No abstract

This paper aims to examine the demand for outbound tourism by mainland Chinese residents to 11 international destinations, and provide long-run forecasts up to the year 2020. The empirical results suggest that the income level and the cost of a stay at a tourism destination compared with that of staying at a Chinese tourism destination are two important factors that affect Chinese residents' traveling abroad. Results also show that the long-run income elasticities for all destinations range from 0.406 to 1.785, while the own price elasticities vary from −9.490 to −0.152. Based on the response patterns of tourism demand to income and price changes, five main categories of Chinese outbound tourism markets are identified. An ex ante forecasting exercise is also carried out which shows that outbound tourists from China mainly travel to Asian countries/regions during the period 2012-2020. The total number of outbound tourists is expected to reach 138.7 million by 2020.

There has been much rhetoric about tourism's role in promoting world peace. This research takes a global perspective examining the relationship between peace and tourism across 111 different countries using a panel data model using two indicators, international tourist arrivals and the Institute for Economics and Peace's Global Peace Index. The results indicate that tourism is the beneficiary of peace rather than grounds for peace. Peace is most important to tourism in medium income destinations but still important for high income nations. No relationship exists between peace and tourism arrivals for low income nations.

No abstract

No abstract

Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro-level by predicting the number of visits to London museums. The number of visits to London museums is forecasted and the predictive powers of Naïve I, seasonal Naïve, SARMA, SARMAX, SARMAX-MIDAS and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models' forecasting accuracy varies for short-and long-term demand predictions. The application of higher-frequency search query data allows generation of weekly predictions, which are essential for attraction-and destination-level planning.

Purpose Leader–member exchange (LMX) theory is particularly relevant to the hospitality and tourism industry due to its labor-intensive and service-focused nature. However, the hospitality literature regarding the impact of LMX on its various outcomes have inconsistent results. A holistic review of LMX studies is nonexistent in the current literature. Thus, the purpose of this study is to use a meta approach to quantitatively summarize and examine the relationship between LMX and its outcomes in the hospitality and tourism literature. Design/methodology/approach A total of 89 individual observations from 36 studies conducted between 1997 and 2018 were identified. A Bayesian random effect model was introduced into the hospitality and tourism literature for the first time to implement the meta-analysis. Findings The results suggest significant differences in the impact of LMX on various groups of outcomes. LMX has the strongest impact on firms’ practice-related outcomes, such as organizational justice and employee empowerment. Few moderators are identified on the impact of LMX, such as LMX measure, culture, industry sector and statistical method. Practical implications Findings yielded several recommendations for both hospitality researchers and organizations in developing LMX related studies, as well as managing employees. Originality/value This study is the first Bayesian meta-analysis in the hospitality and tourism literature; it complements LMX theory by linking it to cognitive appraisal theory. Specific characteristics of LMX in the hospitality and tourism industry, such as the measurement of LMX and the effect of industry sector, are also identified.

No abstract