Erin Chao Ling obtained her MS.c degree in Management at the University of Bristol, UK. and BS.c. degree in Tourism Management at Hainan University, China. Currently, she is a PhD researcher at the School of Hospitality and Tourism Management, University of Surrey, UK. Her research interests focus on artificial intelligence in the travel industry, intelligent digital assistants, human-chatbot interaction, recommender system, consumer satisfaction, travel behaviour and decision making. Also, Erin has started to serve International Federation for Information Technologies and Travel & Tourism (IFITT) as the Communication Officer since 2018.
University roles and responsibilities
- Student Mentor
Affiliations and memberships
- Artificial Intelligence (AI) in the Travel Industry
- Human-Chatbot Interaction
- Intelligent Digital Assistant
- Recommender System
- Travel Behaviour and Decision-making
- Consumer Satisfaction
- Information and Communication Technology (ICT)
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.
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.