The aims of the paper are two-fold, namely to analyse the seasonal patterns of tourist arrivals from South Korea to Australia, and to use econometric time series modelling to quantify the factors affecting the flow of international tourists between Australia and Korea. Estimates of the effects of changes in income, relative prices, exchange rates, seasonality and other factors are provided, and...

This paper introduces an approach of enhancing tourism web sites, giving the ability for such sites to be used as a tool to allow tourists to tag content of choice for downloading and viewing on their own mobile device whilst on visit. Tourists, upon installation to their mobile device, can view content material and a map of Mytilene (Lesvos, Greece) without the need to “connect” to any mobile ...

Seeking to shift the discussion of the concept of authenticity in tourism scholarship from the dominant concern with tourist experiences to the more sociological problem of the processes of authentication of tourist attractions, we conceptualize two analytically distinct, but practically often intersecting, modes of authentication of attractions, “cool” and “hot”. Through a range of examples, w...

We assess the strength of the different conditions identified in the literature of robust mechanism design. We focus on three conditions: ex post incentive compatibility, robust monotonicity, and robust measurability. Ex post incentive compatibility has been shown to be necessary for any concept of robust implementation, while robust monotonicity and robust measurability have been shown to be n...

wetland functions is the monitoring of newly restored wetland sites to determine how quickly wetland functions develop and whether function-based mitigation objectives are being met. This technical note presents a case study of the application of the Western Kentucky Low-Gradient Riverine Guidebook to monitoring the development of wetland functions on a number of bottomland hardwood forest site...

The engineering of machine learning systems is still a nascent field; relying on a seemingly daunting collection of quickly evolving tools and best practices. It is our hope that this guidebook will serve as a useful resource for machine learning practitioners looking to take advantage of Bayesian optimization techniques. We outline four example machine learning problems that can be solved usin...