DETUROPE - The Central European Journal of Regional Development and Tourism 2024, 16(2):22-35 | DOI: 10.32725/det.2024.018

Determinants of the Croatian Pre-pandemic Inbound Tourism Demand

Esmeralda Marića
a School of Economics and Business Sarajevo, Trg oslobođenja – Alija Izetbegović 1 71000 Sarajevo
Bosnia and Herzegovina

Tourism is a vital sector for the Croatian economy. During the pre-pandemic period, Croatia reported increasing numbers of tourist arrivals and experienced a significant contribution of tourism to GDP and earnings. This research aims to investigate the impact of economic and supply-side determinants on inbound tourism demand. The analysis was conducted on panel data with a five-year long-time dimension and forty-seven incoming countries included in the cross-sectional dimension. In order to investigate determinants of Croatian inbound tourism demand, this research relies on the Two-step System Generalized Methods of Moments (GMM). The results suggest that supply-side determinants and tourist arrivals from the previous year positively affect inbound tourism demand. However, none of the economic determinants proved to have a significant effect on the number of tourist arrivals. Consequently, our findings suggest that infrastructural enhancements and quality services that could lead to an increased number of repeated visits and recommendations are crucial for Croatian inbound tourism demand.

Keywords: inbound tourism, tourism demand, tourism management, services marketing

Received: December 6, 2023; Revised: September 4, 2024; Accepted: February 2, 2025; Prepublished online: February 10, 2025; Published: February 3, 2025  Show citation

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Marić, E. (2024). Determinants of the Croatian Pre-pandemic Inbound Tourism Demand. DETUROPE - The Central European Journal of Regional Development and Tourism16(2), 22-35. doi: 10.32725/det.2024.018
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References

  1. Albaladejo, I. P., González-Martínez, M. I., & Martínez-García, M. P. (2016). Nonconstant reputation effect in a dynamic tourism demand model for Spain. Tourism Management, 53, 132-139. https://doi.org/10.1016/j.tourman.2015.09.018 Go to original source...
  2. Apergis, N., Mervar, A., & Payne, J. E. (2017). Forecasting disaggregated tourist arrivals in Croatia: Evidence from seasonal univariate time series models. Tourism Economics, 23(1), 78-98. https://doi.org/10.5367/te.2015.0499 Go to original source...
  3. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The review of economic studies, 58(2), 277-297. Go to original source...
  4. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of econometrics, 68(1), 29-51. Go to original source...
  5. Baldigara, T., & Mamula, M. (2015). Modelling international tourism demand using seasonal ARIMA models. Tourism and hospitality management, 21(1), 19-31. Go to original source...
  6. Bhuiyan, M.A., Crovella, T., Paiano, A., & Alves, H. (2021). A review of research on tourism industry, economic crisis and mitigation process of the loss: Analysis on pre, during and post pandemic situation. Sustainability, 13(18), p.10314. Go to original source...
  7. Biagi, B., Brandano, M. G., & Detotto, C. (2012). The effect of tourism on crime in Italy: A dynamic panel approach. Economics, 6(1), 20120025. Go to original source...
  8. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of econometrics, 87(1), 115-143. Go to original source...
  9. Bond, S. R., Hoeffler, A., & Temple, J. R. (2001). GMM estimation of empirical growth models. Available at SSRN 290522.
  10. Brida, J. G., & Risso, W. A. (2009). A dynamic panel data study of the German demand for tourism in South Tyrol. Tourism and Hospitality Research, 9(4), 305-313. Go to original source...
  11. Chiu, Y. B., Zhang, W., & Ding, K. (2021). Does globalisation influence inbound tourism? Evidence from a dynamic panel threshold analysis. Journal of Travel Research, 60(5), 1074-1084. Go to original source...
  12. Choyakh, H. (2008). A model of tourism demand for Tunisia: inclusion of the tourism investment variable. Tourism Economics, 14(4), 819-838. https://doi.org/10.5367/000000008786440238 Go to original source...
  13. Croatian Bureau of Statistics. (2018). Statistical Yearbook of the Republic of Croatia 2018. Retrieved August 8, 2023, from https://www.dzs.hr/Hrv_Eng/publication/2022/SGRH_2022.pdf
  14. Croatian Ministry of Tourism. (2013). Croatian tourism development strategy. Retrieved August 8, 2023, from https://mint.gov.hr/UserDocsImages/arhiva/130426-Strategija-turizam-2020.pdf
  15. Deluna Jr, R., & Jeon, N. (2014). Determinants of international tourism demand for the Philippines: an augmented gravity model approach. MPRA Paper 55294, University Library of Munich, Germany.
  16. Dogru, T., Sirakaya-Turk, E., & Crouch, G. I. (2017). Remodeling international tourism demand: Old theory and new evidence. Tourism management, 60, 47-55. https://doi.org/10.1016/j.tourman.2016.11.010 Go to original source...
  17. Easterly, W., & Kraay, A. (2000). Small states, small problems? Income, growth, and volatility in small states. World development, 28(11), 2013-2027. Go to original source...
  18. Erjavec, N., & Devčić, K. (2022). Accommodation Capacity, Trade Openness and International Tourism Demand in Croatia: Evidence From a Dynamic Panel Model. Tourism: An International Interdisciplinary Journal, 70(1), 43-52. Go to original source...
  19. Esquivias, M.A., Sugiharti, L., Rohmawati, H., & Sethi, N. (2021). Impacts and implications of a pandemic on tourism demand in Indonesia. Economics & Sociology, 14(4), 133-150. Go to original source...
  20. Garín-Munoz, T. (2006). Inbound international tourism to Canary Islands: a dynamic panel data model. Tourism management, 27(2), 281-291. Go to original source...
  21. Garin-Munoz, T., & Montero-Martín, L. F. (2007). Tourism in the Balearic Islands: A dynamic model for international demand using panel data. Tourism management, 28(5), 1224-1235. Go to original source...
  22. Ghosh, S. (2022). Modelling inbound international tourism demand in Australia: Lessons from the pandemics. International Journal of Tourism Research, 24(1), 71-81. Go to original source...
  23. Habibi, F. (2017). The determinants of inbound tourism to Malaysia: A panel data analysis. Current Issues in Tourism, 20(9), 909-930. Go to original source...
  24. Habibi, F., & Abbasianejad, H. (2011). Dynamic panel data analysis of European tourism demand in Malaysia. Iranian Economic Review, 15(29), 27-41.
  25. Habibi, F., Rahim, K. A., Ramchandran, S., & Chin, L. (2009). Dynamic model for international tourism demand for Malaysia: Panel data evidence. International Research Journal of Finance and Economics, 33(1), 208-217.
  26. Holidu. (2023). The European cities most overloaded with tourists. Retrieved September 23, 2023, from https://www.holidu.co.uk/magazine/european-cities-overtourism-index
  27. Ibrahim, M. A. M. A. (2011). The determinants of international tourism demand for Egypt: Panel data evidence. European Journal of Economics, Finance and Administrative Sciences, 30, 50-58.
  28. Kim, S., & Song, H. (1998). Analysis of inbound tourism demand in South Korea: a cointegration and error correction approach. Tourism Analysis, 3(1), 25-41.
  29. Kumar, N., Kumar, R. R., Patel, A., Hussain Shahzad, S. J., & Stauvermann, P. J. (2020). Modelling inbound international tourism demand in small Pacific Island countries. Applied Economics, 52(10), 1031-1047. Go to original source...
  30. Leitão, N. C. (2015). Portuguese tourism demand: a dynamic panel data analysis. International journal of economics and financial issues, 5(3), 673-677.
  31. Lio, M. C., Liu, M. C., & Ou, Y. P. (2011). Can the internet reduce corruption? A cross-country study based on dynamic panel data models. Government Information Quarterly, 28(1), 47-53. Go to original source...
  32. Mervar, A., & Payne, J. E. (2007). An analysis of foreign tourism demand for Croatian destinations: long-run elasticity estimates. Radni materijali EIZ-a, (1), 5-21. Go to original source...
  33. Naudé, W. A., & Saayman, A. (2005). Determinants of tourist arrivals in Africa: a panel data regression analysis. Tourism economics, 11(3), 365-391. Go to original source...
  34. Permatasari, M. F., & Esquivias, M. A. (2020). Determinants of tourism demand in Indonesia: A panel data analysis. Tourism Analysis, 25(1), 77-89. Go to original source...
  35. Phakdisoth, L., & Kim, D. (2007). The determinants of inbound tourism in Laos. ASEAN economic bulletin, 225-237. Go to original source...
  36. Rey, B., Myro, R. L., & Galera, A. (2011). Effect of low-cost airlines on tourism in Spain. A dynamic panel data model. Journal of Air Transport Management, 17(3), 163-167. Go to original source...
  37. Roodman, D. (2009a). How to do xtabond2: An introduction to difference and system GMM in Stata. The stata journal, 9(1), 86-136. Go to original source...
  38. Roodman, D. (2009b). A note on the theme of too many instruments. Oxford Bulletin of Economics and statistics, 71(1), 135-158. Go to original source...
  39. Seetanah, B., Durbarry, R., & Ragodoo, J. N. (2010). Using the panel cointegration approach to analyse the determinants of tourism demand in South Africa. Tourism Economics, 16(3), 715-729. Go to original source...
  40. Seetaram, N. (2012). Estimating demand elasticities for Australia's international outbound tourism. Tourism Economics, 18(5), 999-1017. Go to original source...
  41. Sequeira, T. N., & Maçãs Nunes, P. (2008). Does tourism influence economic growth? A dynamic panel data approach. Applied economics, 40(18), 2431-2441. Go to original source...
  42. Simundic, B. (2022). Evidence on pre-pandemic outbound tourism demend determinants in OECD countries. Economic and Social Development: Book of Proceedings, 75-85.
  43. ©krinjarić, T. (2011). Investigation of foreign tourism demand in Croatia using panel data analysis. Acta turistica, 23(2), 145-173.
  44. ©kuflić, L., & ©toković, I. (2011). Demand function for croatian tourist product: A panel data approach. Modern economy, 2(1), 49-53. Go to original source...
  45. Song, H., & Witt, S.F., 2006. Forecasting international tourist flows to Macau. Tourism management, 27(2), 214-224. Go to original source...
  46. Statista. (2023). Retrieved September 8, 2023, from https://www.statista.com/
  47. Tang, C. F. (2018). The impacts of governance and institutions on inbound tourism demand: evidence from a dynamic panel data study. Asia Pacific Journal of Tourism Research, 23(10), 1000-1007. Go to original source...
  48. Tang, C. F., & Tan, E. C. (2015). The determinants of inbound tourism demand in Malaysia: another visit with non-stationary panel data approach. Anatolia, 27(2), 189-200. Go to original source...
  49. Tang, J., Yuan, X., Ramos, V., & Sriboonchitta, S. (2019). Does air pollution decrease inbound tourist arrivals? The case of Beijing. Asia Pacific Journal of Tourism Research, 24(6), 597-605. Go to original source...
  50. Tica, J., & Koľić, I. (2015). Forecasting Croatian inbound tourism demand. Economic research-Ekonomska istraľivanja, 28(1), 1046-1062. Go to original source...
  51. Uddin, M. A., Ali, M. H., & Masih, M. (2017). Political stability and growth: An application of dynamic GMM and quantile regression. Economic Modelling, 64, 610-625. Go to original source...
  52. Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of econometrics, 126(1), 25-51. Go to original source...
  53. Witt, S. F., & Witt, C. A. (1995). Forecasting tourism demand: A review of empirical research. International Journal of forecasting, 11(3), 447-475. Go to original source...
  54. World Bank. (2023). World Development Indicators (WDI). Retrieved August 8, 2023, from https://databank.worldbank.org/source/world-development-indicators

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