Evaluation and Interpretation of Tourist Satisfaction for Local Korean Festivals Using Explainable AI

Hoonseong Oh and Sangmin Lee
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Hoonseong Oh: Korea Culture & Tourism Institute, 154 Geumnanghwaro, Gangseo-gu, Seoul 07511, Korea
Sangmin Lee: School of Information Convergence, College of Software and Convergence, Kwangwoon University, Nowon-gu, Seoul 01897, Korea

Sustainability, 2021, vol. 13, issue 19, 1-18

Abstract: In this paper, we propose using explainable artificial intelligence (XAI) techniques to predict and interpret the effects of local festival components on tourist satisfaction. We use data-driven analytics, including prediction, interpretation, and utilization phases, to help festivals establish a tourism strategy. Ultimately, this study aims to identify the most significant variables in local tourism strategy and to predict tourist satisfaction. To do so, we conducted an experimental study to compare the prediction accuracy of representative predictive algorithms. We then built a surrogate model based on a game theory-based framework, known as SHapley Additive exPlanations (SHAP), to understand the prediction results and to obtain insight into how tourist satisfaction with local festivals can be improved. Tourist data were collected from local festivals in South Korea over a period of 12 years. We conclude that the proposed predictive and interpretable strategy can identify the strengths and weaknesses of each local festival, allowing festival planners and administrators to enhance their tourist satisfaction rates by addressing the identified weaknesses.

Keywords: local festivals; explainable artificial intelligence; Shapley additive explanations; tourism strategy; tourist satisfaction; light gradient boosting machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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