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

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.

Satisfacción del turista usando factores motivacionales: comparación de modelos de aprendizaje estadístico

“…El proceso metodológico propuesto en el presente artículo está en línea con las implementaciones que se vienen realizando en el área de turismo (Oh & Lee, 2021). En cada problema se requiere probar diferentes modelos con procesos de optimización de hiperparámetros que permitan identificar la capacidad que tienen para predecir y explicar comportamientos.…”

“…Varios estudios de segmentación en el campo turístico han aplicado modelos de aprendizaje estadístico como los que se proponen en este trabajo, es decir, aplicaciones para la predicción de la satisfacción del turista (Li et al, 2009) y de los asistentes a eventos (Oh & Lee, 2021). Ahora bien, en el campo turístico, un libro reciente repasa varios algoritmos de aprendizaje estadístico aplicados en diversos escenarios predictivos (Egger, 2022).…”

Mjerenje Zadovoljstva Turističkih Vodiča Pruženim Hotelskim Uslugama

“…Dakle, zadovoljstvo korisnika predstavlja rezultat korisnikove ocjene o kvaliteti usluga, kvaliteti proizvoda i cijene (Zeithaml et al, 1996), stoga je neminovna važnost provođenja istraživanja o zadovoljstvu korisnika usluga. Dosad su provedene mnoga istraživanja o zadovoljstvu korisnika turističkih i ugostiteljskih usluga, posebice u područjima poput: menadžmenta turističke destinacije (Chi & Qu, 2008;Rajesh, 2013;Valle et al, 2006;Sangpikul, 2018), menadžmenta događaja (Rodriguez et al, 2020;Oh & Lee, 2021;Dani, 2020, Lee & Hsu, 2013Wan & Chan, 2013), restoraterstva (Yüksel & Yüksel, 2003;Al-Ababneh, 2013;Salleh et al, 2013;Cassar et al, 2020) i hotelijerstva (Shing et al, 2012;. Song et al, 2011;Amisah, 2013;Keshavarz & Ali, 2015).…”