Determinants of Consumer Behavior in an e-Commerce Environmen

In presenting this thesis in partial hlfillment of the requirements for an advanced degree at The University of Maine, I agree that the Library shall make it freely available for inspection. I further agree that permission for "fair use" copying of this thesis for scholarly purposes may be granted by the Librarian. It is understood that any copying or publication of this thesis for financial gain shall not be allowed without my written permission. Online specialty food and beverage marketing has developed rapidly in the last decade. With the obvious increase in the sales income, researchers showed more and more interests in this promising market. Previous studies in this field primarily focused on the demographic profiling of the online specialty food consumers as well as initial analysis in the factors that motivate their purchase behavior. However, it is far from fully explaining consumer's online shopping activities. In order to solve these problems, new methods should be attempted to determine the factors influencing consumer's online buying behavior. A cluster analysis was developed to identify what kind of consumers have a higher preference of shopping online. And this analysis is based on consumer's lifestyle characteristics, which are assumed to be the determinants of their online shopping behavior. By identifying the consumer groups with a relatively high propensity of purchasing on the Internet, this research will provide improved information for companies marketing food and beverage products online and strengthen their abilities to develop effective marketing strategies. The data for this study were obtained from an online survey conducted from September 27th to October 25th, 2001. First a factor analysis was applied to the forty-two lifestyle variables and ten factors were extracted to represent them. Then the factor scores for each observation, an output data set of factor analysis, were submitted to a hierarchical cluster analysis using Ward's minimum-variance clustering method. As a result, lifestyle segmentations of the online specialty food and beverage market were obtained. Each segment was given a name based on the factor that has the highest loading on the particular group so as to emphasize its characteristics. Further analysis has also been done in order to compare the other related characteristics among the groups. ACKNOWLEDGEMENTS Many people deserve acknowledgements for their contribution to my education and the writing of this thesis. Dr. Gregory K. White, my major thesis advisor, deserves special recognition for the many hours he spent reviewing and …