The paper "Changes in Tourism Demand: External Factors" is an outstanding example of a management literature review. Tourism demand is the willingness and ability of people to visit certain places. According to Chan et al, variations in tourism demand depend on changes in economic, political, and technological environments. Technological factors include transport technology and computer reservation systems. Computer reservation systems help improve the e-marketing of a nation’ s tourist attractions, ensure tourists get reliable services, provide the flexibility of the tourism industry, and help potential tourists get reliable and unbiased information regarding tourist destinations.
On the other hand, transport technology helps increase production and consumption of tourism in several ways. It ensures tourist movements are cheap and fast. Transport technology also helps tourists have easier and faster access to transport facilities and services. In keeping with Prideaux (2005), changes in tourism demand may also be caused by political and government controls. Government policies manage the mobility of tourists in a country. Favorable government policies improve tourism demand by reducing taxes on tourism-related expenses and products, minimizing visa restrictions, and lowering user-pay charges.
Political stability also affects tourism demand in that it provides a favorable environment for tourism activities. It helps show the government’ s commitment and support to ensure there is tourism development. Other external factors that affect tourism include global relations and accessibility, environmental factors, and crisis problems. Globalization brings important international business alliances and relations which increases tourism demand. Nonetheless, any country should be capable of dealing with crisis and preserving the environment if it intends to ensure it achieves maximum tourism demand. Tourism Demand Prediction and Forecasting Tourism demand forecasting helps in the decision making of tourism-related businesses.
It involves very diverse econometrics models. Nevertheless, no model has proved to have more forecasting accuracy than other models and outperform them in all situations. This is because when constructing and estimating these models, forecasters mainly base their study on secondary information.
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Chan, F., Lim, C., & McAleer, M. (2005). Modelling multivariate international tourism demand and volatility. Tourism Management, 26(3), 459-471.
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Law, A., DeLacy, T., McGrath, M., & Whitelaw, P. (2011). Tourism destinations in the emerging green economy: Towards blending in brilliantly. In CAUTHE 2011: National Conference: Tourism: Creating a Brilliant Blend (p. 1171). University of South Australia. School of Management.
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Prideaux, B. (2005). Factors affecting bilateral tourism flows. Annals of Tourism Research, 32(3), 780-801.
Ritchie, J. R. B., & Crouch, G. I. (2003). The Competitive Destination: A sustainable tourism perspective. Wallingford: CAB International.
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting—A review of recent research. Tourism Management, 29(2), 203-220.
Swarbrooke, J. & Horner, S. (2007). Consumer behaviour in tourism. Amsterdam London: Butterworth-Heinemann.