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Michael Conyette

Diplômé DBA - 2009

Titre de thèse

determinants if online leisure travel planning decision processes: a segregated approach

Superviseur(s)

Michel Polski
There is an abundance of information sources on the Internet that consumers use to plan and book their travel. In turn, travel comprises a significant part of the business conducted through the Web. Consumers sometimes face the complex task of making purchasing decisions in this dynamic and fast-paced medium. Yet despite the importance of travel and the intricacies of the decision process, an integrated framework that identifies the various determinants of the online leisure travel planning decision process is largely absent. This study extracts from relevant literature several useful elements that might constitute such a framework. Through several phases of qualitative research, this thesis refines the framework and then undertakes a quantitative assessment of data collected from an online questionnaire, completed by 1,198 respondents, to test specific components of the framework that deal with online travel booking intentions. A final model-building stage compares three logistic regression models. The first is a parsimonious model that contains key determinants that lead to online travel booking intentions. These determinants reflect the theoretical frameworks of the theory of reasoned action and innovation adoption theory. The second model includes only involvement, motivation, and knowledge variables, which are thought to influence online booking intentions. The third model includes a combination of relevant predictor variables from the other two models. The relationship of various demographics and online travel booking intentions yields some interesting insights; companies should consider such demographic variables when they attempt to segment travelers to identify those who are more likely to book online. The determinants of online leisure travel booking decision processes also could be used in conjunction with demographic variables to predict leisure travel Website usage more accurately.