Relationship modeling in tourism shopping: a decision rules induction approach

Traditional, tourism research on relationship modeling has concentrated pre dominantly on multivariate econometric models, univariate time-series techn iques, and gravity approaches. These relationship modeling methods, althoug h have attained a certain degree of success in the tourism paradigm, are pr imarily 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 th at incorporates the rough set theory to model the relations that exist amon g 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 publish ed data from the Hong Kong Tourist Association for the period 1983-1996 wer e used to form the decision rules and test the forecasting accuracy of thes e decision rules. Empirical findings indicated that 94.1 per cent of the te sting cases were successfully forecasted and that there was no significant difference between the forecasted values and their actual counterparts. (C) 2000 Elsevier Science Ltd. All rights reserved.