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The majority of research on place attachment has been conducted within a domestic context where local culture is fairly familiar to the visitor. International tourism research has begun to address destination attachment, but has not yet fully explored the concept where the people and the culture are substantially different. This research used in-depth qualitative interviews with international visitors to Thailand to examine the role of local people in destination attachment. Results show that one of the main attributes of international visitors’ attachment is the Thai people. A follow-on survey confirmed that “people attachment” is an integral part of the measurement of destination attachment. [ABSTRACT FROM AUTHOR]

Despite growing research interest in international students, potential links between international tourism and education have received little attention to date. A survey among full-degree foreign students at an Australian university aims to fill this gap. The identified links between travel and study and the conversion of desired into actual travel lead to a proposed conceptual framework of information sources, destination image, and a continuum of study and tourism activities. Further research is proposed to better understand mutual influences of education and travel, their relative importance for international students, and the opportunities for cooperation and exploitation of synergies among respective authorities. [ABSTRACT FROM AUTHOR]

Forecasting has been considered important in a service industry. Many techniques have been applied to improve forecasting results. This study intends to apply a neural network based fuzzy time series model to forecast the international tourist numbers arriving in Taiwan. Neural network is good at handling nonlinear data. On the other hand, the fuzzy time series models have been applied to time series problems in various domains and have been shown to outperform some conventional models. The tourist numbers arriving in Taiwan show a nonlinear characteristic with a structural break. And the application of the neural network based fuzzy time series model is expected to outperform some other models in forecasting these tourist numbers. [ABSTRACT FROM AUTHOR]