A Review of the Methods and Techniques Used in Tourism Demand Forecasting

Authors

  • R. Shakir Al Jassim School of Computing and Information Sciences, UTAS University, Oman
  • Karan Jetly School of Computing and Information Sciences, UTAS University, Oman
  • Ahmad Abushakra Business Information Technology, Princess Sumaya University for Technology, Jordan
  • Sh Al Mansori School of Computing and Information Sciences, UTAS University, Oman

DOI:

https://doi.org/10.4108/eetct.v9i31.2986

Keywords:

Social media, Forecasting Tourism Demand, Web traffic, Search Engines, Forecasting Methods

Abstract

The purpose of this paper is to discuss the methodology and results of researchers who conducted a study concerning the forecasting of tourism demand. In more detail, this study aims to examine and assess various studies about search engines, web traffic data, and social media data, specifically. Using an extensive database of indexed articles, we conducted the review with the goal of providing a solid understanding of the literature. The findings of our study revealed that few researchers integrate different data sources when forecasting tourism demand. Therefore, the authors of this paper decided to conduct a systematic review to provide researchers with a comprehensive overview of the importance of such data. This paper may inspire Omani researchers to undertake similar research based on its findings, which is currently lacking. Thus, this paper will improve understanding of how data sources affect forecasting accuracy and how modern technologies can support economic growth.

References

A.F. Colladon, B. Guardabascio and R. Innarella,(2019) Using social network and semantic analysis to analyze online travel forums and forecast tourism demand,Decision Support Systems,113075, https://doi.org/10.1016/j.dss.2019.113075.

Ainin, Sulaiman; Feizollah, Ali; Anuar, Nor Badrul; Abdullah, Nor Aniza (2020) Sentiment analyses of multilingual tweets on halal tourism. Tourism Management Perspectives,34, 100658–. doi:10.1016/j.tmp.2020.100658

Álvarez Diaz, M., Mateu Sbert, J. and Rossello Nadal, J. (2009) Forecasting tourist arrivals to Balearic Islands using genetic programming, Int. J. Computational Economics and Econometrics, Vol. 1, No. 1, pp.64–75.

Beverley A. Sparks; Victoria Browning (2011) The impact of online reviews on hotel booking intentions and perception of trust,32(6),1310–1323. doi:10.1016/j.tourman.2010.12.011.

Bigne, Enrique; Oltra, Enrique; Andreu, Luisa (2019) Har-nessing stakeholder input on Twitter: A case study of short breaks in Spanish tourist cities. Tourism Manage-ment,71, 490–503. doi:10.1016/j.tourman.2018.10.013.

Bangwayo Skeete, P. F., and Skeete, R. W.(2015) Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46, 454–464. doi:10.1016/j.tourman.2014.07.014.

Bokelmann, Björn; Lessmann, Stefan (2019) Spurious pat-terns in Google Trends data : An analysis of the effects on tourism demand forecasting in Germany. Tourism Man-agement, 75, 1–12. doi:10.1016/j.tourman.2019.04.01.

Carriere-Swallow, Y., Labb e, F. (2011) Nowcasting with Google Trends in an emerging market. Journal of Forecasting, 32, 289-298.

Choi, H., Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2-9.

Geva, T., Oestreicher-Singer, G., Efron, N., and Shimshoni, Y. (2017) Using forum and search data for sales prediction of high-involvement products. MIS Quarterly, 41(1), 65–82.

Ginsberg, J., Mohebbi, M., Patel, R. et al.(2009) Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014. https://doi.org/10.1038/nature07634.

Gunter, U., and Onder, I. (2016) Forecasting city arrivals with Google analytics. Annals of Tourism Research, 61, 199–212.

Havranek, Tomas; Zeynalov, Ayaz (2018) Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data, ZBW – Leibniz Information Centre for Economics, Kiel, Hamburg.

Hisham Nawzer, Manoja Weerasekara, (2018) A Case Study On Customizing The Microsoft Time Series Algorithm: Tourist Arrival Prediction, International Conference On Business Innovation (ICOBI), 25-26 August 2018, NSBM, Colombo, Sri Lanka.

Huang, X., Zhang, L., and Ding, Y. (2017) The Baidu Index: Uses in predicting tourism flows– A case study of the Forbidden City. Tourism Management,58, 301-306.

Li, Gang; Law, Rob; Vu, Huy Quan; Rong, Jia; Zhao, Xinyuan (Roy) (2015) Identifying emerging hotel preferences using Emerging Pattern Mining technique. Tourism Management, 46, 311–321. doi:10.1016/j.tourman.2014.06.015.

Li, H., Hu, M., Li, G. (2020) Forecasting tourism demand with multisource big data. Annals of Tourism Research, 83, 102912. doi:10.1016/j.annals.2020.102912.

Li, S., Chen, T., Wang, L., Ming, C. (2018) Effec-tive tourist volume forecasting supported by PCA and improved BPNN using Baidu index. Tourism Manage-ment, 68, 116–126. doi:10.1016/j.tourman.2018.03.006.

Li, X., Pan, B., Law, R., Huang, X. (2017) Forecasting tourism demand with composite search index. Tourism Management, 59, 57–66. doi:10.1016/j.tourman.2016.07.005.

M. Mariani, R. Baggio, D. Buhalis, C. Longhi (2014) Tourism Management, Marketing, and Development, Copyright © Marcello M. Mariani, Rodolfo Baggio, Dimitrios Buhalis, and Christian Longhi.

Miah, S. J., Vu, H. Q., Gammack, J., and McGrath, M.(2017) A Big Data Analytics Method for Tourist Behaviour Analysis. Information and Management, 54(6), 771–785. doi:10.1016/j.im.2016.11.011

Nikolopoulos, K., Metaxiotis, K., Assimakopoulos, V., and Tavanidou, E. (2003), A first approach to e-forecasting: a survey of forecasting web-services’, Infor-mation Management and Computer Security, Vol 11, No 3, pp 146–152.

Önder, Irem (2017) Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities. International Journal of Tourism Research,doi:10.1002/jtr.2137.

Önder, I., Gunter, U., ans Gindl, S. (2019) Utilizing Facebook Statistics in Tourism Demand Modeling and Destination Marketing. Journal of Travel Research, 1-14, 004728751983596. doi:10.1177/0047287519835969.

Önder, I., Gunter, U., and Scharl, A. (2019) Forecasting tourist arrivals with the help of web sentiment: A mixed-frequency modeling approach for big data. Tourism Analysis, 24(4), 437–452.

Park, S., Lee, J., and Song, W. (2017) Short-term forecasting of japanese tourist inflow to south korea using google trends data. Journal of Travel Tourism Marketing, 34(3): 386–397. doi:10.1016/j.tourman.2014.07.019.

Pan, B., Wu, D. C., and Song, H. (2012) Forecasting hotel room demand using search engine data. Journal of Hospitality and Tourism Technology, 3(3), 196–210.

Rob Law, Gang Li, Davis Ka Chio Fong, Xin Han (2019) Tourism demand forecasting: A deep learning approach, Annals of Tourism Research, Volume 75,Pages 410-423,ISSN 0160-7383, https://doi.org/10.1016/j.annals.2019.01.014.

Sangkon Park, Jungmin Lee and Wonho Song (2016) Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data, Journal of Travel and Tourism Marketing, DOI: 10.1080/10548408.2016.1170651

Schaer, Oliver; Kourentzes, Nikolaos; Fildes, Robert (2018) Demand forecasting with user-generated online information. International Journal of Forecasting, S0169207018300505. doi:10.1016/j.ijforecast.2018.03.005.

Sun, S., Wang, S., Wei, Y., Yang, X., and Tsui, K.-L. (2017) Forecasting tourist arrivals with machine learning and internet search index.IEEE International Conference on Big Data (Big Data).doi:10.1109/bigdata.2017.8258439.

Song, H., and Li, G. (2008) Tourism demand modelling and forecasting : A review of recent research. Tourism Management, 29(2), 203–220. doi:10.1016/j.tourman.2007.07.016

Theologos Dergiades, Eleni Mavragani, Bing Pan (2018), Google Trends and tourists’ arrivals: Emerging biases and proposed corrections,Tourism Management, Volume 66, Pages 108-120, ISSN 0261-5177, https://doi.org/10.1016/j.tourman.2017.10.014.

Volchek, K., Liu, A., Song, H., Buhalis, D. (2018) Forecasting tourist arrivals at attractions: Search engine empowered methodologies. Tourism Economics, 135481661881155. doi:10.1177/1354816618811558

Xiang, Z., Schwartz, Z., Gerdes, J. H., and Uysal, M. (2015) What can big data and text analytics tell us about hotel guest experience and satisfaction?International Journal of Hospitality Management, 44, 120–130. doi:10.1016/j.ijhm.2014.10.013.

Yuan, Fong-Ching (2020) Intelligent forecasting of inbound tourist arrivals by social networking analysis. Physica A: Statistical Mechanics and its Applications, 124944–. doi:10.1016/j.physa.2020.124944.

Yang, Y., Pan, B., and Song, H. (2014) Predicting hotel demand using destination marketing organization’s web traffic data. Journal of Travel Research, 53(4), 433–447.

Yang, X., Pan, B., Evans, J. A., and Lv, B. (2015) Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386–397. doi:10.1016/j.tourman.2014.07.019.

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Published

13-01-2023

How to Cite

1.
Jassim RSA, Jetly K, Abushakra A, Mansori SA. A Review of the Methods and Techniques Used in Tourism Demand Forecasting. EAI Endorsed Trans Creat Tech [Internet]. 2023 Jan. 13 [cited 2023 Feb. 6];9(4):e1. Available from: https://publications.eai.eu/index.php/ct/article/view/2986