Search Results (23)

Understanding and promoting guest satisfaction is central to the economic sustainability of the hospitality industry. Satisfaction influences consumers’ booking intentions, hotel choice, loyalty, and the reputation and performance of accommodation establishments. Thus, accurate decision making by hotel managers relies on trustworthy and easily accessible information on the variables that affect guest satisfaction. Nowadays, this information is available through reviews and ratings provided by online platforms, such as TripAdvisor. Indeed, much research into guest satisfaction uses TripAdvisor reviews. However, this study aims to analyse guest satisfaction using only TripAdvisor ratings. These ratings can be more succinct and tractable indicators than reviews. A sample of 118 hotels in Cape Verde and the Azores, two archipelagos belonging to Macaronesia, and a descriptive, predictive, and discriminant methodology are employed for this purpose. Four main results are obtained. First, the rated items on TripAdvisor are consistent with the scientific literature on this topic. Second, TripAdvisor ratings are valid and reliable. Third, TripAdvisor ratings can predict guest satisfaction based on the perceived quality of hotel services. Fourth, there are significant differences in ratings depending on the tourism destination chosen. These results are of interest to researchers, tourists, as well as hotel, destination, and platform managers. Full article

This study examined the trends and key themes of artificial intelligence in the field of tourism and hospitality research. On 5 March 2025, a search was performed using “artificial intelligence” and related terms in the “Title, Abstract, and Keywords”, focusing on tourism and hospitality journals indexed in Scopus. The identified documents were subjected to performance analysis and science mapping techniques. The search yielded 921 documents, comprising 882 articles and 39 reviews. The number of documents increased from 3 in 1987 to 277 in 2024. R. Law from the University of Macau was the most prolific author, while the Hong Kong Polytechnic University recorded the highest publication count. Chinese researchers produced the most documents, totaling 262 articles and reviews. A keyword co-occurrence analysis revealed four key themes: “machine learning and sentiment analysis of online reviews”, “adoption of AI including robots and ChatGPT in the hospitality industry”, “artificial neural networks for tourism management and demand analysis”, and “random forest models in travel”. Additionally, the study noted a shift in research focus from tourism demand forecasting and sentiment analysis to using service bots and applying artificial intelligence to enhance service quality, with a recent emphasis on generative AI tools like ChatGPT. Full article

During the COVID-19 pandemic, the tourism sector encountered multiple challenges. Numerous governments chose to lock down their cities and countries. Despite this, many companies found their online businesses making the greatest leaps in their portfolios, and social media platforms became one of the most valuable sources of information for purchase decisions. There have been numerous studies on the effects of social media reviews—a form of electronic word-of-mouth (eWOM)—on consumer behavior. Few were found to be related to their impact on group package tours (GPTs) while considering mixed eWOM, that is, both the positive and negative forms present in word-of-mouth communication. As the tourism sector gradually revives, the need to further explore how tourism and hospitality service providers can adapt to changes in post-pandemic consumer behavior has become imperative. The influence of social media reviews on consumers’ value perceptions of a GPT to Japan, allowing for the influence of the marketing mix element of advertised price, was examined through online experiments in this study. Positive, negative, and mixed eWOM were examined. It was found that eWOM was more influential on consumers’ value perceptions than the advertised price for all price acceptability levels. Mixed eWOM was found to negatively affect consumers’ final price perceptions which override the impact of quality perceptions in value formations. The value perceptions of the GPT became less acceptable when eWOM was mixed compared to when eWOM was absent or was positive. Mixed eWOM had a negative effect on value perceptions but not as great as when negative eWOM was present, and this was consistently found to apply for all price acceptability levels of the GPT. This study’s contribution to eWOM research and implications for the post-pandemic recovery of tourism and hospitality service providers are made, together with suggested strategies using innovative technologies and communications to enhance their adaptive resilience in the new normal. Full article

The purpose of this study is to evaluate green hotels in Sentosa Island, Singapore, with big data analysis utilizing online reviews regarding environmental sustainability practices. Tourism, while providing significant economic benefits, also contributes to environmental degradation, particularly through the hotel industry, which accounts for a substantial share of global greenhouse gas emissions. Sustainable tourism practices are becoming increasingly popular as the public becomes more aware of the environment. As a result, green hotels emerged as a solution and hotels are taking steps to become eco-friendly. Based on the 3579 online reviews, the findings indicate that green practices, including water and energy conservation, play a crucial role in enhancing customer satisfaction, alongside traditional hospitality elements such as service quality and amenities. The integration underscores the importance of incorporating sustainability into core operations without compromising the high standards of service that guests expect. This research contributes to the understanding of sustainable hospitality practices, offering actionable recommendations for policymakers and hotel managers to foster environmentally friendly practices while maintaining customer satisfaction. Full article

Although scientific literature already shows the connections between gamification, tourism and sustainability, there is still a gap in research into how gamification can transform cities into more playful, sustainable and intelligent tourism destinations. Gamification in tourism promotes engagement, education and innovation, enriching experiences and driving the development of smart destinations. This study advances this debate by employing a mixed methodology, combining a systematic review (supported by Rayyan, an artificial intelligence) and content analysis (supported by Iramuteq, a textual software, version 0.7 Alpha 2). Additionally, we used Dreamscape by Voyant Tools online for a geographical analysis of the textual corpus formed by n = 61 abstracts. The intersection of main findings from the techniques applied provides eight new insights on urban tourism education through gamification, which are as follows: (1) exchange between the Global North and South; (2) local and global participation; (3) collaborations to achieve the 17 SDGs; (4) improve urban destination marketing by ethics principles; (5) deepening disruption technology and ethics studies; (6) the transformation of reality by sciences; (7) gamification on interdisciplinary perspectives; and (8) the role of gamification on the tourism and hospitality industry. The results suggest that gamification in tourism can offer a new ontological approach to addressing sustainable urban development, highlighting both theoretical and practical challenges. This research has both theoretical and practical implications, advancing the concept of ecogaming by integrating technology, entertainment and sustainability, and reinforces its potential to educate and engage in sustainable urban tourism practices. Full article

In the era of online accommodation reviews, understanding the consumer mind is essential for the hospitality industry. This study seeks to profile consumers based on their reservation decisions made after reviewing online feedback and to explore the complex relationship between consumer perceptions and their decision-making processes. To lay a solid foundation for this research, a thorough bibliometric analysis was conducted to map the existing literature and identify key trends in the field. Data were collected using a non-probability convenience sampling method through an online survey targeting Romanian residents. Performing a hierarchical cluster analysis, followed by a K-means cluster analysis, distinct consumer segments with varying levels of trust and responsiveness were identified. The four primary clusters are Young Risk-Averse Planners, Trust-Oriented Quality Seekers, Skeptical Detail Seekers and Independent Value Seekers. Each segment displayed unique preferences regarding the types of reviews they value and their influence on booking decisions. These findings highlight the need for hotel managers and marketers to develop tailored strategies that cater to the diverse needs of consumers, enhancing service delivery and promoting sustainable tourism practices. This research provides valuable insights into the dynamics of online reviews and stresses the importance of understanding consumer perceptions in navigating the complexities of today’s hospitality industry. Full article

This study examines how tourists perceive food quality in Berlin using AI-driven sentiment analysis tools. The goal is to understand the factors shaping tourists’ perceptions and provide insights to improve the hospitality industry and customer satisfaction. By analyzing reviews from online platforms, this research identifies key themes and trends in tourists’ feedback. The use of AI, specifically for sentiment analysis, supports efficient and detailed evaluation of customer opinions. This study employed lexicon-based sentiment analysis to evaluate tourists’ feedback on online platforms and compared the sentiment scores of textual feedback with their direct rating scores. The results show that integrating sentiment scores derived from AI tools with tourists’ rating scores provides deeper insights into service quality within the tourism sector. Full article

Given the importance of semiotics and destination image (TDI) in the field of tourism and hospitality marketing, this study proposes a conceptual model that integrates Peirce’s semiotic triad, Grönroos’s quality service model, and Morris’s semiotic trichotomies in the TDI formation circle. The new framework aims to measure the contribution of quality hospitality services to online TDI formation. Using scaled comparisons of homogeneous big data, this framework was empirically tested with all two- and three-star Michelin restaurants and a sample of 100 four- and five-star hotels, all located in Asia and Europe, reviewed in 317,979 online travel reviews (OTRs) hosted on TripAdvisor. The results showed that three-star restaurants and five-star hotels are more popular in terms of the number of OTRs, but diners and guests are more satisfied with and loyal to two-star restaurants and four-star hotels. This big data finding contradicts previous survey-based research on quality services. Instead, the results confirm that consumer satisfaction positively affects consumer loyalty. The new approach to the TDI from a semiotic perspective—destination image semiotics—can represent a paradigm shift in the analysis of TDI through user-generated content (UGC). The proposed conceptual framework integrates several sound theoretical models to extract maximum insights from UGC. Full article

For sustainable hospitality and tourism, the validity of online evaluations is crucial at a time when they influence travelers’ choices. Understanding the facts and conducting a thorough investigation to distinguish between truthful and deceptive hotel reviews are crucial. The urgent need to discern between truthful and deceptive hotel reviews is addressed by the current study. This misleading “opinion spam” is common in the hospitality sector, misleading potential customers and harming the standing of hotel review websites. This data science project aims to create a reliable detection system that correctly recognizes and classifies hotel reviews as either true or misleading. When it comes to natural language processing, sentiment analysis is essential for determining the text’s emotional tone. With an 800-instance dataset comprising true and false reviews, this study investigates the sentiment analysis performance of three deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Among the training, testing, and validation sets, the CNN model yielded the highest accuracy rates, measuring 98%, 77%, and 80%, respectively. Despite showing balanced precision and recall, the LSTM model was not as accurate as the CNN model, with an accuracy of 60%. There were difficulties in capturing sequential relationships, for which the RNN model further trailed, with accuracy rates of 57%, 57%, and 58%. A thorough assessment of every model’s performance was conducted using ROC curves and classification reports. Full article

This research tests the relationship between aspects of customer influenceability at the time of booking a hotel with the propensity to write a review in electronic word-of-mouth communication. A valid sample of 739 online questionnaires was obtained. An Exploratory Factor Analysis was conducted in order to reduce the dimensions of the two critical variables, and a measurement model was built. Then a Path analysis was carried out. The novelty of this research lies in measuring the evolution from being a passive eWOM reader to a proactive eWOM writer. Results indicate a relationship between being influenced by reading reviews and the propensity to write reviews. The most important underlying motivation to write a review is altruistic. Managers should try to identify the most responsive customers and encourage them to write reviews on altruistic grounds. This study effectively validated the impact of being responsive to reading reviews on the inclination to, in turn, write them. Findings contribute to the evolving research landscape in eWOM within the hospitality and tourism sector, offering practical insights for industry practitioners to formulate more effective strategies in soliciting and managing customer reviews. Full article

With the diverse demands of customers for their accommodation experiences, integrated resorts have emerged and have been popular among tourists. Meanwhile, in the context of Tourism and Hospitality 4.0, online information collection and marketing has been one of the most effective strategies for industry operators and investors. Thus, the present study adopted online reviews generated by customers themselves to explore their experience and satisfaction with integrated resorts. With the qualitative and quantitative analyses of online customer reviews, firstly, 70 prominent keywords were extracted from the reviews, and some general understanding was obtained from the top frequent words. After conducting a semantic network analysis of the top frequent words, significant and central words in the network were identified, which were utilized as basic variables for the specific exploration of factors reflecting customers’ experience. In the meantime, four dimensions, “Amenities”, “Entertainment”, “Tourist”, and “Atmosphere”, were garnered via CONCOR analysis to illustrate the key dimensions of customers’ cognition and awareness towards resorts. At last, a quantitative analysis with an exploration factor analysis and a linear regression analysis were conducted to testify the correlation between customer experience and satisfaction. Three factors, “environment”, “staff service”, and “food service”, have a significant effect on customer satisfaction. Analytics of online customer reviews with a big volume, which differ from traditional research methods by designed structure or content, could generate more comprehensive understanding and cognition of research subjects. The findings obtained from analytics could provide baseline information for future research and industry development and promotion. Full article

Studies on the effect of biospheric value, eco-design, and green management intransigence on perceived green performance in the tourism and hospitality industry are gradually emerging. However, more evidence is needed from the aviation industry or airport context, especially in Africa. This cross-sectional study aims to probe and demonstrate the effect of biospheric value on green management intransigence and perceived green performance, the mediating role of management intransigence and biospheric value, and the influence on pro-environmental behavior among airport management and employees. The extended theory of planned behavior (TPBe) and triple bottom line theory (TBL)/sustainable economic development theory (SED) (TBL/SED) set the foundation for this research study. With the case study approach, data were collected through online questionnaires from employees and management staff of two international airports in Lagos and Abuja, Nigeria. This scientific study contributes to the literature on green energy by shedding light on the importance of integrating green practices into airport operations with environmentally friendly programs. Its focus on green management intransigence and its implications on employees’ behavior has received little or no attention. The data were analyzed using PLS-SEM and Importance–performance matrix analysis (IPMA). The IPMA is innovative as it helps to extend the results of PLS-SEM by also taking the importance and performance of each construct into account graphically as it relates to green airport management. IPMA posits that management tends to take actions to improve conditions that enhance factors of most significant concern to stakeholders. Our results reveal the effect of biospheric value and the behaviors of management and nonmanagement staff of the selected airports on the green performance with apparent differences in the group-specific performance. In practice, this implies an urgent need for airport management to review their approach and strategy to sustainable practices, airports’ resilience, and adaptation to climate change for sustainable tourism development. This study advances scientific and practical knowledge of eco-design of airport buildings (EAB), biospheric-value (BV), and green management intransigence (GMI). The findings can assist decision makers and practitioners in embracing green technologies and practices in airport management and operations. Full article

In the present Internet age, customers have turned to online booking websites to meet their demand for quality hotel services and convey their experiences. As hotels can survive and succeed by satisfying consumers and obtaining high online ratings, this research predicts the key factors impacting these ratings. The study analyzes online consumer review data gleaned from the popular Booking.com website for hotel ratings near the key religious heritage site in Makkah, Saudi Arabia. The research applies the four-factor theory due to its importance in studying consumer satisfaction and prior non-application to the hospitality and tourism industry. This theory extended the two-factor theory to report four distinct sets of factors: satisfiers, dissatisfiers, criticals, and neutrals. Accordingly, the present research presents a novel categorization of key predictive factors and enriches the literature, which categorized factors as either satisfiers or dissatisfiers. Consequently, facilities and comfort are critical factors, while cleanliness, staff, and location are satisfiers; the value for the money spent is dissatisfier; the availability of breakfast and restaurants are neutral factors. The hospitality industry, specifically the hotel industry, can focus on characteristics of the four distinct sets of factors; various nations can leverage the findings to boost their hospitality and tourism sectors. Full article

E-commerce in the hospitality and tourism field has already ranked No. 2 among all online shopping categories worldwide. However, customers’ visits to a hotel booking website cannot guarantee the generation of sales, while the conversion rate is regarded as the indicator that effectively assesses the e-commerce website performance. This study aimed to investigate the influential factors of conversion rates from both affective content and the communication style of customer’s online reviews. The affective content was evaluated with eight emotional dimensions (i.e., joy, sadness, anger, fear, trust, disgust, anticipation, and surprise) in Plutchik’s emotion wheel, and the communication style perspective was assessed with linguistic style matching (LSM). In total, 111,926 customer reviews from 641 hotels in five cities in the U.S. were collected for the analysis. Results indicated that LSM and four emotions have significant impacts on hotel conversion rates. This research contributes to the knowledge body of customers’ conversion behaviors on hotel booking websites and offers pertinent practical implications. Full article

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. Full article