The role of seasonal trends in shaping tourist preferences for luxury resort: Big data approach - Pamungkas, Luh Made Gunapria Hindu Rajeswari; Pitanatri, Putu Diah Sastri; Adinda, Clearesta (Goodwood Publishing, 2025)
Purpose: This study aims to examine seasonal patterns in tourist preferences for luxury resort stays in Bali, with a focus on how cultural backgrounds influence accommodation choices. The goal is to help resorts better understand guest behavior and optimize occupancy strategies. Methodology/approach: The research analyzes monthly online review data from Tripadvisor for Bvlgari Resort Bali, a prominent luxury hotel. A time-series analysis using the ARIMA (Autoregressive Integrated Moving Average) model is applied to forecast occupancy trends. Prior to modeling, the data is tested for stationarity. In addition to forecasting, the study explores guest preferences by analyzing cultural characteristics inferred from reviews, categorizing them into collectivist and individualist orientations. Results/findings: Findings reveal that occupancy trends do not strictly align with the hotel’s predefined seasonal categories. Instead, they are shaped by global travel trends and cultural factors. Guests from collectivist cultures tend to prefer facilities that support group interaction and shared experiences, while those from individualist cultures prioritize privacy, exclusivity, and personalized services. The ARIMA model delivers accurate forecasting results, helping to predict future occupancy rates effectively. Conclusion: IoT integration enhances the reliability of hospital-based PV systems. Tourist behavior is not solely dictated by conventional seasons but also by cultural expectations and travel motivations. Leveraging these insights allows hotels to better align operations, marketing, and pricing strategies with actual guest preferences. Limitations: The study is limited to a single resort and uses data from one online review platform, which may not fully capture the diversity of all guests. Contribution: This study contributes to tourism analytics, cross-cultural marketing, and hotel management by offering data-driven strategies to enhance occupancy performance.