Enhancing destination image congruence to improve tourist satisfaction ratings: A Sentiment analysis and text mining approach on tourist textual data

Abstract

Digital innovation has reshaped the tourism industry, affecting both tourist behaviour and research methods. The study of tourism destination image remains vital, as it influences destination competitiveness. Destination images consist of projected images, shaped by marketing, and perceived images, formed through tourist experiences. A mismatch between these can negatively affect tourist satisfaction, especially as online reviews now impact long-term destination appeal and sustainability. Although research underscores the need to align projected and perceived images, there is limited focus on how this alignment affects satisfaction. Additionally, methods for assessing image congruence in digital media and strategies for improving marketing remain underdeveloped. This study seeks to investigate the impact of image congruence on tourist satisfaction and proposes marketing strategies to enhance this alignment. The study is structured around three primary objectives: (1) to assess the influence of destination image congruence on tourist satisfaction ratings; (2) to develop a novel methodological framework for evaluating this congruence; and (3) to offer strategic marketing recommendations that align the projected and perceived images to enhance tourist satisfaction. This study takes an interdisciplinary approach, combining tourism image theory, consumer behaviour, marketing, and communication to address challenges in tourism research. Focusing on the Chinese wildlife tourism market, with the Chengdu Research Base of Giant Panda Breeding as a case study, it analyses 1,214 promotional messages and 12,561 tourist reviews from online travel agencies. Topic analysis using Latent Dirichlet Allocation (LDA) identifies differences between projected and perceived images. Sentiment analysis and Importance-Performance Analysis (IPA) further examine tourist reviews, while a survey of 307 responses, analysed through descriptive statistics and Spearman rank correlation, assesses the effect of image congruence on satisfaction. Exploratory Factor Analysis (EFA) was also applied to survey data and compared with LDA results. This study's methodological contributions include a new framework for measuring image congruence and integrating diverse data sources for comprehensive analysis. The findings show that congruence between a destination's projected and perceived images significantly affects tourist satisfaction. At the Panda Base, alignment is found across four key topics, while discrepancies exist in four others. Based on this, the study recommends targeted marketing strategies, including improved crowd management, regularly updated multimedia content on pandas, immersive VR/AR experiences, clearer information on weather and timing, sustainable tourism practices, and accurate transportation details. The study also suggests reducing focus on less critical areas like general facilities and parent-child amenities. This research makes important theoretical contributions by providing a solid empirical foundation for understanding the link between image congruence and tourist satisfaction. It also advances the use of digital innovations in measuring image congruence, offering a methodology that can be applied to other destinations and enriching the academic discussion on destination image research. The study introduces the concept of adjusting projected images based on real-time tourist perceptions, creating an interactive feedback loop that challenges traditional assumptions and opens new avenues for destination marketing. By focusing on attractions like giant panda tourism, the study addresses gaps in the literature, offering valuable insights into niche wildlife tourism in China and improving the relevance of destination image research. In practical terms, this study offers useful insights for marketers and policymakers by providing concrete strategies to optimize marketing and management practices, improving image congruence, and increasing perceived tourist value. These strategies aim to boost satisfaction and support sustainable tourism, helping wildlife destinations balance conservation efforts with tourist expectations. The findings serve as a valuable reference for governments and other wildlife tourism sites for effective marketing and management in the digital age.