Data-Driven Techno-Behavioral Segmentation of Post-Pandemic Tourists Using TwoStep Cluster Analysis
DOI:
https://doi.org/10.26877/asset.v8i2.2950Keywords:
Digital adoption, Smart tourism ecosystem, techno-behavioral segmentation, TwoStep Cluster AnalysisAbstract
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.
References
[1] D. R. Toubes, N. Araújo Vila, and J. A. Fraiz Brea, “Changes in Consumption Patterns and Tourist Promotion after the COVID-19 Pandemic,” JTAER, vol. 16, no. 5, pp. 1332–1352, Apr. 2021, doi: 10.3390/jtaer16050075.
[2] I. Terziyska and E. Dogramadjieva, “One year later: shifts and endurances in travel intentions of Bulgarian residents in the time of pandemic,” EJTR, vol. 32, p. 3220, Aug. 2022, doi: 10.54055/ejtr.v32i.2704.
[3] S. Gössling, D. Scott, and C. M. Hall, “Pandemics, tourism and global change: a rapid assessment of COVID-19,” Journal of Sustainable Tourism, vol. 29, no. 1, pp. 1–20, Jan. 2021, doi: 10.1080/09669582.2020.1758708.
[4] S. Dolnicar, “Designing for more environmentally friendly tourism,” Annals of Tourism Research, vol. 84, p. 102933, Sep. 2020, doi: 10.1016/j.annals.2020.102933.
[5] I. Kiseleva, S. Iskajyan, A. Grigoryan, V. Grigoryan, D. Badalyan, and M. Abramyan, “Analyzing The Environmental And Social Implications Of Digital Travel Behavior: The Rise Of The Smart Tourist,” Int. J. of Ecosyst. and Ecology Sci., vol. 15, no. 6, pp. 61–66, 2025, doi: 10.31407/ijees15.608.
[6] D. Buhalis and A. Amaranggana, “Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services,” in Information and Communication Technologies in Tourism 2015, I. Tussyadiah and A. Inversini, Eds., Cham: Springer International Publishing, 2015, pp. 377–389. doi: 10.1007/978-3-319-14343-928.
[7] U. Gretzel, M. Sigala, Z. Xiang, and C. Koo, “Smart tourism: foundations and developments,” Electron Markets, vol. 25, no. 3, pp. 179–188, Sep. 2015, doi: 10.1007/s12525-015-0196-8.
[8] K. H. Bhuiyan et al., “Smart Tourism Ecosystem: A New Dimension toward Sustainable Value Co-Creation,” Sustainability, vol. 14, no. 22, p. 15043, Nov. 2022, doi: 10.3390/su142215043.
[9] S. Paiva, M. Ahad, G. Tripathi, N. Feroz, and G. Casalino, “Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges,” Sensors, vol. 21, no. 6, p. 2143, Mar. 2021, doi: 10.3390/s21062143.
[10] M. Šoltésová, B. Iannaccone, Ľ. Štrba, and C. Sidor, “Application of GIS Technologies in Tourism Planning and Sustainable Development: A Case Study of Gelnica,” IJGI, vol. 14, no. 3, p. 120, Mar. 2025, doi: 10.3390/ijgi14030120.
[11] F. Fraboni, G. Prati, G. Casu, M. De Angelis, and L. Pietrantoni, “A cluster analysis of cyclists in Europe: common patterns, behaviours, and attitudes,” Transportation, vol. 49, no. 2, pp. 591–620, Apr. 2022, doi: 10.1007/s11116-021-10187-3.
[12] A. Batzios, V. Samathrakis, A. Theodoridis, G. Koutouzidou, and A. Kakouris, “Identifying Veterinary Students’ Attitudes on Entrepreneurial Intentions: A Two-Step Cluster Analysis,” in The 17th International Conference of the Hellenic Association of Agricultural Economists, MDPI, Jan. 2024, p. 21. doi: 10.3390/proceedings2024094021.
[13] “Hair et al. - 2019 - Multivariate data analysis.pdf.”
[14] A. Supandi, A. Saefuddin, and I. D. Sulvianti, “Two step Cluster Application to Classify Villages in Kabupaten Madiun Based on Village Potential Data,” XPlore, vol. 10, no. 1, pp. 12–26, Dec. 2020, doi: 10.29244/xplore.v10i1.272.
[15] V. Harantová, J. Mazanec, V. Štefancová, J. Mašek, and H. B. Foltýnová, “Two-Step Cluster Analysis of Passenger Mobility Segmentation during the COVID-19 Pandemic,” Mathematics, vol. 11, no. 3, p. 583, Jan. 2023, doi: 10.3390/math11030583.
[16] F. Vanclay, J. T. Baines, and C. N. Taylor, “Principles for ethical research involving humans: ethical professional practice in impact assessment Part I,” Impact Assessment and Project Appraisal, vol. 31, no. 4, pp. 243–253, Dec. 2013, doi: 10.1080/14615517.2013.850307.
[17] M. Mwale, R. Luke, and N. Pisa, “Factors that affect travel behaviour in developing cities: A methodological review,” Transportation Research Interdisciplinary Perspectives, vol. 16, p. 100683, Dec. 2022, doi: 10.1016/j.trip.2022.100683.
[18] M. Firdausi, E. Ahyudanari, and W. Herijanto, “Study on the analysis of travel behavior: A review,” E3S Web Conf., vol. 434, p. 02022, 2023, doi: 10.1051/e3sconf/202343402022.
[19] S. Djebali, Q. Gabot, and G. Guérard, “Tourists Profiling by Interest Analysis,” in Advanced Data Mining and Applications, B. Li, L. Yue, J. Jiang, W. Chen, X. Li, G. Long, F. Fang, and H. Yu, Eds., Cham: Springer International Publishing, 2022, pp. 42–53. doi: 10.1007/978-3-030-95408-64.
[20] B. Mckercher and G. Lau, “Movement Patterns of Tourists within a Destination,” Tourism Geographies, vol. 10, no. 3, pp. 355–374, Jul. 2008, doi: 10.1080/14616680802236352.
[21] F. K. Teklehaymanot, M. Muma, and A. M. Zoubir, “Bayesian Cluster Enumeration Criterion for Unsupervised Learning,” IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5392–5406, Oct. 2018, doi: 10.1109/TSP.2018.2866385.
[22] M. Sigala, “Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research,” Journal of Business Research, vol. 117, pp. 312–321, Sep. 2020, doi: 10.1016/j.jbusres.2020.06.015.
[23] L. Florido-Benítez and B. Del Alcázar Martínez, “How Artificial Intelligence (AI) Is Powering New Tourism Marketing and the Future Agenda for Smart Tourist Destinations,” Electronics, vol. 13, no. 21, p. 4151, Oct. 2024, doi: 10.3390/electronics13214151.
[24] D. Setiawan, S. Priyanto, and M. R. F. Amrozi, “Integration of Public Transport Systems for Enhanced Passenger Mobility: A Systematic Review,” IJTDI, vol. 8, no. 1, pp. 109–118, Mar. 2024, doi: 10.18280/ijtdi.080110.
[25] N. B. Taylor, “Comparative analysis of transport modes for regional mobility: a case study,” Proceedings of the Institution of Civil Engineers - Transport, vol. 175, no. 2, pp. 87–96, Feb. 2019, doi: 10.1680/jtran.18.00150.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Advance Sustainable Science Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
