An analysis of students' travel motivations and images of China as a tourist destination

Despite China's rapid growth in inbound tourism, the nature of its Canadian tourist market has been insufficiently studied. In response to this need, the objectives of this study are to identify China's destination image in Canadian students' minds, their possible internal motivations for visiting China as well as examining demographic influences on people's destination image formation. The study reviews image formation process and travel motivation categorisation, discusses their relationship, and implements Baloglu and McCleary's (1999) perceptual and affective image formation model and "push and pull factors" theory as its framework. A self-administered survey was applied to 424 undergraduate students in a Canadian university in early 2004. Exploratory factor analyses were conducted to identify perceived images and travel motivation. Summated means were calculated to illustrate the affective attitudes. A series of f-test and ANOVA tests were employed to examine the influence of demographics. An open-ended question format was adopted to analyse other images, motivations and visitation barriers that students may have. Findings demonstrate that cultural and natural attractions are the predominant image which the Canadian students have of China'; some stereotypes and negative images still influence the students' perception; travel service quality is largely unknown; increasing knowledge and seeking excitement and fun are the significant motivators in the likelihood of the Canadian students choosing to visit China; and personal interests may be a factor that significantly influences an individual's destination image and travel motivation. Raising awareness and increasing familiarity through promotion are suggested as methods to create a positive destination image of China.

City of the Falls clippings, 1833, n.d.

The clippings describe the City of the Falls and its attempts by its shareholders to sell lots to this newly developing city. The venture by shareholders W. Allan, James Buchanan, Thomas Clark, J.H. Dunn, Thomas Dixon, General Murray, James Robinson, Samuel Street and William Witla intended to favourably position their city as a destination for “affording an easy approach for the annual assemblage of the Fashionable, the Learned and the Great”. The venture failed due to the building of the Buffalo and Niagara Falls railway.

Niagara-on-the-Lake, Canada. Commemorative stamps, 1981.

Niagara-on-the-Lake was originally known as Butlersburg, after Colonel John Butler, the commander of Butler's Rangers. This military corps served in British North America until 1784. At that time, the general forces of the British Army in North America were significantly reduced. In 1781, the town received official status. It became known as Newark, then Niagara, and subsequently Niagara-on-the-Lake in order to distinguish itself from Niagara Falls. It is historically significant as the original capital of Upper Canada and is a popular tourist destination.

Multi-Objective Genetic Algorithms for the Single Allocation Hub Location Problem

Hub Location Problems play vital economic roles in transportation and telecommunication networks where goods or people must be efficiently transferred from an origin to a destination point whilst direct origin-destination links are impractical. This work investigates the single allocation hub location problem, and proposes a genetic algorithm (GA) approach for it. The effectiveness of using a single-objective criterion measure for the problem is first explored. Next, a multi-objective GA employing various fitness evaluation strategies such as Pareto ranking, sum of ranks, and weighted sum strategies is presented. The effectiveness of the multi-objective GA is shown by comparison with an Integer Programming strategy, the only other multi-objective approach found in the literature for this problem. Lastly, two new crossover operators are proposed and an empirical study is done using small to large problem instances of the Civil Aeronautics Board (CAB) and Australian Post (AP) data sets.