뉴스 빅데이터 기반 제주 관광수요 예측지수：관리회계 정보로서의 가능성과 유용성 검토

[Purpose] This study aims to verify the usefulness of a tourism demand prediction index developed using news big data, recognizing the limitations of traditional survey－based and historical data－driven forecasting methods in the rapidly changing tourism industry. [Methodology] This study applies the Vector Error Correction Model (VECM) to analyze the long－term and short－term equilibrium relationships between the tourism demand prediction index based on news big data and key economic variables, including the Consumer Price Index (CPI), Building Permit Area, and Tourism Service Production Index. It examines whether the prediction index is useful for explaining changes in tourism demand. [Findings] The results demonstrate that the tourism demand prediction index is effective in both short－term and long－term forecasting, exhibiting higher accuracy and responsiveness to tourism demand fluctuations compared to traditional methods. Notably, the index effectively incorporates real－time external factors, such as economic uncertainty, thereby overcoming the limitations of conventional survey－based forecasting models. [Policy Implications] This study confirms that the tourism demand prediction index can contribute to more rapid and efficient decision－making for businesses and policymakers in the tourism industry. In particular, it presents practical applicability in cost management and budgeting, pricing strategy adjustments, risk management, and supply chain optimization