Relationship modeling in tourism shopping: A decision rules induction approach

Traditional tourism research on relationship modeling has concentrated predominantly on multivariate econometric models, univariate time-series techniques, and gravity approaches. These relationship modeling methods, although have attained a certain degree of success in the tourism paradigm, are primarily based on mathematical functions and are numeric in nature. A major drawback of these mathematical function-based modeling techniques is their inability to handle non-numeric data. This paper presents a new approach that incorporates the rough set theory to model the relations that exist among a set of mixed numeric and non-numeric tourism shopping data. The output of the rough set approach is a group of decision rules that represents the relations in a tourism shopping information system (IS). Officially published data from the Hong Kong Tourist Association for the period 1983-1996 were used to form the decision rules and test the forecasting accuracy of these decision rules. Empirical findings indicated that 94.1 per cent of the testing cases were successfully forecasted and that there was no significant difference between the forecasted values and their actual counterparts.