Development of a bias ratio to examine factors influencing hypothetical bias.

Using data from three contingent valuation studies, this study investigates if socio-economic factors and the type of good being valued can help explain changes in respondents' answers. A bias ratio, defined as the ratio of respondents who change their choice under hypothetical and "real" settings, is developed to help explain changes in respondents' answers. Marginal changes in the bias ratio resulting from a change in a particular independent variable consist of changes in responses in both the hypothetical and real situation. Results provide limited evidence that socio-economic variables and type of good being study can help in explaining bias. Respondents who are older, have a higher level of education, and are environmentally conscious tend to show less bias. Familiarity with the good also tend to decrease bias. A large amount of the variation in bias, however, remains unexplained.

The forecasting of International Expo tourism using quantitative and qualitative techniques.

This study predicts the number of visitors to an international tourism Expo to be held in Korea in 2012, an unprecedented event for the host city. Forecasting demand for such a mega-event has received only limited attention in the literature. Unlike most studies forecasting international tourism demand, forecasting Expo demand involves using both quantitative forecasting models and qualitative technique because of data limitations. Combining quantitative techniques with willingness-to-visit (WTV) surveys predicts the Expo demand at 8.9 million visitors. In comparison using the Delphi method, experts predict Expo demand at 6.8 million visitors. For this study, the Delphi method provides more conservative estimates than estimates from combining quantitative techniques with WTV. Policy implications presented are directed toward Expo planners and practitioners in terms of demand and supply side, application of these results in the decision-making process, and future challenges surrounding demand forecasting.