The purpose of this study is to explore the influence of quality of life and residential status on resident attitudes toward further tourism development. The measurement of tourism and quality of life (TQOL) is modified. Using a sample of 562 residents from Shenzhen OCT community of China, this study has identified six TQOL domains and examines the effects of each TQOL domains based on the residential status and residents' attitudes in supporting further tourism development. The results reveal that the positive supporting attitudes of residents depends on the selected TQOL domains, especially on non-material improvements of TQOL. Tenants and dormitory residents have more positive attitudes than those house owners. This study also identifies four resident clusters with different attitudes and it is found that the residents' attitudes of tourism development depend on whether they perceive the community as a place for earning a living or a place to live.

Despite the increasing number of online users and products that are being offered on the Web, there is relatively little work that specifically examines the role of gender and educational level on the attitudes of Internet users in the Singapore context. Our findings reveal that there is a general consensus amongst Singaporeans that the Internet is a convenient medium for information search or making purchases. The better‐educated respondents seem to be less concerned with security issues. They also perceive that Internet shopping provides better prices and more cost savings. Females indicate a strong dislike for not being able to savour a physically fulfilling shopping experience online.

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Most forecasting models for tourist arrivals were constructed under the assumption of only minor changes in the environment. The performance of forecasting models in situations of sudden, drastic environmental change(s) has not been given much attention. Using the Gulf War as an example of sudden environmental change, the present article explored the relative performance of different forecasting models. The findings showed that in terms of forecasting accuracy, the naive II model was the best in dealing with unstable data.