Post-epidemic factors influencing customer's booking intent for a hotel or leisure spot: an empirical study

Are we ready for hotel robots after the pandemic? A profile analysis

COVID-19 has changed many aspects of the hospitality and tourism industry, including technology-oriented and contactless solutions. Despite the increasing number of service companies using robots on their premises, most of the previous attempts and practices of adoption have remained unsuccessful. Prior research hints that socioeconomic factors could influence the successful adoption of these emerging technologies. Nevertheless, these studies ignore the role of profile factors and assume a homogenous response to using robots in service operations during the pandemic. Based on the theory of diffusion of innovation and a sample of 525 participants, this study investigates the differences in customers' attitudes, their level of involvement, and optimism for service robots as well as their intentions to use service robots in the five main areas of hotel operations (front desk, concierge, housekeeping, room service, and food and beverage) based on five profile factors (age, gender, income level, education, and purpose of trip). MANOVA tests show significant differences in all variables based on demographic factors; male, younger, more educated, higher income, and leisure travelers show more positive attitudes, higher involvement, optimism, and intention to use service robots across various hotel departments. In particular, mean scores were found to be smaller for the traditionally human-oriented functional areas of the hotel operations. We also clustered the participants based on their level of comfort and optimism about using service robots in hotels. Given the rapid changes in the service industry and the increasing adoption of service robots, this paper adds a much-needed contribution to the ongoing research on service robots in the service industry by investigating the impact of profile factors on guests' behavior towards service robots.

Risk Perception, Perceived Government Coping Validity, and Individual Response in the Early Stage of the COVID-19 Pandemic in China

As a major crisis event, the COVID-19 pandemic has affected the global economy, threatened the lives of the public, and caused varying degrees of impact on the public. Previous studies have shown that risk perception and government response had different impacts on the public, but they revealed more about the independent impact of risk perception and government response on the public. This study will comprehensively consider the impacts of these two factors on the behavior of the public in the early stage of the epidemic. We analyzed data from an online survey in the early days of the COVID-19 pandemic in China and categorized individual behaviors into three dimensions: entertainment and travel, work, and the stockpile of supplies. In addition, we defined the risk perception variables by two dimensions: knowledge of the epidemic itself and knowledge of the consequences of the epidemic. At the same time, we used an exploratory factor analysis to construct the variable of perceived government coping validity and then adopted the ordinal logit model for analysis. The results showed that in terms of entertainment and travel, people would not be affected even if they fully understood the epidemic itself; once they were aware of the negative social consequences of the epidemic, people would suspend entertainment and travel to prevent the spread of the virus. As for work or employment, people would not stop working or employment even if they realized the infectivity and harmfulness of the disease and its social consequences. Furthermore, fear of COVID-19 and the perception of uncontrolled COVID-19 significantly positively affected people's material stockpiling behavior. These results indicate that different risk perceptions had different effects on individual responses, and individual behaviors reflected different coping logics. In addition, the government's effective response to the epidemic would significantly reduce the negative impacts of the epidemic on the three dimensions of people's responses. These conclusions have certain policy implications for preventing and responding to outbreaks in other countries.

Driving hospitality and tourism to foster sustainable innovation: A systematic review of COVID-19-related studies and practical implications in the digital era

Sustainable innovation is a solution for the hospitality and tourism (H&T) industries to cope with the COVID-19 crisis, build resilience, and ensure survival post-pandemic. The primary aim of our review is to systematically identify and critically review the literature on sustainable innovation in H&T amid COVID-19 (conducted in 2020 and 2021), in order to synthesize and classify prevalent types, antecedents, and outcomes of sustainable innovation. The authors present a comprehensive review of the 58 articles on sustainable innovation in H&T through the Web of Science (WoS) database, spanning over 2 years (2020–2021). This review demonstrates that since the beginning of COVID-19, H&T have strongly mobilized network technologies (especially social media and digital platforms) and data-processing technologies (especially Artificial Intelligence (AI) and Machine Learning (ML)) in comparison with physical-digital interface technologies (especially Virtual Reality (VR)), while physical-digital process technologies remain very limited in these industries. Several relevant antecedents of the adoption of sustainable innovation, more specifically digital technologies, have been identified at multiple levels of analysis, including the organizational, managerial, and stakeholder levels. Our research also reveals several consequences of the adoption of sustainable innovation in H&T. These consequences were congregated according to the three main dimensions related to sustainability in economic, social, and environmental outcomes. This study provides important practical implications for the H&T sectors in the digital era and post-pandemic. The current research is the first study to systematically and critically review sustainable innovation in an H&T context.

Tourism Sustainability and COVID-19 Pandemic: Is There a Positive Side?

The COVID-19 pandemic has wreaked havoc on the tourism industry like never before, resulting in massive losses of revenue and jobs around the world. Accordingly, the pandemic exacerbated the already existing sustainability challenges of the tourism industry. However, there is also a positive side of the pandemic which is often overlooked by international scholarship. Thus, the present study aims to review the extant literature in the area of COVID-19 and the tourism industry’s sustainability and resilience in future crises. Through a synthesis of secondary data, it was possible to bring attention to the negative as well as the positive effects of COVID-19 on the global tourism industry. This article contributed to a better understanding of the positive side of the pandemic in terms of rethinking, resetting, and redefining the industry in a more sustainable way. The study lays out a conceptual framework for tourism managers and destination planners to identify the pandemic as an opportunity and adopt sustainable solutions to deal with the post-pandemic challenges, thereby developing more sustainable and resilient tourism businesses and destinations.

HRNET: AI-on-Edge for Mask Detection and Social Distancing Calculation

The purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations. The paper proposes a unique outbreak response system framework based on artificial intelligence and edge computing for citizen centric services to help track and trace people eluding safety policies like mask detection and social distancing measure in public or workplace setup. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. The adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The conceptual framework presented is validated through quantitative data analysis via secondary data collection from researcher's public websites, GitHub repositories and renowned journals and further benchmarking were conducted for experimental results in Microsoft Azure cloud environment. The study includes selective AI models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large-scale societal setup. Overall YOLO model outperforms in object detection task and is faster enough for mask detection and HRNetV2 outperform semantic segmentation problem applied to solve social distancing task in AI-Edge inferencing environmental setup. The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. The paper enriches the technological advancement in artificial intelligence and edge computing applied to problems in society and healthcare systems. The framework further equips government agency, system providers to design and construct technology-oriented models in community setup to increase the quality of life using emerging technologies into smart urban environments.