Key Drivers of Customer Experience at the World’s Top 10 Airports: A Big Data Analysis Through Online Reviews

Abstract

Airports have evolved from mere transportation nodes into complex service environments where passengers’ lived experience shapes perceptions, word-of-mouth, and overall travel memory. This study examines the drivers of passenger experience at the world’s top 10 airports by applying a big data approach. Semantic network analysis and CONvergence of CORrelations (CONCOR) network analysis were done on 10,516 online reviews collected from Google Map Reviews. Using the top 100 frequent words extracted from review text and the co-occurrence patterns among them. A total of 4 clusters were found. Results show that operational concepts (security, check, immigration) and service concepts (staff, friendly, helpful) are central nodes, but physical environment and amenities (terminal, lounge, food, shopping, comfortable) bridge operational and emotional clusters in the CONCOR structure. This multi-dimensional structure suggests that passenger experience must be viewed thoroughly, because although efficient processing is essential, comfort, amenity design and service interactions work together to create memorable travel experiences. This study discusses practical implications for airport managers and planners, and suggest directions for future research, including sentiment-enhanced network modelling and cross-airport comparative analyses.