Dr Erin Chao Ling

This study investigates the influence of employing persons with disabilities (PWD) in hotels on consumer behavior, with an emphasis on word of mouth and repurchase intention. The research delves into the underlying mechanisms behind these effects and examines potential boundary conditions using hotel characteristics. Utilizing two between-subject design experiments, 1443 responses were analyzed using the propensity score weighting scheme and multigroup analysis. Results reveal that PWD employment in the hotel industry has a positive impact on word of mouth and repurchase intention through the moral decision-making process, specifically progressing from moral judgement to moral obligation, and perceived corporate social responsibility (CSR). These effects remain consistent across different hotel star ratings and whether chain or independent. The findings enhance the literature on equality, diversity and inclusion by identifying the underlying mechanism of how hotel guests respond to PWD employment in the hotel industry, drawing insights from moral psychology and perceived CSR. •Persons with disabilities (PWD) employment in hotels boosts word of mouth and repurchase intent.•Moral decision-making mediates PWD employment's impact on behavior.•Perceived CSR is pivotal in shaping guest responses to PWD employment.•Effects of PWD employment are consistent across all hotel types.•Study bridges gaps using insights from moral psychology and perceived CSR.

This study developed a perceived intelligence scale for artificially intelligent (AI) assistants and investigated its impact on users’ travel-related behavioral intentions. A four-stage study with a mixed-methods design was conducted. Study 1 identified three dimensions and 26 initial items through a systematic literature review, interviews, and focus group discussions. Study 2 used exploratory factor analysis to refine the items. Through composite confirmatory analysis, Study 3 confirmed an 18-item and three-dimensional scale (conversational intelligence, information quality, anthropomorphism). Study 4 established the scale’s predictive validity in travelers’ intentions to use AI assistants to search for travel information and make travel bookings. This research made the first attempt to identify factors shaping users’ perceptions of AI assistant intelligence, extending the understanding of human-AI interaction and AI technology adoption in the travel sector. Furthermore, it provides actionable recommendations for the travel industry and AI developers when designing and deploying AI assistant services.

This study developed a perceived intelligence scale for artificially intelligent (AI) assistants and investigated its impact on users' travel-related behavioral intentions. A four-stage study with a mixed-methods design was conducted. Study 1 identified three dimensions and 26 initial items through a systematic literature review, interviews, and focus group discussions. Study 2 used exploratory factor analysis to refine the items. Through composite confirmatory analysis, Study 3 confirmed an 18-item and three-dimensional scale (conversational intelligence, information quality, anthropomorphism). Study 4 established the scale's predictive validity in travelers' intention to use AI assistants to search for travel information and make travel bookings. This research made the first attempt to identify factors shaping users' perceptions of AI assistant intelligence, extending the understanding of human-AI interaction and AI technology adoption in the travel sector. Furthermore, it provides actionable recommendations for the travel industry and AI developers when designing and deploying AI assistant services.

This paper presents a new information source for supporting robot localisation: material composition. The proposed method complements the existing visual, structural, and semantic cues utilized in the literature. However, it has a distinct advantage in its ability to differentiate structurally, visually or categorically similar objects such as different doors, by using Raman spectrometers. Such devices can identify the material of objects it probes through the bonds between the material's molecules. Unlike similar sensors, such as mass spectroscopy, it does so without damaging the material or environment. In addition to introducing the first material-based localisation algorithm, this paper supports the future growth of the field by presenting a gazebo plugin for Raman spectrometers, material sensing demonstrations, as well as the first-ever localisation data-set with benchmarks for material-based localisation. This benchmarking shows that the proposed technique results in a significant improvement over current state-of-the-art localisation techniques, achieving 16\% more accurate localisation than the leading baseline.

This study developed a perceived intelligence scale for artificially intelligent (AI) assistants and investigated its impact on users’ travel-related behavioral intentions. A four-stage study with a mixed-methods design was conducted. Study 1 identified three dimensions and 26 initial items through a systematic literature review, interviews, and focus group discussions. Study 2 used exploratory factor analysis to refine the items. Through composite confirmatory analysis, Study 3 confirmed an 18-item and three-dimensional scale (conversational intelligence, information quality, anthropomorphism). Study 4 established the scale’s predictive validity in travelers’ intentions to use AI assistants to search for travel information and make travel bookings. This research made the first attempt to identify factors shaping users’ perceptions of AI assistant intelligence, extending the understanding of human-AI interaction and AI technology adoption in the travel sector. Furthermore, it provides actionable recommendations for the travel industry and AI developers when designing and deploying AI assistant services.

As artificially intelligent conversational agents (ICAs) become a popular customer service solution for businesses, understanding the drivers of user acceptance of ICAs is critical to ensure its successful implementation. To provide a comprehensive review of factors affecting consumers’ adoption and use of ICAs, this study performs a systematic literature review of extant empirical research on this topic. Based on a literature search performed in July 2019 followed by a snowballing approach, 18 relevant articles were analyzed. Factors found to influence human-machine cognitive engagement were categorized into usage-related, agentrelated, user-related, attitude and evaluation, and other factors. This study proposed a collective model of users’ acceptance and use of ICAs, whereby user acceptance is driven mainly by usage benefits, which are influenced by agent and user characteristics. The study emphasizes the proposed model’s context-dependency, as relevant factors depend on usage settings, and provides several strategic business implications, including service design, personalization, and customer relationship management.

This paper presents a new information source for supporting robot localisation: material composition. The proposed method complements the existing visual, structural, and semantic cues utilized in the literature. However, it has a distinct advantage in its ability to differentiate structurally [23], visually [25] or categorically [1] similar objects such as different doors, by using Raman spectrometers. Such devices can identify the material of objects it probes through the bonds between the material’s molecules. Unlike similar sensors, such as mass spectroscopy, it does so without damaging the material or environment. In addition to introducing the first material-based localisation algorithm, this paper supports the future growth of the field by presenting a gazebo plugin for Raman spectrometers, material sensing demonstrations, as well as the first-ever localisation data-set with benchmarks for material-based localisation. This benchmarking shows that the proposed technique results in a significant improvement over current state-of-the-art localisation techniques, achieving 16% more accurate localisation than the leading baseline. The code and dataset will be released at: https://github.com/ThirgoodC/RaSpectLoc

This paper presents a new information source for supporting robot localisation: material composition. The proposed method complements the existing visual, structural, and semantic cues utilized in the literature. However, it has a distinct advantage in its ability to differentiate structurally [23], visually [25] or categorically [1] similar objects such as different doors, by using Raman spectrometers. Such devices can identify the material of objects it probes through the bonds between the material's molecules. Unlike similar sensors, such as mass spectroscopy, it does so without damaging the material or environment. In addition to introducing the first material-based localisation algorithm, this paper supports the future growth of the field by presenting a gazebo plugin for Raman spectrometers, material sensing demonstrations, as well as the first-ever localisation data-set with benchmarks for material-based localisation. This benchmarking shows that the proposed technique results in a significant improvement over current state-of-the-art localisation techniques, achieving 16 % more accurate localisation than the leading baseline. The code and dataset will be released at: https://github.com/ThirgoodC/RaSpectLoc

The implementation of artificial intelligence, robotics, and automation in tourism and hospitality has received increasing attention from researchers and practitioners alike. It is expected that innovative technological solutions will bring a host of transformation to the sector (Ivanov & Webster, 2019). While examples of full automation in tourism remain scant, understanding the factors influencing organizational decision to adopt automation is important to assess the likelihood to increase adoption rate in the future. Of interest is identifying potentially modifiable factors that can be employed to improve adoption of best practices (Wisdom et al., 2014). Thus far, no empirical studies have been done to address this. This research aims to fill the gap by providing a set of factors identified by practitioners as driving the organizational adoption of automation. The results suggest avenues for further research and offer best practices to implement automation in tourism.

Increasing implementation of automation has brought global concerns over the future of jobs in various sectors. To ensure that the transition to automation in travel and tourism will be made in a responsible and accountable manner, this study conceptualizes how automation, found to be driven largely by labor shortage, can be used to promote decent work. Utilizing Grounded Theory to analyze data from in-depth interviews and focus group discussions with industry practitioners, this study provides rich descriptions of the transformation brought by automation to companies, employees, and wider society and develops a theoretical model to explain ‘Decent Work through Automation’ (DW–A). In doing so, this study opens a pathway for further research on technology and decent work in tourism, including second- and third-order impacts of emerging technology. The paper offers practitioners and policymakers guidelines for responsible adoption of automation.

Hack Hospitality brought together Surrey’s research team with experts in AI and robotics, as well as thought leaders in the hospitality and travel industry to envision how to best implement chatbots for hospitality. Workshop participants engaged in insightful discussion and collaborative exercises using Personas and Scripts to codesign human-chatbot conversations and think about the benefits and challenges of implementing chatbots in the travel and hospitality industry.