Simulation of Tourism Supply Chain Planning with Digital Marketing Demand Forecasts

Tourism supply chains have become increasingly vulnerable to demand volatility amid disruption and recovery, creating a need for forecasting inputs that are both timely and operationally meaningful. This study examines whether digital marketing demand indicators can serve as empirically grounded inputs for planning-oriented analysis of tourism supply chains under disruption and recovery conditions. Using annual global data for 2005–2025, the analysis combines digital marketing, tourism demand, booking-readiness, and planning-related indicators in a time-series modeling framework. Ordinary least squares models with HC3 (Heteroskedasticity-Consistent version 3) robust standard errors and lagged specifications are used to test the relationships among digital marketing intensity, tourism demand, digital transaction readiness, and capacity- and travel-related planning outcomes across pre-disruption, disruption, and recovery periods. The results show that digital marketing demand indicators are positively associated with tourism demand intensity, online booking readiness, and digital transaction capacity, while tourism demand intensity is positively linked to travel intensity and air-passenger throughput. Lagged digital marketing indicators also significantly predict subsequent planning-related outcomes, supporting their use as forward-looking inputs for simulation-based tourism planning. Moreover, the moderation analysis indicates that this predictive relationship is significantly moderated by the disruption and recovery regimes, with the link between lagged digital marketing and capacity pressure steepening during disruption and reversing sign during recovery. Overall, the findings indicate that digital marketing demand forecasts can provide useful and empirically grounded inputs for tourism simulation and strategic planning, while underscoring the need to recalibrate these inputs across disruption and recovery regimes.