Search for: All records

This paper leverages natural language processing, spatial analysis, and statistical analysis to examine the relationship between restaurants’ safety violations and COVID-19 cases. We used location-based consumers’ complaints data during the early stage of business reopening in Florida, USA. First, statistical analysis was conducted to examine the correlation between restaurants’ safety violations and COVID-19 transmission. Second, a neural network-based deep learning model was developed to perform topic modeling based on consumers’ complaints. Third, spatial modeling of the complaints’ geographic distributions was performed to identify the hotspots of consumers’ complaints and COVID-19 cases. The results reveal a positive relationship between consumers’ complaints about restaurants’ safety violations and COVID-19 cases. In particular, consumers’ complaints about personal protection measures had the highest correlation with COVID-19 cases, followed by environmental safety measures. Our analytical methods and findings shed light on customers’ behavioral shifts and hospitality businesses’ adaptive practices during a pandemic. 

This paper reviews the state-of-art articles on instructional technologies for hospitality and tourism education. The types of technologies, roles in instruction, theoretical underpinnings, assessments, benefits, and challenges are synthesized. Education context, knowledge, skills, and attitudes developed through instructional technologies are also explored. Virtual reality, virtual games, social media, online courses, and simulations have been primarily used. Instructional technologies were applied to facilitate content delivery, practice, communication, assessments, feedback, and authentic learning experiences. Most studies did not ground their educational technologies in defensible learning theories. Only a limited number of studies employed adoption theories to examine the intention of using technologies. Further studies should investigate whether certain instructional technologies provide significant benefits to learning relative to their costs. 

The COVID-19 pandemic has imposed various obstacles and restrictions for the tourism and hospitality industry. This paper adopts the concept of the behavioural immune system to discuss tourism and hospitality customers’ potential behaviours during the pandemic and provide business strategies that can address these behaviours. The behavioural immune system is a motivational system that determines individuals’ behaviours to pathogen infection. First, this study introduces the mechanism of the behavioural immune system including environmental evaluation and aversive perception, aversive emotional and cognitive responses, and avoidance behaviours. It also provides examples in the guest service context to better portray the mechanism. Second, the study suggests specific measures for tourism and hospitality businesses that may help them to prevent the aversive and avoidance responses of customers triggered by their behavioural immune system during the pandemic. Then, the study integrates customers’ responses and businesses’ measures in a framework, which extends the literature on customers’ behaviour in the hospitality and tourism context. To the authors’ knowledge, this is the first time the concept of behavioural immune system is adopted to discuss customers’ behaviours towards tourism and hospitality services during a pandemic. 

Purpose This paper aims to examine transferable skills and viable career transition pathways for hospitality and tourism workers. Future career prospects are discussed, along with the importance of reskilling for low-wage hospitality workers. Design/methodology/approach A network analysis is conducted to model skill relationships between the hospitality industry and other industries such as health-care and information technology. Multiple data are used in the analysis, including data from the US Department of Labor Occupational Information Network (O*NET), wage data from the Bureau of Labor Statistics and job computerization data (Frey and Osborne, 2017). Findings Although hospitality workers have lower than average skills scores when compared to workers from other career clusters included in the analysis, they possess essential soft skills that are valuable in other industries. Therefore, improving hospitality workers’ existing soft skills may help them enhance their cross-sector mobility, which may allow them to obtain jobs with a lower likelihood of computerization. Practical implications The findings shed light on workforce development theories and practice in the hospitality industry by quantitatively analyzing cross-sector skill correlations. Sharpening transferable soft skills will be essential to enhancing hospitality workers’ career development opportunities. Originality/value To the best of the authors’ knowledge, this is the first study that specifically examines the skill taxonomy for the hospitality industry and identifies its connection with other in-demand career clusters. 

Purpose This study reviews existing research and current applications of artificial intelligence (AI) in the hospitality and tourism industry. It further proposes a new evaluation framework to inform the susceptibility of AI adoptions. Design/methodology/approach This is a synthesis and evaluation study that qualitatively summarizes and presents findings on AI applications in the hospitality and tourism industry. Current AI applications are rated using a seven-dimensional framework based on Rogers' (2003) diffusion theory. Findings AI adoption susceptibility in the hospitality and tourism industry varies based on the type of AI. Search/booking engines, virtual agents and chatbots rank high in the adoption susceptibility. Research limitations/implications This study bridges innovation diffusion theoretical underpinnings and AI applications. The findings support researchers, developers and managers in evaluating the adoption susceptibility of AI technologies in the hospitality and tourism industry. Originality/value This paper is among the few that focus on assessing AI adoption susceptibility in the hospitality and tourism industry. This paper develops a theory-based framework for systematically evaluating AI innovations in hospitality and tourism. 

Fast technological developments have transformed the tourism and hospitality services and the labor market. The outbreak of the COVID-19 pandemic exposed the vulnerability of the tourism and hospitality workforce. There is insufficient prior work about creating better work conditions and greater career opportunities for hospitality and tourism professionals in the swiftly changing labor market. To this end, this article analyzes the historical employee–employer relationships in the tourism and hospitality industry. It presents a new employee-sharing model framework for enhancing resilience and flexibility for hospitality and tourism enterprises and workers.