Identifying Unique Features of Norway Destination Image: Evidence from User-Generated Content

Abstract

Purpose: This research aims to elucidate the unique features shaping Norway's destination image by analyzing User-Generated Content (UGC) from tourists. The primary focus is on identifying key themes within the UGC that are associated with both satisfaction and dissatisfaction, thereby contributing to a comprehensive understanding of the factors influencing visitors' perceptions of Norway as a destination. Method: This research centers on TripAdvisor reviews, the prominent platform in hospitality. It specifically explores attractions in Norway by examining travel websites. The dataset, comprising 10,250 usable reviews, was processed and analyzed using Leximancer software. Findings: Our research results demonstrated nine dominant themes in descriptions of Norway travel experiences: “Norway,” “places,” “train,” “hiking,” “rental,” “expensive,” “hotel,” “northern lights,” and “restaurants.” The themes used with negative comments were “train,” “rental,” and “expensive.” The benefits of incorporating UGC in tourist research are highlighted by the excellent insights acquired. The findings also provide a visual depiction of the primary themes and concepts in visitors' narratives, allowing for a better understanding of the key features of destination image. Conclusion: Our study leverages Leximancer's software to autonomously identify key themes in tourists' user-generated content (UGC), offering valuable insights into how travelers perceive Norway's destination image. The prominence of specific themes is emphasized, minimizing researcher intervention and enriching the existing literature by highlighting predominant themes associated with satisfaction and dissatisfaction. Our content analysis reveals distinct perspectives from different tourist segments, with negative UGC linked to aspects such as pricing, transportation, and rentals, while positive UGC focuses on Norway's experiences, natural attractions, hotels, and restaurants. By providing a detailed examination of satisfaction ratings, our research contributes to destination image literature, offering clarity on service features that contribute to perceived "value for money." Moreover, our use of machine learning algorithms offers a practical roadmap for destination marketing organizations to enhance their marketing strategies by aligning them with tourist opinions on platforms like TripAdvisor, thus facilitating a more comprehensive understanding of the destination image.