1 research outputs found
Determinants of online leisure travel planning decision processes :a segmented approach
D.B.A. ThesisThere 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