Psychographic characteristics of weekend wine tourists : a multiple case study of four wineries in the Niagara Region

The purpose of this study is to examine the psychographic (product attributes, motivation opinions, interest, lifestyle, values) characteristics of wine tourists along the Niagara wine r,~ute, located in Ontario, Canada, using a multiple case study method. Four wineries were selected, two wineries each on the East, and West sides of the wine route during the shoulder-season (January, February, 2004). Using a computer generated survey technique, tourists were approached to fill out a questionnaire on one of the available laptop computers, where a sample ofN=321 was obtained. The study findings revealed that there are three distinct wine tourist segments in the Niagara region. The segments were determined using an exploratory factor analysis (EFA) and a K-means cluster analysis: Wine Lovers, Wine Interested, and Wine Curious wine tourists. These three segments displayed significant differences in their, motivation for visiting a winery, lifestyles, values, and wine purchasing behaviour. This study also examined differences between winery locations, on the East and West sides of the Niagara wine route, with respect to the aforementioned variables. The results indicated that there were significant differences between the regions with respect to these variables. The findings suggest that these differences present opportunities for more effective marketing strategies based on the uniqueness of each region. The results of this study provide insight for academia into a method of psychographic market segmentation of wine tourists and consumer behaviour. This study also contributes to the literature on wine tourism, and the identification of psychographic characteristics of wine tourists, an area where little research has taken place.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.