Cultural Landscape: Brand Knowledge Wisatawan tentang Bali dalam Online Travel Review Communication Platform

Kebudayaan Bali memiliki peran penting bagi citra dan competitiveness Bali dalam industri pariwisata global dan unsur-unsur budaya telah digunakan sebagai ikon pemasaran pariwisata. Namun demikian, dalam perjalanan kepariwisataan di Bali, motivasi wisatawan berkunjung beragam karena banyaknya alternatif aktivitas wisata ditawarkan di Bali. Di sisi lain, perkembangan teknologi komunikasi merubah trend wisatawan mempersiapkan perjalanan wisata melalui pencarian informasi dengan merujuk ke rekomendasi dari sosial media secara online, salah satu yang populer adalah travel review forum. Tulisan ini bertujuan untuk mengetahui brand knowledge wisatawan tentang Bali melalui mediasi online travel review platform. Penelitian ini merupakan penelitian kuantitatif. Pengumpulan data dilakukan melalui survey dengan kuesioner online sebagai instrument penelitian. Analisis data dilakukan menggunakan metode Spearman Rank Correlation. Hasil penelitian menunjukkan bahwa eWOM, khususnya online travel review memiliki hubungan yang kuat dan positif terhadap brand knowledge wisatawan bahwa Bali merupakan destinasi wisata alam. Sebaliknya, hubungan yang lemah dan negatif terjadi antara eWOM terhadap brand knowledge wisatawan tentang Bali sebagai destinasi wisata budaya.

Comparing online travel review platforms as destination image information agents

Online travel reviews have been extensively used as an important data source in tourism research. Typically, data for online travel review research is collected only from one platform. However, drawing definite conclusions based on single platform analyses may thus produce biases and lead to erroneous conclusions and decisions. Therefore, this research verifies whether or not there are discrepancies and commonalities between different travel review platforms. In this study, five native Chinese travel review platforms were selected: Ctrip; Qyer; Mafengwo; Tuniu; and Qunar. Using a mixed content analysis method, the destination image of Finland was extracted from 10,197 travel reviews in Simplified Chinese as the destination image is a popular topic in online review research. Results show Finland’s destination image representation varies between Chinese travel review platforms. This discrepancy is especially prominent in the dimension of functional and mixed functional-psychological destination attributes. Significant theoretical contributions and managerial implications for the analysis of online travel reviews and destination image research are discussed.

Research on Sentiment Classification of Online Travel Review Text

In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.

From Blockbuster to Neighbourhood Buster: The Effect of Films on Barcelona

In recent years, cities such as Venice, Dubrovnik, Paris and Barcelona have experienced an exponential increase in visitor numbers leading to episodes of tourismphobia by anti-tourism movements, or even the decline of the destination. Among other solutions, some destinations see film-induced tourism as a possible way of diversifying tourism supply and demand. Through the analysis of the locations of six thematic film routes in Barcelona compared to the same locations on the largest online travel review platform, TripAdvisor, it is concluded that, far from spreading out tourist flows, fiction-induced tourism in Barcelona has concentrated tourism at the main attractions of the city. Only a few exceptions of films with minor audiences lead tourists off the beaten track. Overall, this paper provides a set of recommendations, strategies and challenges for destination managers to help alleviate overtourism and to offer more sustainable tourism away from spots that attract mass tourism.

Assessing the Impact of Textual Content Concreteness on Helpfulness in Online Travel Reviews

The online travel review has become one of the most influential information sources for travelers’ decision making. This research primarily aims to examine the relationship between review textual content concreteness and review helpfulness in the tourism context. In particular, we examine how this relationship plays out under two individual circumstances, namely, temporal distance and risk–benefit tendency. Based on the construal level theory and feeling-as-information theory, two experiments are conducted to test whether the influence of review concreteness is moderated by temporal distance and risk–benefit tendency. The results show that the main effect of review concreteness is significant; however, in contrast to the assumptions of theories and results of relevant studies, both interaction effects of temporal distance and risk–benefit tendency are not. Finally, we interpret the findings and discuss the implications and limitations of this research.