Characterizing Regional Tourism in Romania through Web-Scraped Data and Multivariate Statistical Analysis - Boboc, Cristina Rodica; Babaligea, Ana Maria; Ghita, Simona Ioana; Ghinea, Claudiu Nicolae; Francu, Cristian Constantin (Transformations In Business & Economics, 2026)
The study investigates the tourism profile of Romania’s counties by using automatically collected data from a major online travel platform. The analysis integrates variables related to the number of accommodation facilities, average prices, overall and category-specific ratings, number of reviews, and available amenities. To identify regional typologies and the determinant factors of tourism quality and development, cluster analysis and Principal Component Analysis (PCA) were applied. The results revealed four distinct clusters corresponding to different levels of development and attractiveness, as well as two principal components: a Tourism Offer Quality Component, reflecting visitors’ satisfaction and perceptions of services, and a Tourism Development Component, associated with the scale and intensity of tourism activity. The study provides an integrated view of regional disparities, highlighting mature tourism destinations, counties with growth potential, and emerging areas, and offers recommendations for differentiated regional development and tourism promotion policies. The innovative contribution of this research lies in the use of alternative, web-scraped data that complement and enrich official statistics and data, offering timely, detailed insights, oriented towards the actual experience of tourists. Based on the findings, the study proposes recommendations for decision-makers, aiming to stimulate growth in emerging regions, improve service quality, and support balanced regional tourism development across Romania. By integrating digital data and multivariate methods, the research makes an original contribution to understanding the competitiveness and performance of regional tourism in Romania.
Keywords: web-scraping; regional tourism; multivariate analysis; cluster analysis; principal component analysis