Determinants of online leisure travel planning decision processes :a segmented approach

Abstract

There is an abundance of information sources on the Internet that consumers use to plan and book their travel. This information reflects the fact that travel comprises a significant part of the business conducted through the web. Consumers are sometimes faced with a complex task of making purchasing decisions in the dynamic and fast-paced medium of the Internet. In spite of 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 and how they interact, is largely absent in travel literature. This study aims to make a contribution by extracting from relevant literature useful elements that could comprise such a framework. It also uses several phases of qualitative research to refine the framework, and then 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 intention. In the final model building stage, three logistic regression models were compared. The first is a parsimonious one containing key determinants that lead to online travel booking intention. These determinants emerged from theoretical frameworks of the theory of reasoned action and innovation adoption theory. The second Model used strictly involvement, motivation, and knowledge variables that are thought to influence online booking intention. The third Model included a combination of relevant predictor variables from the other two Models. The relationship between various demographics and online travel booking intention was investigated yielding some interesting insights. Consequently, this study recommends these demographic variables be considered in segmenting travelers to find those more likely to book online. The determinants of online leisure travel booking decision processes could be used in conjunction with demographic variables to more accurately predict leisure travel website usage.