Kapadokya Gezgin Profili Analizi : Kriz Sonrasi Değişim Ve Dinamikleri

Year 2018, Volume: 7 Issue: 1, 51 - 74, 01.01.2018

Tekiner Kaya Alper Aykut Ekinci

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

• Athanasopoulos, George, R.J. Hyndman (2008). "Modelling and forecasting Australian domestic tourism", Tourism Management, 29(1): 19-31
• Baltagi, Badi H. (2008). Econometric Analysis of Panel Data, Wiley, USA.
• Bernardina, Algieri, (2006). "An econometric estimation of the demand for tourism: the case of Russia", Tourism economics, 12(1): 5-20
• Berry, D.William, and S. Feldman, S. (1985). "Multiple Regression in Practice". Sage
• 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
• 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",
• 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
• Chen, C. Sandy (2011). "Residents’ Perceptions of the Impact of Major Annual Tourism
• Events in Macao: Cluster Analysis", Journal of Convention & Event Tourism, 12(2): 126-128
• Dristakis, Nikolaos (2004). "Cointegration analysis of German and British tourism demand for Greece". Tourism Management, 25(1): 111-119
• Garin-Munoz, T. (2009). "Tourism in Galicia: domestic and foreign demand", Tourism Economics, 15(4): 753-769
• Greene, William (2012). "Econometric Analysis" (7th ed.), Pearson: 234–237
• 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",
• Tourism management, 22: 259-269
• 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
• Küçük Oteller Derneği Raporu, (2015), "2015 yılı ilk 6 ay değerlendirmesi anket sonuçları",
• Küçük Oteller Derneği, İstanbul. Leung, Xi Yu, Baloglu, Ş. (2013). "Tourism competitiveness of Asia Pacific destinations".
• Tourism analysis: an interdisciplinary journal, 18(4): 371-384
• 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
• Leung, Xi Yu, S.Baloglu (2013). "Tourism Competitiveness of Asia Pacific Destinations",
• Tourism Analysis, 18(4): 371-384. Middleton, Victor (1994). "Marketing in travel and tourism (2nd ed.)", Oxford: Butterworth- Heinemann
• Reisinger, Yvette (2005). "Travel Anxiety and Intentions to Travel Internationally:
• Implications of Travel Risk Perception", Journal of Travel Research, 43(3): 212-225
• 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
• Tabachnick, Barbara. G., and L.S. Fidell. (1996). "Using multivariate statistics (6th ed."),
• 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
• The World Tourism Organization (UNWTO) Report (2015, 2014, 2013, 2012, 2011 editions), http://unwto.org/annualreports
• Van Raaij, W. F. (1986). "Consumer research on tourism: Mental and behavioral constructs",
• 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=#
• Trading Economics, viewed 10 February 2016, http://www.tradingeconomics.com/china/indicators
• CNN, viewed in 18 December 2015, http://edition.cnn.com/2015/11/18/world/paris-soccer- fans-turkey/
• SPSS Tools, viewed 23 February 2016, http://spsstools.net/en/syntax/442/
• 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.
• Human Development Reports, viewed 23 February 2016, http://hdr.undp.org/;
• Turkish Statistical Institute, viewed 22-28 February 2016, www.tuik.gov.tr
• Dogan News Agency, viewed 10 January 2016, http://www.dha.com.tr/kapadokyada- memnuniyet-barcelona-ve-st-petersburgdan-yuksek_1011757.html
• Interviews with visitors from Australia, Japan and USA in 2015 and 2016.