Tourism intelligence and big data can improve the strategic management of tourist destinations through analysis of the environment. This study aims to explain the flight searches from London and Manchester to the Costa Blanca using variables from big data sources: online hotel satisfaction levels, flight price, hotel price, and temperature. Data for three years (2019, 2020 and 2021) are analyzed and the results are compared. The results show that online hotel satisfaction levels heterogeneously explain flight searches during these years. Hotel price positively explains searches from London. Flight price only influenced searches in 2019. Temperature in outbound destinations is the variable that best explains flight searches. This study contributes to the literature on strategic management of tourist destinations by explaining the potential tourist demand in the early decision-making stages of a trip. We highlight that the explanatory variables do not behave consistently during the years analyzed.

中文翻译：

---

利用旅游情报和大数据解释旅游目的地的航班搜索：科斯塔布兰卡案例（西班牙）


旅游情报和大数据可以通过对环境的分析来改善旅游目的地的战略管理。本研究旨在使用大数据源中的变量来解释从伦敦和曼彻斯特飞往科斯塔布兰卡的航班搜索：在线酒店满意度、航班价格、酒店价格和温度。对三年（2019年、2020年和2021年）的数据进行分析并进行结果比较。结果表明，在线酒店满意度水平对这些年的航班搜索的解释存在差异。酒店价格积极解释了伦敦的搜索量。航班价格仅影响 2019 年的搜索量。出境目的地的温度是最能解释航班搜索量的变量。这项研究通过解释旅行早期决策阶段的潜在游客需求，为旅游目的地战略管理的文献做出了贡献。我们强调，解释变量在分析的年份中表现并不一致。