Understanding and Managing Behavioral Preferences of Tourists and Local Residents: An Empirical Study on Ice-and-Snow Tourism in Northeast China

Abstract

The soaring popularity of Ice-and-Snow Tourism (IST) and the growing number of tourists pose challenges for destination managers, particularly in balancing attraction development with the maintenance of high-quality tourism experiences. Understanding tourist behavioral preferences is crucial for the effective utilization of ice-and-snow resources and targeted marketing strategies. However, differences in IST participants" behavioral preferences and influencing factors remain unclear. Taking northeast China as an example, this paper applies text clustering and image recognition techniques to analyze 43,063 pieces of user-generated content (UGC) related to IST. Notable disparities are identified in destination image perception as well as in the temporal and spatial distribution patterns of IST participants, including both tourists and local residents. These findings reveal potential links between the variations and general regional attributes in 41 cities. Overall, tourists tend to develop diverse travel patterns but show similar preferences during specific holiday periods. They tend to concentrate in popular destinations, with stronger perception of distinctive architectural and natural landmarks. In contrast, local residents, with little travel time, exhibit a more dispersed travel pattern, favoring short trips to lesser-known surrounding attractions. In addition, unexpected insights emerge from the coupled analysis of spatiotemporal characteristics related to the destination image. Among 16 regional attributes, most socio-economic factors are positively correlated with tourist distribution, whereas physical geographic factors show relatively weak correlations. Although tourists show a greater preference for well-developed tourism infrastructure, diverse experience offerings, and enhanced safety, they also have a greater appreciation for natural environments than local residents. These findings offer valuable insights for more human-centered IST planning, supporting the optimization of existing attractions to enhance their competitiveness and sustainability.