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.

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.

Purpose: This study aims to develop an assessment scale for university service quality (university SQ) and examine University Service Quality assessment model (UNIQUAL) of higher education during the pandemic.Design/methodology/approach: Two studies applied a mixed-method design to develop and validate the UNIQUAL scale. In-depth interviews and literature reviews were conducted to refine the initial dimensions and items of UNIQUAL in Study 1. Item analysis, EFA and CFA were then conducted to purify item refinement, scale refinement, purification and validation in Study 2. Finally, a confirmed UNIQUAL model was analyzed via partial least squares structural equation modeling (PLS-SEM) using Smart-PLS 4.0.Findings: The research confirms the four-factor structure of UNIQUAL, with a total of 16 items, to be a valid and reliable scale for the assessment of the service quality (SQ) of universities. Having adopted the bias-corrected and accelerated (BCa) bootstrap approach to study 5,000 subsamples from 27 countries, the authors found “responsiveness” and “empathy” to be significantly associated and have positive relationships with students' satisfaction with university SQ. Furthermore, university SQ and satisfaction were mediated by “health and safety” concerns.Practical implications: The newly developed UNIQUAL scale would be of value to educators and authorities of higher education to assess the SQ of their universities to enhance the effectiveness of student learning. The improvement in satisfaction with higher education's SQ ultimately helps in retaining both international and local students amidst concerns about traveling and studying during the pandemic.Social implications: COVID-19 has affected the private and public sectors worldwide. Millions of students have been affected by schools being shut down and substituted with distance-learning programs. Thus, the assessment of the quality of university services has become an important support mechanism for retaining the sustainability of higher education.Originality/value: The UNIQUAL scale provides a conceptual model and validates an assessment tool. The research hypotheses confirm the relationship between university SQ and satisfaction from the perspective of international students.

Introduction: COVID-19 has severely impacted industries and individual lives globally. Due to travel restrictions and social distancing to reduce the spread of the disease, it has seriously affected the travel and tourism industry in Thailand, especially its community-based tourism. The impact of travel bans has magnified employment and income loss to most local families and their communities, negatively impacting the development of local tourism economies. Purpose: The main objective of this study is to investigate the impacts of COVID-19 on community-based tourism as well as its adaptation solutions using a case study from a specific region in Thailand. Design/methodology/approach: Using a phenomenological approach, a series of qualitative face-to-face, semi-structured interviews were undertaken with 42 stakeholders involved in community-based tourism. Findings: The study results indicate that job relocation, temporary business closures, reducing fixed costs, and increasing different sale channels represent the most critical factors impacting CBT operational activities directly affected by the COVID-19 crisis and requiring immediate action. Other supplementary actions involve government financial support, business compensation, early vaccination, reduction of agricultural debt, and increasing agricultural product value. Social implications: These study findings offer direction for Thai governmental policy makers and CBT leaders for the establishment of actionable practices designed to respond rapidly and appropriately to local communities and entrepreneurs during crises such as the COVID-19 pandemic. Originality: The originality of this research was obtained from local stakeholders’ insights on the impacts of COVID-19 upon community-based tourism in the northeast region of Thailand, where tourism represents significant economic value in terms of salaries, wages, and employment generation.

Effectively capturing consumer feedback can help organizations swiftly react and improve the quality of their service processes and delivery systems. In most organizations, frontline employees are a vital source of customer feedback as they interact with customers frequently and intimately. Thus, the current paper seeks to understand the factors influencing employee willingness to report customer feedback up the organizational channels of communication. The conceptual model was tested empirically using data collected from major chain restaurants in Taiwan involving 332 frontline employees. A hierarchical regression analysis was used to test the hypothesized model. The obtained results demonstrate the impact of service climate, perceived organizational support, and internal locus of control on frontline employees’ willingness to report customer feedback.