Search Results (76)

Online reviews play a critical role in shaping consumer decisions and providing valuable insights to enhance the products and services for businesses. As visual content becomes increasingly prevalent in reviews, it is essential to understand how image complexity influences review helpfulness. Despite the growing importance of images, the impact of color diversity and texture homogeneity on review helpfulness remains underexplored. Grounded in Information Diagnosticity Theory and Dual Coding Theory, this study investigates the relationship between image complexity and review helpfulness, as well as the moderating role of review text readability. Using a large-scale dataset from the hotel and travel sectors, the findings reveal that color diversity has a positive effect on review helpfulness, while texture homogeneity follows an inverted U-shaped relationship with helpfulness. Furthermore, text readability strengthens the positive impact of texture homogeneity, making moderately homogeneous images more effective when paired with clear and well-structured text. Heterogeneity analysis demonstrates that these effects vary across product categories. The results advance the understanding of multimodal information processing in online reviews, providing actionable guidance for platforms and businesses to refine the review systems. Full article

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

Although integrating smart technologies into service encounters can provide hoteliers with a competitive advantage, managing customer satisfaction in smart hotels remains challenging due to limited knowledge of how to prioritize improvements across smart service and traditional service. Therefore, the study aims to evaluate customer satisfaction with both smart and non-smart technology attributes in smart hotels, identify attributes with high improvement priorities, and uncover factors contributing to customer dissatisfaction. This study proposes a prioritization method for service improvement in smart hotels by analyzing online reviews from 42 smart hotels. The findings reveal that customers’ technological needs are well met in smart hotels, but smart hotels need to promptly address three key issues: long check-in wait times, staff attitude and competence, and breakfast quality. To maximize customer satisfaction, managers should adopt a hybrid service model that strikes the right balance between technology and human interaction. Full article

Hotels are one of the fastest-growing sectors in the tourism industry, and sentiment analysis plays a vital role in improving business performance and supporting sustainable practices. This paper proposes a novel framework combining topic mining and aspect-based sentiment analysis to examine 29,334 hotel reviews in Henan province in China, with the aim of informing strategies for sustainable hotel development. Our results reveal six key attributes of customer concern, particularly emphasizing family experiences, which reflect Henan’s appeal as a family tourism destination. Additionally, we uncover sentiment quadruples, including categories, aspect terms, opinion terms, and polarities, thus enabling a dual-dimensional evaluation of factors influencing customer satisfaction. The results reveal that service mainly influences overall category-level satisfaction, while bed, front desk, and breakfast primarily drive aspect-level satisfaction. This study provides valuable insights into customer feedback, offering empirical support for optimizing services and guiding the sustainable strategic development of regional hotels. Full article

Online recommendation agents have demonstrated their value in various contexts by helping users navigate information overload, supporting decision-making, and influencing user behavior. There is a lack of studies focusing on recommendation systems for green hotels that utilize user-generated content from social networking and e-commerce platforms. While numerous studies have explored the use of real-world datasets for hotel recommendations, the development of recommendation systems specifically for green hotels remains underexplored, particularly in the context of Saudi Arabia. This study attempts to develop a new approach for green hotel recommendations using text mining and Long Short-Term Memory techniques. Latent Dirichlet Allocation is used to identify the main aspects of users’ preferences from the user-generated content, which will help the recommender system to provide more accurate recommendations to the users. Long Short-Term Memory is used for preference prediction based on numerical ratings. To better perform recommendations, a clustering technique is used to overcome the scalability issue of the proposed recommender system, specifically when there is a large amount of data in the datasets. Specifically, a spectral clustering algorithm is used to cluster the users’ ratings on green hotels. To evaluate the proposed recommendation method, 4684 reviews were collected from Saudi Arabia’s green hotels on the TripAdvisor platform. The method was evaluated for its effectiveness in solving sparsity issues, recommendation accuracy, and scalability. It was found that Long Short-Term Memory better predicts the customers’ overall ratings on green hotels. The comparison results demonstrated that the proposed method provides the highest precision (Precision at Top @5 = 89.44, Precision at Top @7 = 88.21) and lowest prediction error (Mean Absolute Error = 0.84) in hotel recommendations. The author discusses the results and presents the research implications based on the findings of the proposed method. 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

The rapid growth of the internet and social media has made electronic word of mouth (eWOM) a key element of modern marketing. In the hospitality industry, nowadays, effective eWOM management is essential for developing impactful strategies and fostering customer satisfaction. This paper introduces an enhanced approach to strategic customer base management based on online reviews by extending the Recency, Frequency, and Monetary (RFM) model with three novel dimensions, the Helpfulness, Promoter Score, and Stability of the customer, thereby forming the RFHPS model. It also includes the 2-tuple linguistic model, one of the most popular computing with words models, to improve precision in the RFHPS score’s computation and the findings’ interpretability. Using K-means clustering, customers are segmented across these five dimensions. The data on luxury hotels in Spain gathered from TripAdvisor demonstrate the model’s applicability. By integrating this framework into customer relationship management systems, managers can tailor marketing strategies for distinct segments, facilitating deeper customer understanding and bolstering eWOM generation. 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

Travelers’ perceptions of hotels and their aspects have been the focus of much research and are often studied by analyzing consumers’ online reviews. Yet, little attention has been given to the effect of the trip mode, i.e., whether the person travels alone or with others, on travelers’ preferences as sentiment. Here, we study the influence of the trip mode and purpose using a mixed-methods approach. We conducted a user study to evaluate the perceptions of reviews across trip modes and found that star ratings do not consistently capture the sentiment in text reviews; on average, solo travelers’ text reviews are perceived as more negative than the star ratings they assigned, whether they travel for business or pleasure. We then analyzed over 137,000 reviews from TripAdvisor and Venere and found that a co-occurrence network approach naturally divides the text of reviews into hotel aspects. We used this result to measure the importance of hotel aspects across various traveler modes and purposes and identified significant differences in their preferences. These findings underscore the need for personalized marketing and services, highlighting the role of trip mode in shaping online review sentiment and traveler satisfaction. Full article

Hospitality services play a crucial role in shaping tourist satisfaction and revisiting intention toward destinations. Traditional feedback methods like surveys often fail to capture the nuanced and real-time experiences of tourists. Digital platforms such as TripAdvisor, Yelp, and Google Reviews provide a rich source of user-generated content, but the sheer volume of reviews makes manual analysis impractical. This study proposes integrating aspect-based sentiment analysis with zero-shot learning to analyze online tourist reviews effectively without requiring extensive annotated datasets. Using pretrained models like RoBERTa, the research framework involves keyword extraction, sentence segment detection, aspect construction, and sentiment polarity measurement. The dataset, sourced from TripAdvisor reviews of attractions, hotels, and restaurants in Central Java, Indonesia, underwent preprocessing to ensure suitability for analysis. The results highlight the importance of aspects such as food, accommodation, and cultural experiences in tourist satisfaction. The findings indicate a need for continuous service improvement to meet evolving tourist expectations, demonstrating the potential of advanced natural language processing techniques in enhancing hospitality services and customer satisfaction. Full article

This study compares the effectiveness of fine-tuning Transformer models, specifically BERT, RoBERTa, DeBERTa, and GPT-2, against using prompt engineering in LLMs like ChatGPT and GPT-4 for multi-class classification of hotel reviews. As the hospitality industry increasingly relies on online customer feedback to improve services and strategize marketing, accurately analyzing this feedback is crucial. Our research employs a multi-task learning framework to simultaneously conduct sentiment analysis and categorize reviews into aspects such as service quality, ambiance, and food. We assess the capabilities of fine-tuned Transformer models and LLMs with prompt engineering in processing and understanding the complex user-generated content prevalent in the hospitality industry. The results show that fine-tuned models, particularly RoBERTa, are more adept at classification tasks due to their deep contextual processing abilities and faster execution times. In contrast, while ChatGPT and GPT-4 excel in sentiment analysis by better capturing the nuances of human emotions, they require more computational power and longer processing times. Our findings support the hypothesis that fine-tuning models can achieve better results and faster execution than using prompt engineering in LLMs for multi-class classification in hospitality reviews. This study suggests that selecting the appropriate NLP model depends on the task’s specific needs, balancing computational efficiency and the depth of sentiment analysis required for actionable insights in hospitality management. Full article

This study aims to dynamically mine the demands of hotel consumers. A total of 378,270 online reviews in the cities of Beijing, Chengdu, and Guangzhou in China were crawled using Python. Natural language processing (e.g., opinion mining and the BERT model) and an improved Kano model (containing One-dimensional, Attractive, Indifferent, and Must-be) were utilised to analyse online hotel reviews. The results indicate that the hotel attributes that consumers care about (e.g., Clean, Breakfast, and Front Desk) are dynamically fluctuating, and the attention and satisfaction of corresponding attributes will also change. This study classified consumer demand into eight types across cities and found that it changes over time. In addition, we also found that hotel attributes, satisfaction and attention, and consumer demands vary among different cities. Existing studies of capturing consumer demand are usually time-consuming and static, and the results are subjective. This study compared and analysed the consumer demands of hotels in different cities via a dynamic perspective, and used hybrid methods to improve the granularity of the analysis, expanding the general applicability of the Kano model. Hotel managers can refer to the results of this article to allocate resources for improvement and create competitive hotel services. Full article

As a result of globalization and other factors, periodic shocks to economic activity have become more frequent in recent years. How these periods of economic uncertainty affect different business sectors and industries has become an important emerging area of research activity. Sensory experiences are increasingly recognized as an important aspect of the customer experience. Likewise, online reviews and the usefulness rating given by review consumers are important factors in the consumers’ purchasing decision-making process. How these factors are affected by periods of crisis is an underexplored area of research that this study addresses through the exploration and comparison of the perceived helpfulness of sensory experience online reviews, specifically in the hotel industry, before and since the COVID-19 pandemic. Primary data were harvested from the Booking.com website before and during the pandemic; 143,739 online reviews were analyzed using a keyword search based on six dimensions of hotel services to identify those reviews with sensory experience content. The analysis applied Herzberg’s two-factor theory, where each service attribute was examined as both positive (satisfier) and negative (dissatisfier). Empirical analytical methods were applied to produce compelling findings. The findings indicate that the reviews of multisensory experiences affect the perceived value of a post both negatively and positively, respectively, and that the pandemic did not affect the relationship between reviews and the perceived helpfulness of the reviews. This study has both theoretical and practical implications for researchers and practitioners by applying and building on Herzberg’s two-factor theory of online reviews in the hospitality sector during a period of crisis, as well as addressing a gap in the existing literature on how the pandemic affected the relationships between the online reviews of sensory experiences and their perceived usefulness. Practitioners may find the results useful in how they allocate their resources and focus during such periods to optimize their competitiveness. 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

This study conducts a bibliometric analysis of online customer and hotel review research, aiming to provide insights into where each field comes from, stands now and ought to go in the future. In particular, this study examines how the existing research on online customer reviews can benefit future hotel review research. Data collected from Web-of-Science and Scopus created an expanded network of 797 core articles and 19,374 citations to identify intellectual structures, developing trends, and future research gaps. This study offers a visual overview of journals, institutions, countries, research themes and authors to assess the overall directions hotels can take. It underscores the necessity for rigorous and relevant research amid the proliferation of online reviews and emphasises the imperative for academia to bridge the gap between theoretical insights and practical applications within the dynamic tourism industry. This study provides researchers and industry professionals with useful tools to understand and deal with the complexities of online reviews. It also highlights the important role these reviews play in shaping the future of tourism strategies. Full article