In today’s hyperconnected world, disaster announcements—regardless of actual impact—can significantly shape consumer behaviour and brand perception in the hospitality sector. This study investigates how customers respond online to disaster-related signals, focusing on digital marketing activities by luxury hotels in Santorini, Greece. Drawing on a case study of the Santorini Earthquake in February 2025—during which the Greek government declared a state of emergency—we use big data analytics, including web traffic metrics, social media interaction and fuzzy cognitive mapping, to analyse behavioural shifts across platforms. The findings indicate that disaster signals trigger increased engagement, altered sentiment and changes in advertising efficiency. This study provides actionable recommendations for tourism destinations and hospitality brands on how to adapt digital strategies during crisis periods.

In an era where environmental consciousness increasingly shapes consumer behaviour, the tourism industry faces the dual challenge of promoting destinations while ensuring ecological sustainability. This study explores how web analytics and big data can be leveraged to enhance the visibility and attractiveness of eco-friendly destinations. Building upon digital marketing and sustainability frameworks, the authors develop a data-driven methodology that integrates website performance metrics, search behaviour patterns, and social media engagement indicators. After data collection, statistical and content analyses were implemented, followed by a Fuzzy Cognitive Map (FCM) to visualise the interrelationships between online user behaviour, environmental awareness, and destination appeal.

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.