Alanzi, Eman Mealith (2023) Modelling and Forecasting Saudi Arabia’s Inbound Tourism Demand. PhD thesis, Victoria University.

Abstract

The tourism sector in Saudi Arabia has been identified as one of the priority sectors in Saudi’s Vision 2030. This vision is focused on diversifying the economy, contributing to economic growth (of more than 10 per cent), and creating one million jobs by 2030. The Saudi tourism industry has recently witnessed a spectacular expansion in recent years due to the introduction of clear and specific policies and institutional structures. However, for effective tourism management strategy and planning, appropriate policy decisions and infrastructure development, there needs to be a greater understanding of what factors influence international tourism demand. Motivated by this need, this study has three main objectives: to identify the impact of the main determinants (both economic and selected non-economic factors) of inbound tourism demand; to forecast inbound tourism demand; and to assess and project the impact of the COVID-19 pandemic on international tourist arrivals to Saudi Arabia. To address gaps in the body of knowledge, this study introduces country-specific factors into tourism demand models, including human rights issues, destination prosperity, students studying abroad, and expatriate workers. This study also fills gaps in existing knowledge by developing holistic models focused on an analysis of specific tourism market segments: religious, business, and visiting friends and relatives (VFR). To obtain robust results, this study used both static and dynamic panel estimators to measure the effects of both economic factors and selected non-economic factors on tourist flows to Saudi Arabia, from 2000 to 2019. The latest econometric models, time-series models, and two combined forecasting methods were employed to generate within-sample forecasts. To test whether a combined forecast model could outperform the individual model forecasts, root mean squared error (RMSE) and mean absolute percentage error (MAPE) approaches were used to measure forecast accuracy. Finally, scenario analysis, impulse response functions (IRF), and quantile regression (QR) were conducted to assess the impact of the COVID-19 pandemic health shock on tourism demand in Saudi Arabia during 2020 and 2021. The results indicate that the income of the tourist origin countries, the income of the destination country (Saudi Arabia), travel costs, the cost of living at the destination (tourism price), investment in the tourism sector, political risks, and destination prosperity impacted all tourist market segments. In addition, word-of-mouth, visa restrictions, and relative temperature had a significant impact on religious tourism demand. Increased government respect for human rights had a positive and significant effect only on religious and business tourism. Trade openness had a positive and significant effect on business tourism, and Saudi students studying abroad had a positive and significant impact on VFR tourism. The number of expatriate workers had a positive and significant impact on business and religious tourism demand but a negative effect on VFR tourism. The results suggest that business tourists were more sensitive to health risks than religious and VFR tourists between 2000 and 2019. When comparing econometric and time series model forecasting, time series models provided more accurate forecasts for religious and business tourism demand, whereas the econometric model provided more accurate forecasts for VFR tourism. The combined forecast method produced more accurate predictions only for business and VFR tourism. Scenario analysis was useful for assessing the short-term impact of COVID-19, whereas the IRF may be useful for understanding the long-term impact. This study indicates that the COVID-19 outbreak significantly and negatively influenced Saudi Arabia's tourism industry, as travel restrictions and bans were imposed by governments across the globe. The study also shows that religious tourism was the most affected by the pandemic and needed the longest time to recover, whereas business tourism recovered relatively rapidly. The QR model indicated that the negative impact of confirmed COVID-19 cases was more at the lower quantiles of tourism demand, while there was less negative impact at the higher quantiles. The findings of this study may assist in developing Saudi Arabia's tourism sector and economy by providing knowledge to policymakers, investors, and tourism promoters. This will enhance the development of tourism policies and increase the number of international tourists, a central goal of Vision 2030 to diversify the Saudi economy.

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