Operators and Development Organisations in Tourism

Question: Discuss about the Operators and Development Organisations in Tourism . Answer: Introduction Tourism is a major foreign exchange earner for Australia. In cities like Sydney, the arrival of tourists depends greatly on theme tourists. Such tourists, who come in groups, are visiting this magnificent city with the purpose of exploring the various cultural aspects of Australia. For economic and social reasons, forecasts about tourist arrivals becomes important as it enables the various tourism related sectors of the country such as airlines, hotels and other service providers. This forecasting also helps all these stakeholders in making preparation for the expected number of tourists at a given future period, assert Buhalis Crotts, (2013). This paper makes the attempt at forecasting arrival of tourists, especially in Sydney, by using the Statistical Time Series Modelling Techniques. Techniques used by the author include Double Exponential Smoothing and Auto-Regressive Integrated Moving Average (ARIMA). All the data related to arrival of tourists in Sydney has been obtained from Australia Bureau of Statistics, the Australian Trade Commission and Department of Tourism, Government of New South Wales, as per Erskine Meyer, (2012). The author has used the tourist arrivals for the period 2006 to 2015 for model validation. Arrivals of international tourists and the revenues generated from these international tourists have been used by nations across the globe as benchmark aggregate series for arriving at an assessment of the importance of tourism by specific countries. Nations often make use of advertising campaigns and international political discussion forums for arriving at a higher international tourist arrivals level so as to emphasize the success of their country in the international community, say Evans, Stonehouse Campbell, (2012). On the same scale, a sizeable increase in international tourist revenues become a good indicator of the role played by tourism in the economy of a nation when assessing its Gross Domestic Product and foreign exchange generation. Subsequently, the policy makers get convinced and assist in development of tourism and to further increase the profitability from these enhanced tourism activities. In Australia, the overseas visitors contributed about 25% of the total touri sm earnings of the country. In 2014-15, the revenue generated from overseas visitors was $32billion and it represented about 11% of Australias total export revenue, asserts Harris, (2012). Tourism Forecasting Methodology Rapid global growth of the tourism industry across all nations in the past 20 years is instrumental in contributing highly to the economy of that nation. The research data provided by World Travel Tourism Council (WTTC) has shown that output value of global tourism, including other sectors related to tourism, was US$5.474 trillion and this was 9.4% of the Global GDP in 2009. According to WITC, this output value of global tourism industry may reach US$10.478 trillion, generating 9.5% of Global GDP by 2019, as detailed by Gilligan Hird, (2012). Methodologies used for tourism forecasting can be broadly divided into Qualitative and Quantitative methodologies. Regardless of the forecasting methodology used, this author has noticed that usefulness of the tourism demand forecasting model is actually based on the accuracy of the tourism demand forecasts which the technique generates, especially when it is measured in comparison with the actual inflow of the tourist arrivals, as defined by Hall, Timothy Duval, (2012). In this regard, there are five patterns in use while working on a tourism time series: Seasonality Stationarity Linear Trend Non-linear Trend and Stepped Series. It has also been observed that while forecasting, the single variable approach has limitations as it lacks in explanatory variables. Also, because it is best suitable when making short- to medium-term forecasting, assert Erskine Meyer, (2012). Another factor in this approach are the factors which are directly related to seasonality, trend and cycle, as they change slowly and can be best extrapolated in the short- to medium term forecasting, say Huimin, Ryan, (2011). The conventional tourism demand forecasting methodologies have been categorised as Univariate Time-series Approaches and Multivariate Demand Modelling Approaches. In this respect, say Lemelin, Dawson Stewart (ed.), (2013), the latter can be implemented by using either a conventional four-step travel planning model or direct demand model. As far as overseas tourist demand forecasting is concerned, the direct demand modelling approach has been used more often because of its capability to identify the demand elasticity which is helpful in representing the causal relationship between the demand and the explanatory variables, assert Lemelin, Dawson Stewart (ed.), (2013). However, in many of the forecasting studies conducted, one of the factors comprises of the facilities which the tourists demand and the prices at which the tours are offered. Under such circumstances, this model has shown that the Univariate Approach has been able to demonstrate a better prediction accuracy. The universal application of the Univariate Time-series Models, according to Jung, Namkung Yoon, (2012), in arriving at an accurate overseas tourist demand forecasting has established the superiority of this forecasting model in this field. Univariate Time-series Model This paper uses the univariate model and the multivariate direct demand model for forecasting overseas tourist arrival in Sydney. The author has used the ARIMA model for Univariate Time-series Analysis and this discussion is based on the dynamic Partial Adjustment Model (PAM), which is constructed from the Sydney Household Travel Survey (SHTS), as explained by Jung, Namkung Yoon, (2012). For the direct demand modelling approach ARIMA model has been used. This author has found that using a number of time-series models, which have been developed for the purpose of forecasting, does not in fact specify a superior time-series model, as this author has noticed and as has been detailed by Evans, Stonehouse Campbell, (2012), that the forecasting power solely depends on the nature of the data used and the context in which the study has been conducted. The ARIMA model, which was introduced by Box and Jenkins in 1970, has been the most widely used time-series model because of its capability to process non-stationary as well as the seasonal data. Hence, instead going into the comparison of performance of the different univariate time-series models available, the focus of this paper is on the forecasting power between univariate modelling technique and multivariate modelling technique, assert Lemelin, Dawson Stewart (ed.), (2013). This paper also looks at their implications for their practical use of tourist demand forecasting. Hence, the author has selected the ARIMA model for the univariate analysis in this paper solely because of its popularity in tourist demand forecasting studies as well as its flexibility while using a wide range of applications. The ARIMA model, typically denoted as an ARIMA (p, q) model, consists of the Auto-Regressive (AR) term and the Moving Average (MA) term. The AR (p) model uses p lags of time for predicting the dependent variable y as is specified below in equation which is referred to as ARIMA (p, 1, q) Equation. Data plotted in Graph-1 shows that the time series was a non-stationary one as there was certain trend component present in the flow of data. As per the available trend shown, the data was made into a stationary one by taking into consideration the first order difference (d = 1). The arrived at time series of this differenced data has been shown in Graph-2. Using the R-language for different values of p and q, the author arrived at different results of the ARIMA model, which were fitted for comparison with the requirements and then the best model was selected based on the minimum values prescribed in the selection criteria. For this purpose, this paper chose the Akaike Information Criteria (AIC), the formula for which is given in the equation shown below. Based on these findings, this author arrived at the best ARIMA (1, 1, 1) model. The best model was represented by the following equation. The author also made use of the Root Mean Square Error (RMSE) technique and the Mean Absolute Percentage Error technique (MAPE) using the following formulae for arriving at the best ARIMA Model. In making all these estimations, this author has used the Maximum Likelihood Estimation Technique (MLET). After this, use of data was made for forecasting the arrival of international tourists for the period 2006 to 2015 and the relevant values have been shown in the Table shown in the Appendix. List of References Buhalis, D. and Crotts, J. 2013. Global alliances in tourism and hospitality management. Routledge, Oxon. Erskine, L. M. and Meyer, D. 2012. Influenced and influential: The role of tour operators and development organisations in tourism and poverty reduction in Ecuador. Journal of Sustainable Tourism, 20(3), 339-357. 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