Kapadokya Gezgin Profili Analizi : Kriz Sonrasi Değişim Ve Dinamikleri

Abstract

Kapadokya, dünyada mevcut 1031 miras alanı içerisinde, bünyesinde hem doğal hem de kültürel özellikler barındıran 32 ender bölge arasında (%3) yer almaktadır. Doğal, tarihi ve kültürel alanda yüksek ürün çeşitliliği ile ön plana çıkan önemli bir destinasyondur. Diğer yandan gezginlerin seyahat tercihleri sadece destinasyon özelliklerine değil, o ülkedeki makro kriterlere (güvenlik, ulaşılabilirlik gibi) bağlı olarak da değişkenlik göstermektedir. Pazarlama karması kapsamında pazarı bölümlendirebilmek, hedef pazarlara farklı hizmetler sunabilmek adına son derece önemlidir. Pazarda meydana gelen değişimleri ölçümlemek, bir yandan neden sonuç ilişkisi kurulabilmesini sağlarken, aynı zamanda geleceğe ilişkin alınması gereken tedbirleri/gelişim noktalarını da tayin eder. Bu çerçevede, bu çalışmanın temel amacı, Nevşehir İli (Kapadokya) sınırları içerisinde 2011-2015 yılları arasında konaklayan yabancı turistlerin, ülke, konaklama gün sayısı ve dönemi bazında benzerliklerine göre sınıflandırılması; bu ülkelerin/grupların Kapadokya ziyaretini etkileyen faktörlerin ve etki düzeylerinin belirlenmesidir. Bu ülkelerin kendi aralarındaki ilişkilerinin, stokastik eğilimi de dikkate alarak ortaya konması, çalışmanın ikincil amacını oluşturmaktadır. Bu kapsamda kümeleme analizi ile gözlemlerin benzerlikleri temel alınarak objektif bir sınıflandırma yapılmıştır. Gezginlerin Kapadokya ziyaretine etki eden faktörler ise panel regresyon analizi ile analiz edilmiştir. Uzun vadede birlikte hareket eden ülke gruplarını belirlemek için ise eşbütünleşme analizi kullanılmıştır. Analiz sonuçlarına göre, incelenen dönemde Fransa, Almanya ve Türkiye'nin incelenen kriterler bazında farklı bir tutum sergiledikleri; Kanada, Fransa, HongKong ve Japonya'nın ise uzun dönemde birlikte hareket ettikleri görülmüştür. Avrupa Birliği ülkeleri ise Kapadokya seyahatlerinde orta kuvvette benzer bir davranış sergilemişlerdir. Sabit etkiler panel regresyon sonuçları ise, ekonomik büyümenin, farklı ülkelerden Kapadokya'ya gelen turist sayıları üzerinde anlamlı bir etkisi olduğunu göstermektedir.

Keywords

• Kapadokya, gezgin profili değişimi, kümeleme analizi, eşbütünleşme analizi, panel regresyon

CAPPADOCIA VISITOR PROFILE ANALYSIS : POST-CRISIS CHANGE AND ITS DYNAMICS

Abstract

Cappadocia which is both UNESCO cultural and natural heritage area is in 32 (3%) very rare area in 1031 UNESCO heritage sites. It is an important destination with high potential product diversity in the field of natural, historical and cultural circumstances. On the other hand, destinations' performances do not just depend on the feature of destinations, but also the macro-criteria (such as security or transportation capabilities) of related countries. To be able to make segmentation in terms of marketing mix theory, analyzing the visitor profile has crucial role. Measuring changes on market provides decision makers to make root cause analysis and put forward which countermeasures should be taken/developed. In this perspective, aim of this study is to classify the visitors stayed in Cappadocia country, term and stayed nights basis and to determine the factors (and effect levels) affecting their travel choices in a macro environmental perspective between 2011 and 2015. The secondary aim of the study is to figure out the long-term relationship among countries' travel behaviors to Cappadocia considering stochastic trend. For these aims, cluster analysis is done for objective classification. The factors (and affects level) that affect visitors' travel are figured out via setting panel regression model. In addition, cointegration analysis is used to figure out the long-term relationship among visitors. Results show that while Germany, France and Turkey had unique visiting time pattern which means that they all have a specific visiting behavior, European Union countries had medium sized similar strength on Cappadocia travel. And Canada, Hong Kong, France and Japan had long term similar visiting pattern. Finally, fixed affect panel regression analysis results present that GDP is the only significant variable that affects visitors' visiting behavior for Cappadocia.

Keywords

• Cappadocia, destination profile change, cluster analysis, cointegration analysis, panel regression

References

1. Athanasopoulos, George, R.J. Hyndman (2008). "Modelling and forecasting Australian domestic tourism", Tourism Management, 29(1): 19-31
2. Baltagi, Badi H. (2008). Econometric Analysis of Panel Data, Wiley, USA.
3. Bernardina, Algieri, (2006). "An econometric estimation of the demand for tourism: the case of Russia", Tourism economics, 12(1): 5-20
4. Berry, D.William, and S. Feldman, S. (1985). "Multiple Regression in Practice". Sage
5. University Paper Series on Quantitative Applications in the Social Sciences, 07-050. Newbury Park, CA, Sage. Bigano, Andrea., J.M. Hamilton, R.S.J. Tol (2006). "The impact of climate change on domestic and international tourism: A simulation study". The integrated assessment journal , (1): 25-49
6. Brida, Juan Gabriel, L. Osti, A. Barquet (2010): "Segmenting resident perceptions towards tourism—a cluster analysis with a multinomial logit model of a mountain community",
7. International Journal of Tourism Research, 12(5): 591–602 Brida, Juan Gabriel, E. Riaño, A.S. Zapata (2012). "Residents' perceptions toward cruise tourism impacts on a community: a factor and cluster analysis", Cuadernos de Turismo, 29: 107
8. Chen, C. Sandy (2011). "Residents’ Perceptions of the Impact of Major Annual Tourism

9. Events in Macao: Cluster Analysis", Journal of Convention & Event Tourism, 12(2): 126-128
10. Dristakis, Nikolaos (2004). "Cointegration analysis of German and British tourism demand for Greece". Tourism Management, 25(1): 111-119
11. Garin-Munoz, T. (2009). "Tourism in Galicia: domestic and foreign demand", Tourism Economics, 15(4): 753-769
12. Greene, William (2012). "Econometric Analysis" (7th ed.), Pearson: 234–237
13. Heung, Vincent C.S., H. Qu, R. Chu (2001). "The relationship between vacation factors and socio-demographic and travelling characteristics: the case of Japanese leisure travelers",
14. Tourism management, 22: 259-269
15. Hudson, Simon, B. Ritchie (2002). "Understanding the domestic market using cluster analysis: A case study of the marketing efforts of Travel Alberta", Journal of Vacation Marketing, 8(3): 263-276
16. Küçük Oteller Derneği Raporu, (2015), "2015 yılı ilk 6 ay değerlendirmesi anket sonuçları",
17. Küçük Oteller Derneği, İstanbul. Leung, Xi Yu, Baloglu, Ş. (2013). "Tourism competitiveness of Asia Pacific destinations".
18. Tourism analysis: an interdisciplinary journal, 18(4): 371-384
19. Laurentina, Maria da Cruz Vareiro, P.C.Riberio (2013): "Residents' perceptions of tourism impacts in GuimarAes (Portugal): a cluster analysis", Current Issues in Tourism, 16(3): 535
20. Leung, Xi Yu, S.Baloglu (2013). "Tourism Competitiveness of Asia Pacific Destinations",
21. Tourism Analysis, 18(4): 371-384. Middleton, Victor (1994). "Marketing in travel and tourism (2nd ed.)", Oxford: Butterworth- Heinemann
22. Reisinger, Yvette (2005). "Travel Anxiety and Intentions to Travel Internationally:
23. Implications of Travel Risk Perception", Journal of Travel Research, 43(3): 212-225
24. Seddighi, H.R., D.F. Shearing (1997). "The demand for tourism in North East England with special reference to Northumbria: an empirical analysis", Tourism Management, 18: 499–511
25. Tabachnick, Barbara. G., and L.S. Fidell. (1996). "Using multivariate statistics (6th ed."),
26. HarperCollins, New York. Taylor, Tim, R. A. Ortiz, (2009). "Impacts of climate change on domestic tourism in the UK: a panel data estimation", Tourism Economics, 15(4): 803-812
27. The World Tourism Organization (UNWTO) Report (2015, 2014, 2013, 2012, 2011 editions), http://unwto.org/annualreports
28. Van Raaij, W. F. (1986). "Consumer research on tourism: Mental and behavioral constructs",
29. Annals of Tourism Research, 13: 1-9. World Bank, Databank, viewed 15 February 2016, http://databank.worldbank.org/data/reports.aspx?source=2&country=AUS&series=&period=#
30. Trading Economics, viewed 10 February 2016, http://www.tradingeconomics.com/china/indicators
31. CNN, viewed in 18 December 2015, http://edition.cnn.com/2015/11/18/world/paris-soccer- fans-turkey/
32. SPSS Tools, viewed 23 February 2016, http://spsstools.net/en/syntax/442/
33. Vision of Humanity Index, viewed in 8 March 2016, http://www.visionofhumanity.org/; unempoloyment and GDP growth rates are observed via worldbank data and tradingeconomics.com.
34. Human Development Reports, viewed 23 February 2016, http://hdr.undp.org/;
35. Turkish Statistical Institute, viewed 22-28 February 2016, www.tuik.gov.tr
36. Dogan News Agency, viewed 10 January 2016, http://www.dha.com.tr/kapadokyada- memnuniyet-barcelona-ve-st-petersburgdan-yuksek_1011757.html
37. Interviews with visitors from Australia, Japan and USA in 2015 and 2016.