Purpose: This study aims to enrich the published literature on hospitality and tourism by applying big data analytics and data mining algorithms to predict travelers’ online complaint attributions to significantly different hotel classes (i.e., higher star-rating and lower star-rating). Design/methodology/approach: First, 1992 valid online complaints were manually obtained from over 350 hotels located in the UK. The textual data were converted into structured data by utilizing content analysis. Ten complaint attributes and 52 items were identified. Second, a two-step analysis approach was applied via data-mining algorithms. For this study, sensitivity analysis was conducted to identify the most important online complaint attributes, then decision tree models (i.e., the CHAID algorithm) were implemented to discover potential relationships that might exist between complaint attributes in the online complaining behavior of guests from different hotel classes. Findings: Sensitivity analysis revealed that Hotel Size is the most important online complaint attribute, while Service Encounter and Room Space emerged as the second and third most important factors in each of the four decision tree models. The CHAID analysis findings also revealed that guests at higher-star-rating hotels are most likely to leave online complaints about (i) Service Encounter, when staying at large hotels; (ii) Value for Money and Service Encounter, when staying at medium-sized hotels; (iii) Room Space and Service Encounter, when staying at small hotels. Additionally, the guests of lower-star-rating hotels are most likely to write online complaints about Cleanliness, but not Value for Money, Room Space, or Service Encounter, and to stay at small hotels. Practical implications: By utilizing new data-mining algorithms, more profound findings can be discovered and utilized to reinforce the strengths of hotel operations to meet the expectations and needs of their target guests. Originality/value: The study’s main contribution lies in the utilization of data-mining algorithms to predict online complaining behavior between different classes of hotel guests.

Purpose: This study investigates how virtual reality (VR) travel attributes (e.g. sense and quality of information) influence spectators' flow experience, how emotion and past experience affect enjoyment and examines the impact of flow experience and enjoyment on satisfaction and booking or visiting intention.Design/methodology/approach: The VR tour stimuli were fabricated using scenic views from the National Aquarium in the USA. Participants were equipped with Matterport VR and audio headsets and started their virtual travel. Once the participants completed their VR tours, they were asked to complete the questionnaire. Using the stimulus-organism-response theory, 303 valid responses were analyzed using partial least square structural equation modeling (PLS-SEM).Findings: The results showed that the sense and quality of information in VR travel positively and significantly impacted the flow experience. Moreover, emotions and past experiences positively and significantly influenced the enjoyment of VR travel. Similarly, flow experience and enjoyment positively and significantly affect satisfaction. However, satisfaction with VR-related tourism experiences negatively affects users' bookings and visiting intentions.Practical implications: This study concludes that, from Thai tourists' perspectives, virtual travel should be used as a solution only during the pandemic because, in the long term it can cause a loss to the business chain in the tourism industry.Originality/value: To the best of the authors' knowledge, no prior research has examined the influence of past experiences and emotions on satisfaction with VR travel.

Literature has demonstrated that online reviews from tourism-specific websites and other electronic channels (e.g., TripAdvisor) do influence tourists’ decisions in all aspects. At this global era, it is relatively important to analyse the eWOM related research for obtaining more in-depth understanding and applications to this new mode of communication. The total of 110 SSCI-listed research articles related to eWOM studies since 2004 was retrieved from 18 refereed journals, all of which listed in the Web of Science, ScienceDirect and Google Scholar. By conducting content analysis approach, all research papers were systematically reviewed and analysed which generate 18 significant topical issues. As this review paper tries to retrospect and discuss the development and influential factors of eWOM, the study unveils the application in the hospitality industry and research opportunities for the future.

Organic products have been gaining popularity among consumers worldwide due to the environmental and health benefits they are associated with. As a result of this trend, organic industries have been flourishing and have been able to expand into a variety of consumer product/service categories. Looking to explore purchasing behavior related organic coconut cosmetic products, this study attempted to apply the theory of planned behavior (TPB), which is a method of predicting consumer behavior that has been used extensively in a variety of research areas in recent years. Based upon the literature review, an extended TPB model that incorporates health concerns and health knowledge, in addition to attitude, subjective norms and perceived behavior control was examined in this study. For the data collection, an online survey was issued to residents of Bangkok, Thailand; with a total of 613 respondents retuning the questionnaires. Structural equation modeling (SEM) was employed to analyze the data using SPSS AMOS 24. The results showed that attitude, subjective norms, perceived behavior control and health concerns positively affect purchase intention; however, health knowledge did not influence purchase intentions related to the organic coconut cosmetic products. Similar to the findings in most extant literature, attitude was found to exert the most influence on the purchase behavior in this study.