Travel Reference
In-Depth Information
The information is of great help for governments to improve their destinations, for travel agencies
and tour operators to more effectively promote their products and for marketers to devise
appropriate marketing strategies.
Conclusion
Depending on different objectives, different preference estimation methods can be used in a
range of situations. For the tourism market where little or nothing is known about, the more
direct methods such as simple questionnaires and interviews are handy to obtain the fi rst
impression of what attributes or factors tourists in this group care about. After narrowing down
the important attributes into a shortlist, it is possible to test the specifi c infl uences of certain
attributes or the combined effects of multi-attributes by more sophisticated methods such as
regressions or conjoint analysis. If more detailed exploration about the mental processing in
tourism decision-making is required, rather than thinking of it as a simple input and output
procedure, the AHP method that decomposes decision-making into different stages may be
applied. And in some contexts such as limited information available or limited time to make the
decision, where tourists do not use utility maximization evaluation, methods that are based on
non-compensatory choice heuristic theory such as greedoid method could be useful. However,
the methods mentioned in this chapter are not the only options to estimate tourists' preference
but just those commonly available and used.
All of these methods are adopted from other disciplines (e.g. economics) or research fi elds
(e.g. marketing research and operations studies). Although these methods are very useful tools to
investigate general decision-making, tourism decision-making may have its own features
compared to other types of decision-making. Therefore, how to adapt these methods accordingly
is a key issue for tourism scholars. A smart methods combination is one option. For example, due
to the large number of destinations available, Hsu (2009) combined fuzzy theory with the
traditional AHP to reduce the huge workload for tourists to compare the alternatives. Cina
(2012) combined game theory with conjoint analysis to identify which combinations of
attributes are suitable for different tourism festivals.
Moreover, with the development of tourism decision-making studies, more innovative
research methods are desired to further explore tourists' preferences rather than staying at
the stage of identifying preferred attributes or assign utility values to different attributes.
For instance, do tourists evaluate destinations rationally? How do their preferences change at
different stages? How is it possible to distinguish between different preference groups? All of
these questions require more sophisticated theory models and estimation methods to answer.
Greedoid analysis provides a starting point to explore non-compensatory (irrational) choice
heuristics. However, further research needs to be done to apply or modify this method into
tourism decision-making studies.
References
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