Using tourism intelligence and big data to explain flight searches for tourist destinations: The case of the Costa Blanca (Spain)

Abstract

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.