Destination Competitiveness Improvement: Insights From Causal Counterfactual AI Analysis

Abstract

Previous methods for destination competitiveness improvement have mainly focused on identifying and prioritizing competitive disadvantages of destinations. Although effective, this approach may not be optimal as it may require more change than improving combinations of other competitive disadvantages. Furthermore, these methods neglect the differing foci of travel experiences between tourist groups and have been unable to identify targeted competitiveness improvement strategies for different tourist groups. This study addresses these research gaps by developing an analytical framework that can identify targeted strategies that entail minimal changes to improve the competitiveness of destinations for different tourist groups, based on user-generated data, aspect-level sentiment analysis, and the optimization-based causal counterfactual Al algorithm. The application of the framework is demonstrated through a case study involving four destinations in Australia. The proposed analytical framework and findings are valuable in assisting destinations to improve their competitiveness in today's increasingly competitive experiential tourism market.