Information Technology & Tourism最新文献

In the tourism industry, the implementation of effective strategies to promote destinations is considered of utmost importance. Taking advantage of social media, Destination Management Organizations (DMOs) have embraced these platforms as direct channels of communication with potential visitors. However, it remains unclear to what extent these efforts work to effectively construct the desired image and influence visitors’ behavior. In order to explore this phenomenon, this study proposes a comparison of destination images within Instagram, used by both DMOs and visitors (user generated content). Thus, a deep-learning method is presented to automatically compute differences between destination images. Four destinations were selected from Mexico (two urban destinations and two beach destinations). The findings suggest that the images of urban destinations share more significant similarities, particularly in dimensions related to culture, tourist infrastructure, and natural resources when compared to beach destinations. Conversely, the images of beach destinations tend to converge on dimensions such as sun and sand, gastronomy, and entertainment, while differing in aspects related to tourist infrastructure and eco-tourism offerings. It is worth noting that these results underscore the importance of tailoring marketing strategies to the unique characteristics of each destination, taking into account the divergences and similarities in the perceptions of potential visitors.

When tourists search information online, personalization algorithms tend to contextually filter the vast amount of information and provide them with a subset of information to increase relevance and avoid overload. However, limited attention is paid to the dark side of these algorithms. An influential critique of personalization algorithms is the filter bubble effect, a hypothesis that people are isolated in their own information bubble based on their prior online activities, resulting in narrowed perspectives and fewer discovery of new experiences. An important question, therefore, is whether algorithmic filtering leads to filter bubbles. We empirically explore this question in an online tourist information search with the three-dimensional ‘cascade’ tourist decision-making model in a two-step experiment. We train two virtual agents with polarized YouTube videos and manipulate them to conduct travel information searches from both off-site and on-site geolocations in Google Search. The first three pages of search results are collected and analyzed with two mathematical metrics and follow-up content analysis. The results do not show significant differences between the two virtual agents with polarized prior training. However, when search geolocations change from off-site to on-site, 39–69% of the search results vary. Additionally, this difference varies between search terms. In summary, our data show that while algorithmic filtering is robust in retrieving relevant search results, it does not necessarily show evidence of filter bubbles. This study provides theoretical and methodological implications to guide future research on filter bubbles and contextual personalization in online tourist information searches. Marketing implications are discussed.

Consumer perceptions of helpfulness remain an open question due to the lack of semantic and spatial features of review content. This paper aims to explore three aspects of the contents of a review: time, rating, and location, to assess the helpfulness of hotel reviews. A multi-view graph convolutional network (MVGCN) and attention mechanisms that capture multimodal semantic information are designed. The experimental results on Yelp and TripAdvisor are evaluated. The findings indicate that this facilitates the filtering of helpful information and avoids information overload when reading to customers. The results show that the proposed model outperforms the baseline and illustrates the interpretability of the models in each view. Our work is essential for professionals of both hotel and travel platforms that can utilize our findings to optimize their sales systems. Also, the results can help visitors or users acquire beneficial information and avoid information overload. This study is one of the few articles that can promote a model interpretable for information overload, which aims to guide research on evaluating the helpfulness of reviews in the hotel sector. This study contributes also to the methodology by developing extracting features of multimodal data, giving a multi-view feature with several novel assessments, and a novel framework involving deep learning.

With the rapid developments in ICTs in recent years, destination management organizations (DMOs) have been increasingly expected to adopt data-driven decision-making practices towards fulfilling their role as destination managers. While data-driven decision-making offers a smarter approach to building more sustainable and competitive destinations, there remains a limited understanding surrounding its adoption in practice. Therefore, this study applied a mixed methods approach in efforts to identify the existing practices and barriers facing DMOs at each phase of Athamena and Houhamdi’s (2018) model of the data-driven decision-making process. The findings suggest that Canadian DMOs have been slow to engage in data-driven decision-making practices. Specifically, there remains a need to address the lack of data related to sustainability indicators, the quality of data sources, and the resource limitations faced by DMOs. Theoretical and practical implications are discussed.

Destination image has been a subject of great interest to tourism scholars for several decades. Since the nature of this social construct is highly dynamic, its study poses new challenges under the current conditions of contemporary tourism practices. Considering that the image formation process can be influenced positively or negatively by multiple sources of information available to individuals, it is surprising that analyses of autonomous formation agents, such as online news, have received limited attention in related literature. Although existing studies have explored the influence of this information on image formation, intention to visit, and actual behavior, these normally adopt traditional methodologies to collect information, circumscribing the analysis to limited samples. The main objective of this work is to propose an innovative automated approach based on deep learning aimed at collecting and analyzing available textual data on the internet, such as online news, to produce a more comprehensive picture of the destination image in these sources of information. In order to test this approach, a destination from the country of Mexico was selected as a case study: Cancun. Given that the USA and Canada represent almost 60 percent of all international visitors to Mexico, the information search focused on this geographical context. A total of 3845 online news making reference to Cancun were retrieved during an entire year (July 2021–2022). The analysis of this information allowed the identification of recurrent topics covered by the media in both countries regarding destination safety issues, criminal activities, and the evolution of travel restrictions due to the COVID-19 pandemic. In addition to these topics, favorable coverage could also be detected including topics such as existing amenities in all-inclusive resorts as well as the recognition of Cancun as an ideal tourist destination for the international traveler. In practical terms, we believe this information can be useful for local government and DMOs to explore the evolution of the destination’s image as well as to identify sensitive issues covered in the media that require the implementation of communication strategies to counteract any potential negative effect. Finally, the proposed approach effectively contributes to making the tasks of destination image evaluation easier and faster than traditional research strategies.