This Is AuburnElectronic Theses and Dissertations

Exploring the role of online visual information in pre- and post- tourism experiences




Liu, Sijun

Type of Degree

PhD Dissertation


Nutrition, Dietetics and Hospitality Management

Restriction Status


Restriction Type


Date Available



Visual information significantly influences tourists' pre and post tourism experiences; it creates a vivid stimulation of the experiences and can provoke the tourists' imagination to better convey the favorable information. Considering the role visual information plays, it has received much attention in business and academics, both from the perception of tourists and organizations. It is an important data channel to understand tourism experiences, travel patterns, and individual tourist's preferences. The development of information technology enables researchers to comprehend massive-scale structured and unstructured online data. It provides an easy way to explore how the tourists visualize the destinations and engage with the tourism activities. The objective of this dissertation is revolved around visual information and its implication in the tourism field, along with other online displayed information, explores how they make an impact on post-tourism evaluation and pre-tourism decisions. In order to achieve this objective, this dissertation takes the form of three independent studies hoping to uncover the pictorial content relevance in the tourism field from three aspects: 1. A comprehensive systematic review of existing literature on visual information analysis in the hospitality and tourism field. 2. An experimental study on understanding pre-tourism experience decisions, via Search Engine Result Page (SERP) displays. 3. 2. A mixed-method approach aim to investigate post-experience evaluation on P2P gastronomy tourism, by factoring in web-based attributes, including web photos. The following section breaks down and designates the highlights in each study. Study 1 is a systematic literature review paper taken a text analytic approach that aims to review, analyze, and synthesize visual content analysis in tourism studies adapting big data analytic methods underlying machine learning. The literature search was conducted through two channels by keywords: Web of Science (WOS) and Scimago Journal & Country Rank (SJR) listed 124 tourism, leisure, and hospitality journals. In total, 67 papers were identified and considered after criteria filtering. To enhance the understanding of selected papers, first, a general description is provided with a distribution of research articles by journal fields and publication time. Second, a citation network was conducted to identify the connections between papers. Third, through text clustering, seven latent topics were discussed in detail in the results. The gap addressed and identified in study 1, study 2, and study 3 built on the aforementioned gaps and aims to amplify the significant role visual information plays in tourism behavior. Study 2 aims to investigate information cues displayed through Airbnb experience Search Engine Result Page (SERP) and how it influences potential tourists' decision-making. In order to expand and explain tourists' information processing and identify the mechanism of displayed information cues on Airbnb at discrete levels, an experimental design was adopted. As such, to be able to assess the effects in a valid way, this study simulates Airbnb SERP and segmented information cues based on web collected data using the principle of math quartile for price, ratings, reviews, and time spent, and thematic categories for promotional photos, and promotional cues, including scarcity message, conformity message, and credibility cues, to test tourists' selection. All the thematic categories were decided from the text mining results. In addition, based on the selection of the results, tourists share similar selection characteristics to form a category, the market segmentations are formed based on the shared characteristics in selection. The incorporation of host credibility has never been explored in the context of P2P marketplace through an experimental design, the result from conjoint analysis shows the most important attribute is the host credibility, followed by images and price during consumers decision making. Theoretically, it employed signaling theory and heuristic systematic model in explaining how tourists’ processing online information. It successfully bridges the gap by introducing promotional photos and host credibility information cues in an experimental study. Methodologically, the text mining approach was taken in information cues segmentation, which serves as a solid foundation for conjoint analysis survey design. The survey-based conjoint analysis further identified important information cues to each level and provided insights into each market segment. The findings provide substantial practical implications for marketers, web information displays, and tourists' online information filtering, processing, and selection. Study 3 selected gastronomy tourism, as a unique and growing tourism segmentation, aims to explore post-experience evaluation through Airbnb platform. This study adopted Satisfaction Disconfirmation, and the main contribution of this study is that it has taken a mixed-method approach in understanding gastronomy experiences post evaluation of the P2P platform (i.e., Airbnb). This research focuses on three areas of findings to explore post-evaluation and factors that might be influential to post-evaluation. First, this study examined 196,265 online reviews and used topic modeling to identify the major themes. The results have shown that LDA effectively identified six topics across all the reviews, which are (1) food city tour, (2) social interactions, (3) host, (4) local food culture, (5) beverage appreciation, (6) hands-on learning, ranked by topic importance. In addition to topic modeling, the study further performed sentiment analysis, aim to generate different sentiments reviews count for gastronomy experiences. Second, through Google cloud vision API, 1,331 photos were analyzed, and gained image insights through three aspects: colorfulness, face detection, object detection. Colorfulness was further integrated into OLS regression. Third, OLS regression explored the factors that have significant impacts on gastronomy experiences ratings, with a consideration of review sentiments, image colorfulness, host credibility indicator (i.e., length of experience), and neutralized review count (review count/length of experiences). Theoretically, it employed expectation disconfirmation theory, and this is one of the first studies exploring tourism experiences provided through P2P platforms and evaluating the impact of both textual data and image data on gastronomy experiences. It successfully combined big data analytics (e.g., topic modeling, sentiment analysis, image analysis) and traditional methods (i.e., OLS regression). Practically, for the hosts and service providers, the study findings provide insights into aspects that gastronomy tourists’ care more about, and factors that influence overall satisfactions of post evaluation.