neicon:stavapk:354

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    2486
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    In general, these LAU-1 (former NUTS-4) microregions are backward areas in the light of the most important statistical indicators, because they are characterised by low population density and low level of enterprising spirit, signifi cant out-migration and unfavourable income situation
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    (Kozma 1995, Bujdosó et al. 2011).
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    Almost one-third of the territory and 21.9 percent of the population of Hungary could be regardFigure 1 – Border microregions in Hungary (Source: edited by Bujdosó et al. 2011) Lots of ideas came to light in order to resolve the successful.
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    statistical indicators, because they are characterised by low population density and low level of enterprising spirit, signifi cant out-migration and unfavourable income situation (Kozma 1995, Bujdosó et al. 2011). Almost one-third of the territory and 21.9 percent of the population of Hungary could be regardFigure 1 – Border microregions in Hungary (Source: edited by
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    Bujdosó et al. 2011)
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    Lots of ideas came to light in order to resolve the successful. At the same time, tourism and tourism development were regarded as a possibility to break out in every concept (Kozma, G. – Asworth G, 1993, Süli-Zakar, I. et al. 1999, Dávid, L.
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    In the last few years, remarkable studies appeared about the measuring possibilities of the concept of regional competitiveness in Hungary as well and we tried to utilize the results of these (Kozma,G 2002,
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    Pénzes, et al., 2008).
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    These studies represented the quantitative decomposition of the relative personal incomes into the adaptable and clear socialeconomic factors (Lengyel, I. 2000; Nemes Nagy, J. 2004). The method of decomposition was carried out by the study of József Nemes Nagy in order to investigate the competitiveness and its components of the border microregions.
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    Complex touristic advantage could not be found in any of the border microregions, multi-factored advantage appeared in four cases and single-factored advantage was detected in two cases. Most of the microregions (43 microregions) were disadvantageous in this respect, complex disadvantage could be found in 29 microregions and multi-factored disadvantage was observed in 14 ones
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    (Bujdosó et al., 2011).
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    Dynamic analysis was carried out in order to investigate the changes between 2000 and 2008. (This defi nition was applied by József Nemes Nagy in his study – Nemes Nagy, J. 2004) However, this kind of analysis cannot be regarded as dynamic in its terms, as only the data for the fi rst and the last years are compared to each other instead of the investigation of the whole period.
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    Five microregions out of the 22 units with disadvantage were characterised by single-factored, 12 by multi-factored and fi ve by complex disadvantage in the touristic competitiveness
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    (Bujdosó et al., 2011).
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    The competitiveness of the Hungarian microregions and the spatial distribution of the touristic supports1 1 This paper was supported by the János Bólyai Research Scholarship of the Hungarian Academy of Sciences. tion fee, the number of tourism nights and the capacFigure 2 – Types of tourism competitiveness in the border microregions of Hungary, 2008.
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