IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p1964-d745338.html
   My bibliography  Save this article

A Projection Approach of Tourist Circulation under Conditions of Uncertainty

Author

Listed:
  • Anca-Gabriela Turtureanu

    (Faculty of Economic Sciences, Danubius University of Galati, 800654 Galati, Romania)

  • Rodica Pripoaie

    (Faculty of Law and Administrative Sciences, “Dunărea de Jos” University of Galati, 800008 Galati, Romania)

  • Carmen-Mihaela Cretu

    (Faculty of Economic Sciences, Danubius University of Galati, 800654 Galati, Romania)

  • Carmen-Gabriela Sirbu

    (Faculty of Economic Sciences, Danubius University of Galati, 800654 Galati, Romania)

  • Emanuel Ştefan Marinescu

    (Faculty of Communication and International Relations, Danubius University of Galati, 800654 Galati, Romania)

  • Laurentiu-Gabriel Talaghir

    (Faculty of Physical Education and Sport, Dunarea de Jos University of Galati, 800008 Galati, Romania
    Institute of Sport, Tourism and Service, South Ural State University, 454080 Chelyabinsk, Russia)

  • Florentina Chițu

    (The Economics and International Business Doctoral School, Bucharest University of Economic Studies, 010374 Bucharest, Romania)

Abstract

This paper explores an important problem in tourism demand analysis, namely, the inherent uncertainty involved in projecting tourism demand. Tourism demand continues to be severely affected by unforeseen events associated with the current global health crisis, which has led to an examination of ways to predict the devastating effects of the COVID-19 pandemic on tourism. Tourism flow forecasting relating to arrivals is of particular importance for tourism and the entire hospitality industry, because it is an indicator of future demand. Thus, it provides fundamental information that can be applied in the planning and development of future strategies. Accurate forecasts of seasonal tourist flows can help decision-makers increase the efficiency of their strategic planning and reduce the risk of decision-making failure. Due to the growing interest in more advanced forecasting methods, we applied the ARMA model method to analyze the evolution of monthly arrival series for Romania in the period from January 2010 to September 2021, in order to ascertain the best statistical forecasting model for arrivals. We conducted this research to find the best method of forecasting tourist demand, and we compared two forecasting models: AR(1)MA(1) and AR(1)MA(2). Our study results show that the superior model for the prediction of tourist demand is AR(1)MA(1).

Suggested Citation

  • Anca-Gabriela Turtureanu & Rodica Pripoaie & Carmen-Mihaela Cretu & Carmen-Gabriela Sirbu & Emanuel Ştefan Marinescu & Laurentiu-Gabriel Talaghir & Florentina Chițu, 2022. "A Projection Approach of Tourist Circulation under Conditions of Uncertainty," Sustainability, MDPI, vol. 14(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:1964-:d:745338
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/1964/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/1964/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gianni Barcaccia & Vincenzo D’Agostino & Alessandro Zotti & Bruno Cozzi, 2020. "Impact of the SARS-CoV-2 on the Italian Agri-Food Sector: An Analysis of the Quarter of Pandemic Lockdown and Clues for a Socio-Economic and Territorial Restart," Sustainability, MDPI, vol. 12(14), pages 1-28, July.
    2. Cuccia, Tiziana & Rizzo, Ilde, 2011. "Tourism seasonality in cultural destinations: Empirical evidence from Sicily," Tourism Management, Elsevier, vol. 32(3), pages 589-595.
    3. Diana Foris & Adriana Florescu & Tiberiu Foris & Sorin Barabas, 2020. "Improving the Management of Tourist Destinations: A New Approach to Strategic Management at the DMO Level by Integrating Lean Techniques," Sustainability, MDPI, vol. 12(23), pages 1-22, December.
    4. Giovanni De Luca & Monica Rosciano, 2020. "Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    5. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    6. Alina Badulescu & Daniel Badulescu & Ramona Simu?, 2018. "The Complex Relationship between International Tourism Demand and Economic Growth: An Analysis on Central and Eastern European Economies," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 20(S12), pages 935-935, November.
    7. Andrea Saayman & Ilsé Botha, 2017. "Non-linear models for tourism demand forecasting," Tourism Economics, , vol. 23(3), pages 594-613, May.
    8. Dillon Alleyne, 2006. "Can Seasonal Unit Root Testing Improve the Forecasting Accuracy of Tourist Arrivals?," Tourism Economics, , vol. 12(1), pages 45-64, March.
    9. Yi-Chung Hu, 2021. "Developing grey prediction with Fourier series using genetic algorithms for tourism demand forecasting," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(1), pages 315-331, February.
    10. Vogelsang, Timothy J. & Wagner, Martin, 2013. "A FIXED-b PERSPECTIVE ON THE PHILLIPS–PERRON UNIT ROOT TESTS," Econometric Theory, Cambridge University Press, vol. 29(3), pages 609-628, June.
    11. Makridakis, Spyros & Hogarth, Robin M. & Gaba, Anil, 2009. "Forecasting and uncertainty in the economic and business world," International Journal of Forecasting, Elsevier, vol. 25(4), pages 794-812, October.
    12. Sharma, Piyush & Leung, T.Y. & Kingshott, Russel P.J. & Davcik, Nebojsa S. & Cardinali, Silvio, 2020. "Managing uncertainty during a global pandemic: An international business perspective," Journal of Business Research, Elsevier, vol. 116(C), pages 188-192.
    13. Duro, Juan Antonio, 2016. "Seasonality of hotel demand in the main Spanish provinces: Measurements and decomposition exercises," Tourism Management, Elsevier, vol. 52(C), pages 52-63.
    14. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    15. Mauricio Carvache-Franco & Wilmer Carvache-Franco & Ana Gabriela Víquez-Paniagua & Orly Carvache-Franco & Allan Pérez-Orozco, 2021. "The Role of Motivations in the Segmentation of Ecotourism Destinations: A Study from Costa Rica," Sustainability, MDPI, vol. 13(17), pages 1-18, September.
    16. Gunter, Ulrich & Önder, Irem & Smeral, Egon, 2019. "Scientific value of econometric tourism demand studies," Annals of Tourism Research, Elsevier, vol. 78(C), pages 1-1.
    17. Martin, Christine A. & Witt, Stephen F., 1989. "Forecasting tourism demand: A comparison of the accuracy of several quantitative methods," International Journal of Forecasting, Elsevier, vol. 5(1), pages 7-19.
    18. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    19. Yi-Chung Hu, 2021. "Forecasting tourism demand using fractional grey prediction models with Fourier series," Annals of Operations Research, Springer, vol. 300(2), pages 467-491, May.
    20. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Combination forecasts of tourism demand with machine learning models," Applied Economics Letters, Taylor & Francis Journals, vol. 23(6), pages 428-431, April.
    21. Chia-Lin Chang & Michael McAleer & Vicente Ramos, 2020. "The Future of Tourism in the COVID-19 Era," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(3), pages 218-230, September.
    22. Sigala, Marianna, 2020. "Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research," Journal of Business Research, Elsevier, vol. 117(C), pages 312-321.
    23. Nicholas Apergis & Andrea Mervar & James E. Payne, 2017. "Forecasting disaggregated tourist arrivals in Croatia," Tourism Economics, , vol. 23(1), pages 78-98, February.
    24. Zhang, Hanyuan & Song, Haiyan & Wen, Long & Liu, Chang, 2021. "Forecasting tourism recovery amid COVID-19," Annals of Tourism Research, Elsevier, vol. 87(C).
    25. Chia-Lin Chang & Michael McAleer & Vicente Ramos, 2020. "A Charter for Sustainable Tourism after COVID-19," Sustainability, MDPI, vol. 12(9), pages 1-4, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Juan D. Borrero & Jesús Mariscal & Alfonso Vargas-Sánchez, 2022. "A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors," Stats, MDPI, vol. 5(4), pages 1-14, November.
    2. Yuzhen Li & Guofang Gong & Fengtai Zhang & Lei Gao & Yuedong Xiao & Xingyu Yang & Pengzhen Yu, 2022. "Network Structure Features and Influencing Factors of Tourism Flow in Rural Areas: Evidence from China," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
    3. Andrei-Florin Băbăț & Mirela Mazilu & Amalia Niță & Ionuț-Adrian Drăguleasa & Mihaela Grigore, 2023. "Tourism and Travel Competitiveness Index: From Theoretical Definition to Practical Analysis in Romania," Sustainability, MDPI, vol. 15(13), pages 1-26, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcos Álvarez-Díaz & Manuel González-Gómez & María Soledad Otero-Giráldez, 2018. "Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming," Forecasting, MDPI, vol. 1(1), pages 1-17, September.
    2. Gunter, Ulrich & Zekan, Bozana, 2021. "Forecasting air passenger numbers with a GVAR model," Annals of Tourism Research, Elsevier, vol. 89(C).
    3. Li, Cheng & Zheng, Weimin & Ge, Peng, 2022. "Tourism demand forecasting with spatiotemporal features," Annals of Tourism Research, Elsevier, vol. 94(C).
    4. Ali Zackery & Joseph Amankwah-Amoah & Zahra Heidari Darani & Shiva Ghasemi, 2022. "COVID-19 Research in Business and Management: A Review and Future Research Agenda," Sustainability, MDPI, vol. 14(16), pages 1-32, August.
    5. José María Martín Martín & José Antonio Rodriguez Martín & Karla Aída Zermeño Mejía & José Antonio Salinas Fernández, 2018. "Effects of Vacation Rental Websites on the Concentration of Tourists—Potential Environmental Impacts. An Application to the Balearic Islands in Spain," IJERPH, MDPI, vol. 15(2), pages 1-14, February.
    6. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    7. Chung-Wei Kuo, 2021. "Can We Return to Our Normal Life When the Pandemic Is under Control? A Preliminary Study on the Influence of COVID-19 on the Tourism Characteristics of Taiwan," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    8. José María Martín Martín & Jose Antonio Salinas Fernández & José Antonio Rodríguez Martín & Juan De Dios Jiménez Aguilera, 2017. "Assessment of the Tourism’s Potential as a Sustainable Development Instrument in Terms of Annual Stability: Application to Spanish Rural Destinations in Process of Consolidation," Sustainability, MDPI, vol. 9(10), pages 1-20, September.
    9. Paz Rico & Bernardí Cabrer-Borrás & Francisco Morillas-Jurado, 2021. "Seasonality in Tourism: Do Senior Programs Mitigate It?," Mathematics, MDPI, vol. 9(16), pages 1-27, August.
    10. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    11. Davide Provenzano & Serena Volo, 2022. "Tourism recovery amid COVID-19: The case of Lombardy, Italy," Tourism Economics, , vol. 28(1), pages 110-130, February.
    12. Martínez, José Manuel Guaita & Martín, José María Martín & Rey, María del Sol Ostos, 2020. "An analysis of the changes in the seasonal patterns of tourist behavior during a process of economic recovery," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    13. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    14. Anna Serena Vergori, 2017. "Patterns of seasonality and tourism demand forecasting," Tourism Economics, , vol. 23(5), pages 1011-1027, August.
    15. Michał Roman & Arkadiusz Niedziółka & Andrzej Krasnodębski, 2020. "Respondents’ Involvement in Tourist Activities at the Time of the COVID-19 Pandemic," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    16. Dimitrios Tsiotas & Thomas Krabokoukis & Serafeim Polyzos, 2021. "Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece," Tourism and Hospitality, MDPI, vol. 2(1), pages 1-27, February.
    17. Yang, Yang & Zhang, Carol X. & Rickly, Jillian M., 2021. "A review of early COVID-19 research in tourism: Launching the Annals of Tourism Research's Curated Collection on coronavirus and tourism1," Annals of Tourism Research, Elsevier, vol. 91(C).
    18. Azzeddine Madani & Saad Eddine Boutebal & Hinde Benhamida & Christopher Robin Bryant, 2020. "The Impact of Covid-19 Outbreak on the Tourism Needs of the Algerian Population," Sustainability, MDPI, vol. 12(21), pages 1-11, October.
    19. Foris Diana & Matei Cristina-Alexandra & Foris Tiberiu, 2021. "Exploring Solutions and the Role of GDS Technology in Crossing the Current Pandemic Context in Tourism," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 11(1), pages 91-101, December.
    20. Hadavandi, Esmaeil & Ghanbari, Arash & Shahanaghi, Kamran & Abbasian-Naghneh, Salman, 2011. "Tourist arrival forecasting by evolutionary fuzzy systems," Tourism Management, Elsevier, vol. 32(5), pages 1196-1203.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:1964-:d:745338. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.