Travel Reviews Research Articles

Explore the Characteristics of Destination Image in the New Era of Information Technology, Case of Online Travel Reviews

The follwing piece is based on the Lectio praecursoria presented at the defense of the doctoral dissertation ‘The Nature of Destination Image Concept in Online Travel Reviews’, which was publicly discussed on March 4, 2022, at the University of Eastern Finland. The full version of the dissertation can be found at: http://urn.fi/URN:ISBN:978-952-61-4483-2

The role of online travel reviews in evolving tourists’ perceived destination image

ABSTRACT Online travel reviews play a vital role in the formation of a destination image. However, the mechanism of how that happens is still relatively unknown. Drawing on the elaboration likelihood model and the cognitive–affective-conative model of destination image, this study aims to explore the impact of specific travel review attributes on cognitive, affective, and conative images through the central route and the peripheral route of persuasion. An experiment design survey with four scenarios (i.e. concrete vs. abstract × 5-star rating vs. 1-star rating) was applied in this study. A total of 1,305 Chinese participants were involved in this experiment, and compared their perception of Santa Claus Village, Finland, before and after reading different scenarios’ travel reviews. The results reveal that high rating reviews change cognitive image much more than low rating reviews. Low rating reviews have a large impact on the affective image. Under the high rating scenario, the concrete travel review improves the destination image more than the abstract travel review. Under the low rating scenario, the deterioration of abstract travel review on destination image is larger than concrete travel review. Moreover, both the central and peripheral cues of travel reviews affect tourist destination image formation.

Analysing online travel reviews to identify temporal changes of a destination image

The goal of this study is to examine how destination image and destination sentiment change over time using content from online travel reviews. We explore the evolution of Finland’s destination image from 2013 to 2018 by analysing 10,197 unstructured Chinese travel reviews. With a mixed analysis approach, we identify the evolutions of the perception of destination attributes, destination structure, and destination sentiment at functional, the mixed functional-psychological, and the psychological levels. The results show that the perceptions of destination attributes can evolve and change surprisingly rapidly, even in the case of a country-level destination. Although the structure of destination image seems to be stable, individual dimensions may evolve rapidly. In terms of destination sentiment, the evolving trends of destination sentiment can be even in the opposite direction of the evolution of destination attributes. These findings improve the research on the dynamic characteristics of destination image and provide industry insights for destination marketers in formulating long-term marketing strategies and monitoring destination image development.

Sentiment Analysis of Tourist Scenic Spots Internet Comments Based on LSTM

With the win-win development of tourism and the Internet, word-of-mouth ranking of tourist attractions is a valuable reference factor. We try to find a correlation between tourist reviews and taste ranking of tourist attractions. We study the sentiment features of tourist online reviews from the technical perspective of natural language processing, so we propose an improved long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We abandon traditional dictionaries and machine learning methods. A deep neural network approach was chosen to decompose multisentiment travel reviews into different morpheme levels for classification. Then, through preprocessing, text sentiment topic detection, and sentiment classification network, an accurate grasp of the sentiment features of reviews is finally achieved. To test the performance of our method, we built a web review database by crawler for experimental validation. Experimental results show that our method maintains more than 90% accuracy in comment sentiment detection, significantly outperforming dictionary methods and machine learning methods.

A Combination of Sentiment Analysis Systems for the Study of Online Travel Reviews: Many Heads are Better than One

This study presents an analysis of the Rest-Mex forum task 2021, which is the first international evaluation event using tourism-related (Online Travels Reviews - OTRs) data from Mexico. In that forum, 14 specialized sentiment analysis systems were presented. The main contribution of this research is a method to successfully combine those 14 systems specialized on sentiment analysis systems for OTRs. The outputs of those 14 systems were used to evaluate the proposed combination schemes. The systems were trained and tested with 7,413 OTRs from the city of Guanajuato, Mexico, a well-known cultural destination. All of them were collected from TripAdvisor. We propose three schemes to combine the systems to predict the polarity of OTRs efficiently. The combination based on deep learning improves significantly each of the results obtained in the sentiment analysis systems at the individual level. Also, the results were improved for 4 out of the 5 polarity classes in the collection. To the best of our knowledge, this is the first paper that reports results from the combination of different specialized systems in sentiment analysis for OTRs.

ONLINE REVIEWS AND THEIR PERIPHERAL CUES: A CROSS-PLATFORM COMPARISON OF USEFUL REVIEWS ABOUT TOURISM AND HOSPITALITY

Purpose: This study explores the review structure of the Online Reviews Sites & Online Travel Agencies and examines the differences among the websites in terms of online travel reviews (OTRs) and their peripheral cues. This research also explores review usefulness across these platforms. Methodology: The dataset consisted of 4895 online travel reviews about five tourism products available on four platforms. The analysis was done using SPSS 28.0 through cross-tabulation, Chi-Square, and ANOVA. Findings: The results indicated a considerable difference in the review structure across the four platforms. The users of these websites adopted different approaches while revealing their identities online. However, similarities were found in the case of review type, sidedness, and rating. On all websites, the Utilitarian and Regular reviews were the highest in numbers, and the maximum of tourists gave four and five ratings to tourism products. Moreover, in terms of useful OTRs, the platforms had significant differences. Practical Implications: The results are substantial for the tourism products and service providers (the OTAs) and the review sites. Further, managerial implications are provided to assist the online platform's administrators in making the review sections of websites more user-friendly and effective for potential tourists. The study is concluded along with the future directions of research

What Factors Make Online Travel Reviews Credible? The Consumers’ Credibility Perception-CONCEPT Model

Online reviews have become a fundamental element in searching for and buying a tourism service. In particular, in the phase of post-pandemic caused by the COVID-19, social media are important channels of inspiration of dreams and encouragement to begin envisioning future trips. However, the growing trend of fake reviews is becoming a big issue for consumers. This study proposes and empirically validates a new model that enables predicting consumers’ Credibility Perception of Online Reviews (CPOR) related to tourism, considering all integrated factors of the communication process. A survey was carried out via a structured questionnaire. In particular, 615 answers from Italian travel groups were collected, and correlation and regression analyses were conducted. Results show that the website brand, advisor’s expertise, reviews’ sidedness and consistency, and consumer experience are significant predictors of CPOR. Website usability and reputation are instead weak predictors. This study provides the design and test of a credibility model, contributing to the theoretical and empirical advancement of the literature and enhancing the knowledge on consumer behavior.

Big Data Recommendation Research Based on Travel Consumer Sentiment Analysis.

More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine (SVM) algorithm based on travel consumer sentiment analysis and builds an Hadoop Distributed File System (HDFS) system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification.

Gastronomy Tourism and Well-Being: Evidence from Taiwan and Catalonia Michelin-Starred Restaurants.

In the tourism and hospitality industry, ensuring the well-being of visitors is essential to achieving a competitive tourist destination. This objective is even more pressing in the gastronomy sector. Surprisingly, the scientific literature on this topic is scarce and relies on questionnaire surveys and interviews as a data source. After scrutinizing the 13 articles on gastronomy tourism and well-being indexed in the Web of Science or in Scopus, this study proposes two new lines of research interrelated by the concept of gastronomic image. These exploit the content shared online by consumers in order to assess subjective well-being derived from quality gastronomic experiences. The first is a framework for the customer-perceived image based on Grönroos’s service quality model, and the second is a conceptual model based on Morris’s semiotics to measure gastronomic image. Through mixed methodologies, i.e., qualitative in the first research line and quantitative in the second, the study applies the theoretical framework to Michelin-starred restaurants in two tourist regions with similar features but with different gastronomic cultures—Taiwan (Asia) and Catalonia (Europe)—using as a data source all the online travel reviews (OTRs) written in English about these restaurants shared on the TripAdvisor portal. Comparing the three categories of restaurants in both regions, the results show branding and marketing problems and significant differences in the popularity of restaurants and the satisfaction and well-being of diners. There is a positive relationship between the category of restaurants according to the number of Michelin stars and their popularity according to the number of OTRs, as well as with the satisfaction and well-being of diners, except for a 3-star restaurant that is the worst-rated. These outcomes from the demand side can be useful to stakeholders to design or improve gastronomic products and services.

Analysis of customer sentiments on review sites toward selected travel companies: A big data approach

In India, the travel industry is expanding quickly, and more people are using technology to make online transactions easier. This has caused technology and trip planning to become increasingly entwined. According to studies conducted all around the world, contemporary travellers need interactive features, superior travel assistance, product details and pertinent details to help them make decisions. Because travellers evaluate their experiences based on the impression they have of a place after their journey, online travel reviews are also a good predictor of a location’s post-visit destination image (DI) (González-Rodrguez et al. 2016; Park et al. 2007). The purpose of this study is to find out customers’ experience using travel review sites that impact the travel industry. The advancements in technology have had a significant impact on the travel and tourism sector, as they have on many other service-based businesses. This research paper also examines Indian clients’ preference, usage and satisfaction level with online review sites. In this study, Airbnb, Goibibo, Oyo, Yatra, MakeMyTrip and Cleartrip customer reviews are analyzed using Wordcloud and sentiment analysis, and therefore, conclusions are made based on that. The study concludes that ill-defined situations caused by travel firms are impacting customer satisfaction levels. However, it is concluded that consumers place less satisfaction on travel sites due to various reasons.

Social Network Analysis on Tourists’ Perceived Image of Tropical Forest Park: Implications for Niche Tourism

Tourists’ perceived image is the core of destination marketing. As an important niche tourist destination, the analysis of tourists’ perceived image of tropical forest parks has great value. This study takes the Yalong Bay Tropical Paradise Forest Park as a case site and collects a total of 1,44,022 words from online travel reviews on Ctrip.com via the Python web crawler technology. Firstly, through high-frequency word analysis, we identified 77 core elements and a total of five image themes, which are attraction image, emotional image, service facility image, crowd image, and activity image. Secondly, building on Net Draw analysis, a network structure diagram of the tourist’ perceived image of the Yalong Bay Tropical Paradise Forest Park is constructed. Finally, the overall network and individual network of tourists’ perceived image are analyzed. The results indicate that low overall network density is in possession of core and periphery. Guojianglong Cable Bridge, battery car, and glass path enjoy both high degree centrality and betweenness centrality. They also show significant advantages of structural holes. Therefore, they are on the top of the network. The academic and practical value of tourism image projection and development in tropical forest park is discussed.

Antecedents and Impacts of the Pilgrimage Destination Personality in the Tibetan Region: Case from Larong Wuming Buddhist Academy

The present research aims to explore the perceived destination personality of the Larong Wuming Buddhist Academy, a site that is a pilgrimage destination in the Tibetan region of China, and to examine the influence of involvement as an antecedent on the destination personality. This study further investigates the direct and indirect impact of a destination personality on loyalty. A total of 2454 online travel reviews and a convenience sample of 348 usable surveys were analyzed. The personalities of the pilgrimage destination in three specific dimensions were revealed, namely, the religious, agreeable, and ethereal dimensions. The findings verified the positive effect of tourist involvement on the pilgrimage destination personality. Furthermore, in the context of a pilgrimage, the mediating role of self-congruity and satisfaction between the destination personality and loyalty has been confirmed.

Value Co-creation in Non-profit Accommodation Platforms.

Value co-creation, in the sharing accommodation sector, has been extensively analyzed but mainly with Airbnb as a reference and focusing mostly on guests’ perceptions. The aim of this study is to analyze the value co-created for users (guests and hosts) in the non-profit sharing accommodation platforms Couchsurfing and HomeExchange. This study also aims to analyze whether the co-created value of these platforms differs from that of for-profit platforms, along with how the outcomes, resources, and practices of the value co-creation process can help create wellbeing for individuals involved in the accommodation experience. Given that most of the existing literature on value co-creation in sharing accommodation platforms is based on Airbnb and guest perspectives, this study is a pioneer in analyzing how guests and hosts co-create value in the context of non-profit accommodation platforms using online travel reviews (OTRs) from non-profit platforms, and how the co-created value contributes to the wellbeing of the individuals involved. Results show that a set of tangible and intangible resources, such as the home and its amenities, helps users on non-profit platforms co-create value and that interaction and social practices between guests and hosts help co-create value for both groups. This implies that non-profit accommodation platforms may contribute more to the social dimensions of wellbeing of their users than for-profit platforms such as Airbnb where the host is typically absent from the experience. In addition, this study demonstrates that the co-created value in non-profit platforms depends on the modus operandi of each platform. On Couchsurfing, guests and hosts co-create more value from their social practices, and on HomeExchange, value co-creation depends more on tangible and intangible resources.

Use and Impact of Online Travel Reviews for Planning Free and Easy Holidays

Online travel reviews (OTRs) are used by travelers to plan and book their holiday particular by the free and easy traveler. Hence, it is essential for hoteliers to understand the factors that affect and empower travelers when using OTRs to plan their holidays. This study has adopted three theories: dual-process theory, psychological empowerment (PE) and TPB. These theories were used to test additional dimensions of the informational and normative social influence, its links with perceived empowerment, and their impact on the intention to use OTRs. Using SmartPLS to analyze the data collected from 392 Malaysian, the results show that PE is significantly affected by informational and normative social influence, from travelers’ perception of PE positively affects their attitude toward using OTRs, and attitude significantly influences intention to use OTRs when plans for travel. Furthermore, the research findings contribute to the literature on travelers’ behavioral intention; this study also has direct implications for online travel providers.

Studying Online Travel Reviews related to tourist attractions using NLP methods: the case of Guanajuato, Mexico

ABSTRACT This research's main objective is to analyse Online Travel Reviews (OTRs) related to tourist attractions aiming at identifying recurring discussion topics/themes to infer whether travellers make reference to positive or negative experiences during their trips. This study focuses on the case of Guanajuato, a Mexican cultural destination. It is important to mention that similar analyses have not been carried out to study any Mexican destination so far. OTRs are analysed using two Natural Language Processing approaches: Mutual Information Ranking and Jaccard Coefficient. These are applied to quantify and extract the most representative themes and the main topics from each polarity within the OTRs. These techniques have been extensively studied in various areas, however, to the best of our knowledge, they have not been applied in digital tourism to detect essential issues during the travel experience. As a result, two recurrent negative themes/topics (‘cleanliness’, ‘prices’) were identified throughout this analysis. One surprising finding of this work relates to the lack of variation between national and international travellers' evaluations. The findings of this study contribute to confirming the role of OTRs within the contemporary tourist experience by identifying those places perceived negatively or positively directy from the visitors' perspective.

Impact of Electronic Word of Mouth Communication and Destination Image on Behavioural Intentions: The Case of Eskişehir Turkey

Electronic word of mouth is an important concept in the tourism industry since sharing information and experiences on various online platforms can affect tourists’ perceived destination image and behavioural intentions. The aim of this study is to investigate the relationship between electronic word of mouth, destination image and behavioural intentions in the context of Eskişehir case. The sampling technique used was non-probability sampling with both purposive sampling and convenience sampling. In this context, the created questionnaire form; in various digital platforms; was presented to the people who visited Eskişehir in the last 3 years. Data were collected from 392 domestic tourists between December 2020-January 2021. The socio-demographic data obtained from the participants was interpreted by calculating the frequency, percentage, mean, and standard deviation values from descriptive statistics. The top three digital platforms where the participants read/watch the most travel reviews on the internet are respectively; Instagram, Tripadvisor, and various personal travel blogs. Regression analysis with the help of SPSS program was used to test the hypotheses in the model created for the relationship between variables. The findings of the study show that eWOM has positive and significant results on both destination imageand behavioural intentions. Also, the destination imagehas a positive and significant influence on future behavioural intentions.

Sequence effects of city tour experiences: A tourism fatigue perspective

City tours provide travelers with various experiences by means of a series of attractions, but little attention is paid to the effects that the sequence in which travelers visit these attractions have on their tourism evaluation. The paper aims to investigate the sequence effects of experiences along the experience economy model's two axes. This study analyzed 33,350 records that 7855 travelers on a city tour of Madrid posted of about 525 attractions on TripAdvisor. The results reveal that travelers value a sightseeing experience less if a previous attraction offered the same type of experience. Furthermore, active-participation attractions are more highly rated when visited after passive-participation ones. Similarly, travelers rate attractions providing absorption more positively after they have visited immersion ones. The findings are explained by using various tourism fatigue dimensions: affective fatigue, motivational fatigue, and cognitive fatigue. The study shows that sequence effects play an important role in helping to prevent tourism fatigue and increase tourism satisfaction, both of which have practical implications for tourism marketers' planning strategy. • Marketers' focus should shift from promoting destinations to creating experiences. • The work identifies psychological sequence effects of tourist experiences. • Insights base on two complementary theories: experience economy & fatigue research. • Big data with 33,350 traveler reviews on 525 attractions confirm sequence effects. • Researcher can design trip sequences to prevent fatigue and increase satisfaction.

Effects of Visual Information and Argument Concreteness on Purchase Intention of Consumers Towards Online Hotel Booking

Online consumer reviews impact the traveller’s decision to book hotel online. The study examines the role of visual information and argument concreteness on the credibility of reviews and consequently on purchase intention. The study examines the effect of trust and perceived risk as mediators on the relationship of review credibility and purchase intention. An online survey was conducted on 310 travellers above 18 years. The data was analysed using PLS-SEM. Travellers believed visual information and argument concreteness of review enhances the review credibility. Traveller’s purchase intentions were influenced by the credibility of reviews. This relationship was partially mediated by trust but not by perceived risk. The research suggests that the elaborate travel reviews with visual elements that describe the location and surroundings of the hotel are considered to be more credible and develop a positive intention towards booking the hotel.

Gastronomic experience (co)creation: evidence from Taiwan and Catalonia

ABSTRACT The design of gastronomic experiences based on consumer opinion (co-creation) can contribute to improving the online gastronomic image and the overall image of destinations. By evaluating and sharing their experience on social media, the visitor expresses different levels of satisfaction and loyalty towards the destination. This user-generated content (UGC) represents useful information for analysing the perception of gastronomic experiences and the formation of online gastronomic images. The semiotic model used here considers three semantic aspects of the gastronomic image to analyse gastronomic experiences through big data analytics. The model was applied to two regions with different gastronomic cultures: Taiwan (Asia) and Catalonia (Europe) and four types of experiences. A total of 17,214 online travel reviews (OTRs) from 2008 to 2019 were selected from TripAdvisor’s ‘things to do’ section, which includes various tours and gastronomic activities that are different from typical dining experiences in restaurants. The results highlight the growth of OTRs before the COVID-19 pandemic, the high satisfaction of the participants in the reviewed activities, and differences in popularity and attractiveness according to activity, category and region. The findings also showcase notable differences in destination branding and marketing strategies between the regions.

Online travel review posting intentions: a social exchange theory perspective

ABSTRACT The present study investigates various costs and benefits associated with individuals’ intentions to post online travel reviews and to understand this, the study applies social exchange theory as the base model. The results revealed that altruism, reputation, economic rewards and venting negative feelings positively influence review posting intentions while cognitive and executional costs negatively influence review posting intentions. Further, Innovativeness moderates the relationship between altruism and behavioural intentions and venting negative feelings and behavioural intentions. The findings indicate that cognitive and executional costs reduce the intentions of travelers to post reviews online. Therefore, service providers must focus on user-friendly technology to reduce these costs.