Experience-based Destination Competitiveness: Exploration through Big Data and AI Methods

Abstract

Tourism is a major economic driver for many tourist destinations and a key contributor to sustainable development goals such as no poverty and zero hunger. As a result, destination competitiveness, which influences tourism development, has become a prominent focus in tourism research. However, despite extensive research, understanding of the destination competitiveness concept and its evaluation and improvement methods remains limited because of the complexity of the concept and the evolving nature of the tourism market. Furthermore, with the advent of the big data era, various forms of tourism big data (e.g. user-generated data), combined with advanced artificial intelligence (AI) methods, have proven valuable in advancing many fields of tourism research. However, little research has explored the integration of these new data sources and methods into destination competitiveness research. This thesis addresses these problems by advancing the definition, evaluation, and improvement methods of destination competitiveness and complementing related research methodologies through big data and AI-based methods. Three studies have been conducted to achieve this objective. Study 1 identifies theoretical lenses and research gaps for advancing the definition, evaluation and improvement of the destination competitiveness concept by comprehensively reviewing the literature through the topic modelling-based computational literature review. Study 2 addresses the research gap regarding experience-based destination competitiveness evaluation by applying touchpoint theory and aspect-based sentiment analysis. The study develops an analytical framework (i.e. method) to evaluate destination competitiveness based on tourists' satisfaction with key touchpoints (i.e. interactions at the destination) that are important to their travel experience. Study 3 addresses the research gap regarding the identification of optimal and targeted strategies to improve destination competitiveness. It develops an analytical framework that integrates the causal counterfactual AI algorithm, k-means clustering and the evaluation method from Study 2 to guide destinations in formulating competitiveness improvement strategies. Methodologically, this thesis extends destination competitiveness research with big data and AI-based methodology, which offers several advantages in data collection, analytical power, and insights generation. It also contributes to tourism and management research methods by introducing the computational literature review method for analysing large volumes of literature to identify future research directions. Additionally, it introduces prescriptive AI methods capable of capturing causality to support decision making, a capability not found in conventional descriptive and predictive AI methods. Theoretically, this thesis contributes to the literature by defining experience-based destination competitiveness, providing a complementary approach for destination competitiveness evaluation in today's experience-based tourism market. It also introduces a minimal change view of strategy development, which facilitates further exploration of destination competitiveness improvement through optimisation-based theories. Additionally, it extends destination competitiveness research from the country/regional level to the individual/business level, creating opportunities to theorise destination competitiveness from multiple levels. Practically, this thesis highlights the importance of monitoring tourist satisfaction at key touchpoints because it reveals competitive advantages and disadvantages. It also emphasises the need to consider necessary changes, potential outcomes, and differences among tourist groups when formulating competitiveness improvement strategies. This thesis demonstrates the value of user-generated data and provides prototype tools that can be adapted to evaluate destination competitiveness and develop data-driven improvement strategies. Show more