Abstract Since economic recession happened globally in 2000, Taiwan has not been able to keep itself out of the financial tsunami. The unemployment rate was increasing gradually, and the economic problem was getting worse. In view of this, the central government led by Democratic Progressive Party at that time proposed ¡§Doubling Tourist Arrivals Plan¡¨ in order to improve the economic situation. The plan had the following merits: (1) Tourist industry itself had high added value. (2) The resource consumption was relatively of low pollution (e.g. less water use, and less garbage, so it was similar to the smokeless industry proposed by scholars in 1970). (3) Tourist industry had great correlation with other industries, and thus could bring about the development of related industries (such as food processing industry, transportation industry, retail business, insurance and financial industry ¡K etc.). It was hoped that the traditional industries which were going to have survival problems could be replaced by tourist industry. The proposed policy embraced the goals and objectives of the best economic and industrial development strategies, which were expected to create high economic growth, solve unemployment problem, and alleviate environmental destruction. At the very beginning the intention for establishment of this plan was quite good. However, the actual effects of the plan were not as sound as expected. The study mainly hopes to understand whether the implementation of the strategies had any obstruction or problems that made the plan unable to be thoroughly implemented and made the problems unsolved: (1) Were the planned objectives and the actual implementation sufficiently and perfectly connected, and were the results of the plan affected? (2) Was the allocation of budget and resources appropriately involved in the plan, and how was the effectiveness of the use of funds? (3) Did the influence caused and the effectiveness derived from the implementation of the plan conform to the expected outcomes, and what was the value or contribution caused to the development of tourist businesses of Taiwan? Therefore, the study mainly hopes to accomplish the purposes and positioning of the above three research items, and find out the critical problems and methods so as to give suggestions and endeavoring direction for the improvement and demand of tourist industry in the next step. Furthermore, the study gives concrete suggestions for the promotion of the employment ability, and for how to meet the need of training of the talents in tourist industry.

Globally, the tourism industry is recognised as one of the fastest growing industries, generating high revenues and creating a vast number of job opportunities. In South Africa, this is no different and, in recent years, the tourism industry has outshone the country's gold exports therefore claiming its position as the fourth highest earner of foreign exchange to date. Yet the industry is still to receive the attention it deserves from conventional economics. This research aimed to fill this gap in South Africa by providing an understanding on the determinants of international tourism demand for South Africa. The first objective of the study was to provide a broad overview of the tourism industry of South Africa. The discussion focused on the supply and demand sides of tourism which, in turn, are divided into the domestic and international tourism markets. There has been a high growth, especially in the international market since 1994 and, while domestic and international markets continue to grow, seasonality remains an issue. Tourism has a significant impact on economic activity, employment, and the balance of payments and therefore the industry has great potential. The second objective was to create a theoretical understanding on the different factors that could determine the international demand for the tourism product. From this discussion it was found that there are various economic and non-economic factors that are believed to have an influence on tourism demand. Income, prices, transport cost, and the exchange rate are amongst the favourite economic variables with travel time, population, marketing expenditure, climate, and capacity being the more popular noneconomic factors. Among these, certain threats were also identified that could have harmful impacts on tourism growth. The third objective and main aim of the study was to determine which of the factors identified earlier determine the demand for international tourism to South Africa. This was done through an empirical investigation. Data from all the continents were used to attain an international perspective on tourist arrivals (tourism demand). The results indicated that capacity and climate factors determine tourism demand in the short term with income and transport cost influencing South Africa as a tourism destination in the long term. The last objective was to determine whether certain events or disasters that take place globally have a negative influence on tourism demand to South Africa. The event that was looked as was the terror attacks on the United States in September 2001. It was found that although the overall tourism activity of the world became stagnant during this period, the effect was not that considerable in South Africa's tourism arrivals. Tourism in countries such as the United Sates, on the other hand, has still not recovered fully after this event. / Thesis (M.Com. (International Commerce))--North-West University, Potchefstroom Campus, 2007.

Globally, the tourism industry is recognised as one of the fastest growing industries, generating high revenues and creating a vast number of job opportunities. In South Africa, this is no different and, in recent years, the tourism industry has outshone the country's gold exports therefore claiming its position as the fourth highest earner of foreign exchange to date. Yet the industry is still to receive the attention it deserves from conventional economics. This research aimed to fill this gap in South Africa by providing an understanding on the determinants of international tourism demand for South Africa. The first objective of the study was to provide a broad overview of the tourism industry of South Africa. The discussion focused on the supply and demand sides of tourism which, in turn, are divided into the domestic and international tourism markets. There has been a high growth, especially in the international market since 1994 and, while domestic and international markets continue to grow, seasonality remains an issue. Tourism has a significant impact on economic activity, employment, and the balance of payments and therefore the industry has great potential. The second objective was to create a theoretical understanding on the different factors that could determine the international demand for the tourism product. From this discussion it was found that there are various economic and non-economic factors that are believed to have an influence on tourism demand. Income, prices, transport cost, and the exchange rate are amongst the favourite economic variables with travel time, population, marketing expenditure, climate, and capacity being the more popular noneconomic factors. Among these, certain threats were also identified that could have harmful impacts on tourism growth. The third objective and main aim of the study was to determine which of the factors identified earlier determine the demand for international tourism to South Africa. This was done through an empirical investigation. Data from all the continents were used to attain an international perspective on tourist arrivals (tourism demand). The results indicated that capacity and climate factors determine tourism demand in the short term with income and transport cost influencing South Africa as a tourism destination in the long term. The last objective was to determine whether certain events or disasters that take place globally have a negative influence on tourism demand to South Africa. The event that was looked as was the terror attacks on the United States in September 2001. It was found that although the overall tourism activity of the world became stagnant during this period, the effect was not that considerable in South Africa's tourism arrivals. Tourism in countries such as the United Sates, on the other hand, has still not recovered fully after this event. / Thesis (M.Com. (International Commerce))--North-West University, Potchefstroom Campus, 2007.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.