Assessing the Role of Digital Readiness in Regional Tourism Growth: A Supervised Learning Approach

Abstract

This study aims to examine the effect of digital readiness on tourism sector growth at the provincial level in Indonesia during the period 2020–2024. The study employs panel data regression methods, including pooled ordinary least squares (Pooled OLS), fixed effects, and random effects, to analyze the relationship between digital readiness and tourism growth by incorporating control variables, namely unemployment and education levels. In addition, a Random Forest model is applied to assess the relative importance of each variable and to validate the consistency of the results. The findings indicate that digital readiness has a positive but statistically insignificant effect on tourism growth across all regression models. In contrast, the unemployment level has a negative and significant impact, while the education level shows a positive and significant influence on tourism growth. Results from the Random Forest model support these findings, revealing that unemployment and education level contribute more substantially than digital readiness. Furthermore, the low predictive performance of the Random Forest model suggests that tourism growth may also be influenced by other factors not captured in this study. These findings imply that the development of the tourism sector cannot rely solely on improvements in digital infrastructure. Greater emphasis should be placed on enhancing human capital and strengthening the capacity of industry stakeholders to effectively utilize digital technologies. This study contributes to the literature by integrating econometric and machine learning approaches to better understand the determinants of tourism growth in developing countries at the provincial level.