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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Radinal
Universitas Gadjah Mada
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Data-Driven Techno-Behavioral Segmentation of Post-Pandemic Tourists Using TwoStep Cluster Analysis Radinal; Sigit Priyanto; Dewanti Dewanti
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2950

Abstract

Post-pandemic tourism is characterized by increasing behavioral heterogeneity as digital technologies reshape travel planning and mobility practices, challenging traditional demographic-based segmentation. This study develops a techno-behavioral, data-driven segmentation framework within the Smart Tourism Ecosystem perspective by conceptualizing digital adoption as a mediating mechanism between socio-demographic attributes and travel behavior. Using survey data from 805 domestic tourists in Yogyakarta, Indonesia, TwoStep Cluster Analysis (log-likelihood distance; BIC-based cluster selection) identifies two distinct segments: Digital Leisure Travelers (DLT) and Budget-Conscious Digital Natives (BDN). The clustering solution demonstrates fair quality (Silhouette = 0.32). Predictor-importance and validation tests indicate that income, education, generational cohort, and digital application use are the strongest discriminators, while itinerary intensity differs significantly between clusters (p < 0.001; η² = 0.10). The findings highlight that widespread digital engagement produces differentiated mobility outcomes shaped by socio-economic capacity, emphasizing the need for segment-sensitive and inclusive smart tourism strategies.
Co-Authors Dewanti Dewanti Priyanto, Sigit