First-time versus repeat tourists: A multi-destination data mining comparison of actual behavior

Abstract

Given that the intention to return is not a valid proxy, understanding tourists’ actual repeat behavior is crucial. This study aims to systematically compare the actual behavior of first-time and repeat tourists across multiple destinations. This research employed a large-scale dataset of user-generated content collected through big-data mining (N = 8,059) and conducted t-tests and chi-square tests across different types of tourist destinations (N = 298). The findings indicate that first-time tourists exhibit significantly longer stays, higher per capita expenditure, and, counterintuitively, broader prior travel experience than repeat tourists. These two groups differ significantly across most categories of travel preferences. Statistically significant differences are evident in their choice of travel companions. This study provides valuable insights into the systematic comparison of first-time and repeat tourists’ actual behavior across multiple destinations. Rather than relying on survey-based revisit intention as a proxy for actual tourist behavior, this study analyzes large-scale user-generated content (UGC) from 298 destinations to compare the actual behaviors of first-time and repeat tourists. It uncovers the “travel breadth–loyalty” paradox and delivers actionable resource-allocation recommendations for destination management organizations (DMOs). These findings suggest that, for DMOs, broad customer-acquisition strategies aimed at first-time tourists may yield higher returns than overinvesting in repeat-visit promotions, while repeat tourists can be targeted through companion-oriented and relationship-based products.