Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality 9781787566880, 9781787566873, 9781787566897

Using a combination of theoretical discussion and real-world case studies, this book analyses the use of robotics, artif

419 108 5MB

English Pages [297] Year 2019

Report DMCA / Copyright


Polecaj historie

Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality
 9781787566880, 9781787566873, 9781787566897

Table of contents :
Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality
List of Tables
List of Figures
About the Contributors
Introduction: RAISA in Future Travel-related Industries
SECTION 1: Theoretical Issues of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality
Chapter 1: Conceptual Framework of the Use of Robots, Artificial Intelligence and Service Automation in Travel, Tourism, and Hospitality Companies
Raisa Technologies in TTH
Service Automation in TTH
Artificial Intelligence in TTH
Robots in TTH
Raisa in TTH – A Conceptual Framework
Concluding Remarks
Chapter 2: Economic Fundamentals of the Use of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality
Economic Fundamentals of RAISA – A Framework
Cost-benefit Analysis of Investing in RAISA Technologies
Financial and Non-financial Benefits of RAISA Adoption
Financial and Non-financial Costs of RAISA Adoption
Human Employees and RAISA Technologies – Substitution Versus Enhancement
What is the Adoption of RAISA Technologies Worth?
Chapter 3: Self-service Technologies in the Travel, Tourism, and Hospitality Sectors: Principles and Practice
Definition and Classifications of SSTs
Benefits and Drawbacks from SST Implementation
Benefits for Service Providers
Benefits for Customers
Drawbacks for Service Providers
Drawbacks for Customers
SST Adoption and Usage by Customers
Value Co-creation and Customer Roles in SSTs
Convenience Seeker Role
Motivated Worker Role
Judge Role
Enforced Worker Role
Unskilled Worker Role
Assistance Provider Role
Conclusion and Agenda for Further Research
Chapter 4: Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality: A Conceptual Framework
Definition and Main Characteristics of Attitudes
Customer Attitudes Toward Technology
Customer Attitudes Toward Robots in TTH: Definition and Characteristics
Chapter 5: Making Sense of Robots: Consumer Discourse on Robots in Tourism and Hospitality Service Settings
Literature Review
Attitudes toward Service Robots
Technology Sensemaking
Technology Ideology
Chapter 6: Chatbot Adoption in Tourism Services: A Conceptual Exploration
Fundamentals of Chatbots
Chatbot Intelligence
Chatbots in the Tourism and Hospitality Industry
Chatbots in the Airline Industry
Chatbot Challenges
Theoretical Background
Institutional Theory
Mimetic, Coercive, and Normative Isomorphism
Organizational Learning Theory
Chapter 7: The Impact of Robots, Artificial Intelligence, and Service Automation on Service Quality and Service Experience in Hospitality
The Influence of RAISA on Service Quality and Service Experience
RAISA and Service Quality – Implications for the Future
Chapter 8: Greggg: A Scalable High-performance, Low-cost Hospitality Robot
Literature Review
Hospitality Robots in Airports and Hotels
Hospitality Robots in Museums
Tele-operated Hospitality Robots
Limitations of Hospitality Robots
Hardware Architecture of Greggg
Hardware and Software Integration
Use Case Actors
Use Case Events
Software Architecture of Greggg
Tasks Performed by Greggg
Future Work
Appendix A. Hardware Components Listed in Fig. 4.
SECTION 2: Application of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality
Chapter 9: Robots, Artificial Intelligence, and Service Automation in Hotels
Guest Experience, Guest Cycle, and RAISA
Application of RAISA During the Guest Cycle
Application of RAISA at the Pre-arrival Stage
Application of RAISA at the Arrival Stage
Application of RAISA during the Occupancy Stage
Application of RAISA during the Departure Stage
Application of RAISA during the Assessment Stage
Practical Implications
Pedagogical Implications
Research Implications
Chapter 10: Robots, Artificial Intelligence, and Service Automation in Restaurants
Artificial Intelligence in Restaurants
Voice-activated Technologies
Biometric Identification
Robots in Restaurants
Robots at the Back of the House
Front of the House Restaurant Robots
Industry Insights
Robotic Bars
Service Automation
Automated Restaurant Systems
Automated Food Delivery
Chapter 11: Robots, Artificial Intelligence, and Service Automation in Travel Agencies and Tourist Information Centers
Travel Agencies Operations
RAISA Technologies and Travel Agencies – Current Application
RAISA Technologies in the Front Office Operations of TAs
RAISA Technologies in the Back Office Operations of TAs
RAISA Technologies in TICs
Future Development of RAISA in Travel Agencies’ Operations
AI for Personalization and Customization
Integrated Smart Eco-system
More Intelligent Internal Systems, Relying on Business Analytics and AI
Concluding Remarks
Chapter 12: Robots, Artificial Intelligence, and Service Automation to the Core: Remastering Experiences at Museums
A Chronological Framework of Museums’ Orientations: From Preservation-oriented to Technology Driven
RAISA in Service Design: Participatory and Multisensory Experiences
RAISA Approach to Preservation Management
Museums as Labs of the Future Culture: Skillset for the RAISA Millennium
Chapter 13: The Role of Robots, Artificial Intelligence, and Service Automation in Events
RAISA and the Event Literature
Understanding the Context of Events
Applicability of RAISA to Events
Event Experience

Citation preview

Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality

PRAISE FOR ROBOTS, ARTIFICIAL INTELLIGENCE AND SERVICE AUTOMATION IN TRAVEL, TOURISM AND HOSPITALITY This is the very first book that focuses on robots, artifical intelligence and automation technologies (RAISA) in tourism and does this from a social science perspective. It comprehensively covers the theoretical problems of RAISA adoption in tourism, principles of service automation, attitudes towards robots, impacts of RAISA on business processes and competitiveness, and the use of chatbots. Furthermore, it shows the practical issues that arrise from the application of RAISA technologies in various tourism sectors such as hotels, restaurants, travel agencies, tourist information centres, events and museums. What I find particularly valuable is that the book delves deep into the economic aspects of RAISA technologies in tourism - a problem which has been quite neglected in research. Numerous photographs and figures are used to visualise authors’ ideas. The book is valuable for practitioners, researchers, and students. Professor Dimitrios Buhalis Head of Department of Tourism and Hospitality, Bournemouth University, UK This book is a welcomed addition to the travel, tourism, and hospitality literature. It discusses a timely and increasingly important issue of robots, artificial intelligence, and service automation and provides the readers with the most comprehensive collection of knowledge on these topics. The book looks at the issue from both theoretical as well as practical perspectives and provides a wide selection of current examples. With contributions from more than 30 authors from all over the world, this book is worth reading not just for tourism students, academics and practitioners, but also for anyone in service industries. Tourism and hospitality will drastically transform as the technologies discussed in this book develops, but so will many other service fields. Other service industries can also learn from the various artificial intelligence, service automation and robotic issues explored in this book. Juho Pesonen, PhD Head of e-tourism research, University of Eastern Finland The book provides theoretical underpinning and practical evidence of the application and impacts of robots, artificial intelligence and service automation (RAISA) in various tourism sectors including hotels, restaurants, museums, events and tourism information centres. The book includes chapters contributed by international scholars, all recognised in their own field. The book chapters discuss the implications of RAISA from both the tourism demand and supply perspective such as technology adoption, tourists’ reactions and attitude towards RAISE, operators’ soft and hard benefits and costs. The book is a valuable reading for tourism scholars, students and professionals alike. Professor Marianna Sigala Director of the Centre for Tourism and Leisure Management (CTLM), University of South Australia Business School, Australia The book embraces the frontiers of robot development in hospitality and tourism, which can deliver useful insights to both academic researchers and university students. This book takes readers on a modern and advanced journey to conceptual frameworks of robotrelated technologies and their applications to hotels, restaurants, travel agencies, tourist information centers, and other related fields. It is a must-read primer for anyone who would like to understand the latest changes brought by robots to the hotel and tourism industry. This book indeed does a good job to start the topic with conceptual frameworks, connecting theory with principles and practice. Rob Law, PhD, CHE Professor of Technology Management, The Hong Kong Polytechnic University, China

Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality


STANISLAV IVANOV Varna University of Management, Bulgaria

CRAIG WEBSTER Ball State University, USA

United Kingdom – North America – Japan – India – Malaysia – China

Emerald Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2019 Copyright © 2019 Emerald Publishing Limited Reprints and permissions service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. Any opinions expressed in the chapters are those of the authors. Whilst Emerald makes every effort to ensure the quality and accuracy of its content, Emerald makes no representation implied or otherwise, as to the chapters’ suitability and application and disclaims any warranties, express or implied, to their use. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-78756-688-0 (Print) ISBN: 978-1-78756-687-3 (Online) ISBN: 978-1-78756-689-7 (Epub)

To our loving families and our future robotic colleagues S.I. and C.W.

This page intentionally left blank


List of Tables


List of Figures


About the Contributors


Acknowledgments Introduction: RAISA in Future Travel-related Industries Craig Webster and Stanislav Ivanov



Section 1: Theoretical Issues of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality Chapter 1  Conceptual Framework of the Use of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality Companies Stanislav Ivanov and Craig Webster


Chapter 2  Economic Fundamentals of the Use of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality Stanislav Ivanov and Craig Webster


Chapter 3  Self-service Technologies in the Travel, Tourism, and Hospitality Sectors: Principles and Practice Petranka Kelly, Jennifer Lawlor and Michael Mulvey


Chapter 4  Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality: A Conceptual Framework Velina Kazandzhieva and Hristina Filipova


viii   Contents

Chapter 5  Making Sense of Robots: Consumer Discourse on Robots in Tourism and Hospitality Service Settings Ulrike Gretzel and Jamie Murphy


Chapter 6  Chatbot Adoption in Tourism Services: A Conceptual Exploration Dandison C. Ukpabi, Bilal Aslam and Heikki Karjaluoto


Chapter 7  The Impact of Robots, Artificial Intelligence, and Service Automation on Service Quality and Service Experience in Hospitality Nikola Naumov


Chapter 8  Greggg: A Scalable High-performance, Low-cost Hospitality Robot Sam R. Thangiah, Michael Karavias, Ryan Caldwell, Matthew Wherry, Jessica Seibert, Abdullah Wahbeh, Zachariah Miller and Alexander Gessinger


Section 2: Application of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality Chapter 9  Robots, Artificial Intelligence, and Service Automation in Hotels Georgina Lukanova and Galina Ilieva


Chapter 10  Robots, Artificial Intelligence, and Service Automation in Restaurants Katerina Berezina, Olena Ciftci and Cihan Cobanoglu


Chapter 11  Robots, Artificial Intelligence, and Service Automation in Travel Agencies and Tourist Information Centers Maya Ivanova


Chapter 12  Robots, Artificial Intelligence, and Service Automation to the Core: Remastering Experiences at Museums Nuria Recuero Virto and Maria Francisca Blasco López


Chapter 13  The Role of Robots, Artificial Intelligence, and Service Automation in Events Alfred Ogle and David Lamb255 Index


List of Tables

Chapter 1 Table 1. Impacts of RAISA Introduction on Business Processes of TTH Companies


Chapter 2 Table 1. Sample Monetary and Non-monetary Variables Used to Measure Input, Output, and Productivity in Travel, Tourism, and Hospitality


Chapter 3 Table 1. Examples of SSTs Table 2. SST User Roles and Perceptions

61 70

Chapter 5 Table 1.

Exemplary Technology Ideology Comments


Literature on Chatbots in Different Contexts


Main Examples of RAISA Adoption in Hotel Companies Key Features of NSCI Technologies in Hospitality

161 166

Chapter 6 Table 1.

Chapter 9 Table 1. Table 2.

Chapter 10 Table 1.

Chatbot Capabilities for Different Stages of the Guest Cycle


Chapter 11 Table 1. Table 2.

Tourist Consumer Behavior Activities Current Technologies Application

223 225

This page intentionally left blank

List of Figures

Chapter 1 Fig. 1. Self-check-in Kiosk at Brussels Airport, Belgium Fig. 2. Baggage Self-drop-off Counter at Brussels Airport, Belgium Fig. 3. Communication Kiosk, Munich Airport, Germany Fig. 4. Self-service Kiosk at McDonalds, Porto, Portugal Fig. 5. Table Menu Kiosk at Olive Garden Restaurant, Kissimmee, FL, USA Fig. 6. Conveyor Belt, Incanto Restaurant, Bourgas, Bulgaria Fig. 7. Automated Sleeping Cabins at Munich Airport, Germany Fig. 8 a, b. Kiosk for Automated Donations at Guildford Cathedral, UK Fig. 9. Self-check-in Kiosk for Hotels Fig. 10. Self-boarding Facilities with Facial Recognition at Gatwick Airport, UK Fig. 11a–d. Plovdiv City Concierge Chatbot by Fig. 12. Digital Receptionist, Hotel Aqua, Bourgas, Bulgaria Fig. 13. Pepper Robot, Munich Airport, Germany Fig. 14. Amy Food-serving Robot Fig. 15. Robotic Lawnmower Fig. 16. Conceptual Framework of the Use of RAISA Technologies in TTH Fig. 17. Solutions to the Labor Force Crisis

11 12 12 13 13 14 14 15 15 16 17 18 20 20 21 22 28

Chapter 2 Fig. 1.

Economic Framework of RAISA Adoption in TTH


Conceptual Framework of Customer SST Adoption


Chapter 3 Fig. 1.

Chapter 4 Fig. 1. Interactions Between the Components of Consumer Attitudes Toward Robots in TTH Fig. 2. Dynamics of Consumer Attitudes and Behavior Toward Robots in TTH

86 87

xii    List of Figures

Chapter 5 Fig. 1.

The Ideological Field of Technology


Chapter 6 Fig. 1. Conceptual Framework of Tourism Bot from Input Query to Output Reply111 Fig. 2. Sample Conversation with a Hotel-booking Chatbot 112 Fig. 3. Sample Conversation with Restaurant Chatbot 112 Fig. 4. Sample Conversation with a Flight-booking Chatbot 113 Fig. 5. Conceptual Framework of Chatbot Adoption 116

Chapter 7 Fig. 1. Impacts of RAISA on Service Quality in Hospitality


Chapter 8 Fig. 1. High-level Components of Greggg Fig. 2. The Base Hardware Architecture of the Greggg Robot Fig. 3. UML Diagram for the Greggg Hardware Fig. 4. The Greggg Robot Fig. 5. Use Case Diagram for the Greggg Robot Fig. 6. Sensor Diagram for Greggg Fig. 7. The Greggg Dashboard with Vision Recognition Fig. 8. UML Class Diagram of the Flow of Information Used for Image Processing Fig. 9. Greggg’s Tour Route on Campus Fig. 10. Waypoints Used by Greggg Fig. 11. A NARF Image of a Room

139 140 141 142 143 144 145 146 147 148 149

Chapter 9 Fig. 1

Guest Cycle


Chapter 10 Fig. 1. Employee Clocking in on a Biometric Device Fig. 2. Robot Working as a Concierge at a Japanese Sushi Restaurant Fig. 3. Robotic Arm Preparing Sushi Fig. 4. Robotic Host from Tanuki Restaurant in Dubai, UAE Fig. 5. Robot Penny Delivers Food at a Restaurant Fig. 6. Mr Juan Higueros, Co-founder and COO of Bear Robotics Fig. 7. Bionic Bartenders on Symphony of the Seas Cruise Ship Fig. 8. Customer Ordering Food on a Tablet at a Restaurant Fig. 9. Self-ordering Kiosk with a Touch-screen at a Restaurant 

192 195 197 199 200 201 205 207 208

List of Figures    xiii Fig. 10. Sushi on a Conveyor Belt at a Japanese Restaurant Fig. 11. Two Delivery Robots Crossing the Street in Tallin, Estonia

209 210

Chapter 11 Fig. 1.

Self-services Information Kiosk in Front of a TIC


Chapter 12 Fig. 1. The Use of Drones to Record Endangered Archaeological Sites in Peru Fig. 2. Berenson Fig. 3. RAISA Examples in the Design of the Museum “Visitor Journey Map” Experience Fig. 4. RAISA Approach to Preservation Management of Heritage Resources

240 245 245 247

Chapter 13 Fig. 1. Fig. 2.

Event Stakeholders-event Experience (EE) Linkage Internet of Things Events Applications

258 260

This page intentionally left blank

About the Contributors

Bilal Aslam is a PhD student in Marketing at the University of Jyväskylä School of Business and Economics, Finland. His research interests include digital marketing/advertising, display advertising, mobile advertising, sponsored search advertising, programmatic buying, and artificial intelligence. He has extensive industry experience in a variety of marketing roles in various companies. To his last position, he was Brand Manager in a fashion retail company. His most recent publication has appeared in Telematics and Informatics. Katerina Berezina, PhD, CHTP, CRME, CHIA, is an Assistant Professor in the Department of Nutrition and Hospitality Management at the University of Mississippi. Dr Berezina’s research interests are in the areas of information technology in hospitality and tourism, electronic distribution, and revenue management. She serves as the Managing Editor of the Journal of Hospitality and Tourism Technology and holds a Secretary position on the board of the International Hospitality Information Technology Association. Dr Berezina is also a Coordinator of two university programs that integrate real-life hotel software into the hospitality curriculum: innRoad University Program and M3 Link University Program. Maria Francisca Blasco López is Dean of Commerce and Tourism Faculty and Titular Professor at the Universidad Complutense de Madrid. She has a PhD in Marketing and has a degree in Communication Studies. She is a researcher in RETO H2020 Program in Development of Neuromarketing Metrics. She is Director of Postgraduate Programs: Master in Marketing Management and Master in Neuromarketing and Consumer Behaviour and Co-author of Marketing del Turismo Cultural (ESIC, 2016 ) and Employer Branding (Pearson, 2018) between others books. She is Editor at Journal Cuadernos de EstudiosEmpresariales and Associate Editor at Journal of Business and Management Science. Her research interests are tourism marketing, consumer behavior, neuromarketing, employer branding, and technologies in marketing published in journals like Journal of Hospitality and Tourism Management, Tourism Review, Physiology & Behavior, Soft Computing, BRQ Business Research Quarterly, Procedia Computer Research, Lecture Notes in Artificial Intelligence, Universia Business Review, etc. Ryan Caldwell is a Software Engineer at Raytheon Company in State College, Pennsylvania. He graduated from Slippery Rock University with a Bachelor of Science in Computing, specializing in Computer Science.

xvi    About the Contributors Olena Ciftci, MS, CHIA, is a Doctoral student in the Department of Nutrition and Hospitality Management at the University of Mississippi. She holds two Master’s degrees: one in Mathematics with a minor in Informatics and another one in Hospitality Management. Prior to joining a doctoral program at the University of Mississippi, Ms. Ciftci worked as a Hospitality Data Analyst in the M3 Center for Hospitality Technology and Innovation at the University of South Florida Sarasota-Manatee. Her research interests include information technology in hospitality and tourism, big data analytics, revenue management, and consumer behavior. Cihan Cobanoglu, PhD, CHTP, is the McKibbon Endowed Chair Professor in the College of Hospitality and Tourism Leadership (CHTL) at the University of South Florida Sarasota-Manatee, who also serves as the Director of the M3 Center for Hospitality Technology and Innovation and Coordinator of International Programs for CHTL. He is a renowned hospitality and tourism technology expert. His research involves the use and impact of technology in the hospitality industry. Dr Cobanoglu is the Editor of the Journal of Hospitality & Tourism Technology, the Journal of Global Business Insights, Co-Editor of the Journal of Global Education and Research, and Associate Editor of Tourism Review. He is also currently serving as the President of Association of North America Higher Education International. Hristina Filipova is a PhD student at the University of Economics – Varna, Bulgaria, in the Department of Economics and Organization of Tourism. She holds a Bachelor degree in International Economic Relations from the University of Economics – Varna and a Master degree in Management of Taste and Luxury Goods from the University of Reims Champagne – Ardenne, France. Her main research interests are in the field of consumer behavior in tourism, marketing, cross-cultural relations, restaurant management, tangible and intangible cultural heritage, and souvenirs. Alexander Gessinger is currently pursuing a Bachelor of Science in Computing at Slippery Rock University. His research interests are in robotics, vision processing algorithms using artificial intelligence, machine learning methodologies, and implementing benchmarking tools for high performance computing systems. Ulrike Gretzel is a Senior Fellow at the Center of Public Relations, University of Southern California and serves as the Director of Research at Netnografica, a market research company that extracts insights from online conversations. She received her PhD in Communications from the University of Illinois at Urbana-Champaign. Her research focuses on the impact of technology on human experiences and the structure of technology-mediated communication. She studies social media marketing and destination marketing, influencer marketing, and the emerging reputation economy. She has also researched the design of intelligent systems in tourism, smart tourism development, technology adoption and non-adoption in tourism organizations, tourism in technological dead zones, and the quest for digital detox experiences. She is frequently acknowledged as one of the most cited authors in tourism.

About the Contributors    xvii Galina Ilieva has PhD in Tourism and Master’s degree in English Philology and Tourism. Currently, she works as a Lecturer in English at the College of Tourism in Varna and Front Office Manager in hotel and SPA “Astera” in Golden Sands. She also lectures on marketing in tourism and technology in hospitality services in University of Economics – Varna. Her scientific fields of interest are marketing in tourism and innovative technologies, intercultural communication and encounter staff, gambling tourism, and contemporary forms of tourism. She has participated in a number of research projects such as “The Application of Innovative Technologies in Hotel Service” and “Intercultural communication as a factor for sustainable tourism” funded by the University of Economics Varna, “Update of the National Strategy for Sustainable Development of Tourism in the Republic of Bulgaria 2014–2030” funded by the Ministry of Tourism, and “Research of competitiveness of Varna Municipality as a tourist destination” funded by Municipality of Varna. Stanislav Ivanov is currently Professor and Vice Rector (Research) at Varna University of Management, Bulgaria ( Professor Ivanov is the Founder and Editor-in-Chief of the European Journal of Tourism Research ( and serves in the Editorial boards of over 30 other journals. His research interests include robonomics, robots in tourism/hospitality, revenue management, destination marketing, tourism and economic growth, political issues in tourism, etc. His publications have appeared in a range of academic journals – Annals of Tourism Research, Tourism Management, Tourism Management Perspectives, International Journal of Revenue Management, Tourism Economics, Journal of Destination Marketing & Management, Journal of Heritage Tourism, Tourism Today, Tourism, Tourism and Hospitality Research, Tourism Planning and Development, International Journal of Hospitality and Tourism Administration, Technology in Society, Journal of Economic Studies, Journal of Southern Europe and the Balkans, South-Eastern Europe Journal of Economics, and other journals. Maya Ivanova, PhD, is an Associate Professor at Varna University of Management, Bulgaria, Program Director of the School of Hospitality and Tourism Management, Editorial assistant of European Journal of Tourism Research, member of the Editorial board of Tourism Management Perspectives, and a certified IATA/UFTAA Instructor. Her research interests include: tourism and hospitality management, air transport, tourism intermediaries, and hotel chains. She is a co-editor of the Routledge Handbook of Hotel Chain Management (2016).Due to her large practical experience, she works closely with the business as a Consultant and a Trainer. Michael Karavias received his Bachelor of Science degree in Computer Science from Slippery Rock University specializing in parallel computing. He has published on improving the efficiency of Carthagene (“Consensus Clustering: A Resampling-Based Method for Building Radiation Hybrid Maps”), and has received recognition for his work in building automation for increasing efficiency thereby lowering operating costs.

xviii    About the Contributors Heikki Karjaluoto is a Professor of Marketing at the University of Jyväskylä School of Business and Economics, Finland. His research interests include customer relationship management, marketing communications, mobile communications, and retail banking. Previous publications have appeared in the Business Strategy and the Environment, Computers in Human Behavior, European Journal of Marketing, Industrial Marketing Management, and others. Velina Kazandzhieva is Associate Professor at the University of Economics – Varna, Bulgaria, in the Department of Economics and Organization of Tourism. Her research interests include tourism management, tourism economics, travel agencies and tour operators, special interest tourism, and e-tourism. Dr Kazandzhieva participated in 10 research projects, four of which as a manager, in the field of: innovative technologies in hotel services, sustainable tourism development, tourist destination competitive advantages, and youth and rural tourism. She is a member of Bulgarian Union of Scientists, Varna Chamber of Tourism, Bulgarian Union of Tour Guides. Petranka Kelly is a marketing and management professional with a wide-ranging experience including financial services, hospitality, and consumer research. She is passionate about crafting effective and innovative marketing strategies which result in a win–win situation for all parties involved. Her PhD thesis, titled “An interpretivist study of customer self-service technology usage and experiences in the tourism sector,” was completed at the Dublin Institute of Technology, Ireland. She publishes in the area of self-service technologies and the role of customers in co-creating service experiences. David Lamb is an experienced Senior Academic with management experience of both staff and curriculum. David has extensive lecturing experience at both undergraduate and postgraduate levels with on- and off-campus students. His area of expertise in research and teaching is within the discipline areas of sport events and leisure management. He is an active and competent researcher and is presently involved in a number of research projects, one of which is the application of technology and innovation in event management practice. David has worked as an academic at universities in the United Kingdom, New Zealand, and Australia. He is presently a Consultant and holds a Senior Adjunct position at the University of Notre Dame in Fremantle, Western Australia. Jennifer Lawlor is a Lecturer in Strategic Management in the School of Hospitality Management and Tourism at the Dublin Institute of Technology, Ireland. Her research interests include the role of the customer in a service organization, self-service technologies, value co-creation, and organizational change. She has presented a number of competitive conference papers in the United States, the United Kingdom, and Europe in the area of inter-firm collaboration and cooperation and self-service technologies, and has published articles in Personnel Review, International Journal of Electronic Customer Relationship Management, and Journal of Travel and Tourism Marketing. Georgina Lukanova has PhD in Tourism. Currently, she works as an Associated Professor at University of Economics – Varna. She is a Lecturer in Hotel

About the Contributors    xix Management, Restaurant Management, Service Management in Tourism, and Franchising in Hospitality. Her scientific fields of interest are management and operations in hospitality, innovative technologies in tourism and hospitality. She has participated in a number of research projects such as “The Application of Innovative Technologies in Hotel Service,” “Application of timeshare in the Bulgarian Black sea coast,” and “Tourist profiling of the municipality” funded by the University of Economics Varna. She also has experience in the hotel’s operations since she has worked in high-class hotels on the Bulgarian Black Sea coast. Zachariah Miller graduated from American University in Washington, D.C., with a Bachelor of Science in Business Administration and is currently pursuing a Post-Baccalaureate degree in Computing at Slippery Rock University. He has published on improving query time for large-scale databases using partitioning and high-performance computing. His research interests are in 3D printing, robotics, and vision processing algorithms using artificial intelligence and machine learning methodologies. Michael Mulvey is President of the Dundalk Institute of Technology. He was previously Director of Academic Affairs and Registrar at the Dublin Institute of Technology. Jamie Murphy, an Adjunct Professor with the University of Eastern Finland, has given invited/keynote presentations on six continents and taught tourism/ hospitality at Essec, Florida State University, and the Lausanne Hotel School and taught marketing at the University of Western Australia. His background includes European Marketing Manager for PowerBar and Greg Lemond Bicycles, Lead Academic for the Google Online Marketing Challenge, restaurateur, F&B manager in Yellowstone National Park, and a PhD from Florida State University. Professor Murphy’s industry and academic experience spans continents and includes hundreds of academic publications and presentations, as well as dozens of New York Times and Wall Street Journal stories. His research focuses on robots, sustainability, MOOCs, and effective Internet use by citizens, businesses, and governments. Nikola Naumov is a Senior Lecturer in Hospitality and Tourism Management at the Department of Events, Tourism, and Hospitality at University of Northampton, United Kingdom, Visiting Lecturer at ESH Paris Hotel School, France, and Visiting Assistant Professor at Meikai University, Tokyo, Japan. He has undergraduate and postgraduate degrees in Tourism and Hospitality Management and a PhD in Human Geography completed at King’s College London. His research interests include cultural and historical geographies of Eastern Europe, critical heritage studies, heritage tourism, service quality in hospitality, and innovation in tourism and hospitality management. Alfred Ogle is a Researcher and Academic specializing in Hospitality Management and the Services Industry. He runs a research consultancy in Perth, Australia and is a Sessional Academic at Edith Cowan University. An Hotelier in North America and Asia prior to his entry into academia, he enjoys boundary spanning applied research and industry collaboration. His current research interests

xx    About the Contributors include hospitality facilities management, environmentally friendly design, sustainability, and service enterprise atmospherics. Nuria Recuero Virto is Postdoctoral Researcher at Universidad Complutense de Madrid, with a special grant of Santander Bank. She holds a PhD from the Universidad Complutense of Madrid, which also awarded her a Predoctoral Scholarship (2010–2014). She was finalist of FITUR’s awards for best doctoral thesis (2013). Her specific areas of interest are: tourism marketing, employer branding, and neuromarketing. She is Co-author of two books Marketing of Cultural Tourism (ESIC, 2016) and Employer Branding: Manage Talent in 5 Steps (Pearson Education, 2018). She has extensive experience in research – more than 14 papers in international conferences and she has published in different international journals, such as Tourism Review, Journal of Hospitality and Tourism Management, among others. She is the Associate Editor of Cuadernos de Estudios Empresariales. She is Researcher in a national project on Neuromarketing RETO H2020 and in a project on cultural tourism of the Autonomous Community of Madrid. Jessica Seibert is currently pursuing a Bachelor of Science in Computing at Slippery Rock University. Her research interests are in vision processing algorithms using deep neural networks. Sam R. Thangiah is Professor and Director of the Artificial Intelligence and Robotics Laboratory of the Computer Science Department in the College of Health, Environment, and Science at Slippery Rock University. His main areas of research are robotics, artificial intelligence, machine learning, and application of evolutionary heuristics to solving vehicle routing and scheduling problems. He has published in IEEE Conference on Artificial Intelligence Applications, International Conference on Genetic Algorithms, Annals of Operations Research, International Journal of Science and Technology in the Tropics, Lecture Notes in Economic and Mathematical Systems, Journal of Mathematical and Management Sciences, and Central European Journal of Operations Research. He has written book chapters for Application Handbook of Genetic Algorithms, Practical Handbook of Genetic Algorithms and Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms. He is also on the board of editors for the Journal of Medical and Health Informatics. Dandison C. Ukpabi is a PhD student in Marketing at the University of Jyväskylä School of Business and Economics, Finland. His most recent publications have appeared in Telematics and Informatics and in Tourism Management Perspectives. He has presented papers in conferences such as ENTER e-Tourism conference, Bled eConference, and the European Marketing Academy Conference. His research interests are e-tourism, digital marketing and social media, relationship marketing, and marketing strategy. Abdullah Wahbeh is an Assistant Professor in the Department of Computer Science at College of Health, Environment, and Science. He holds a DSc and MSc in Information Systems from Dakota State University. He worked as an Adjunct Instructor in The College of Business and Information Systems at Dakota State

About the Contributors    xxi University. Dr Wahbeh serves as a reviewer for a number of conferences such as the Americas Conference on Information Systems (AMCIS), European Conference on Information Systems, and Hawaii International Conference on System Sciences (HICSS). His research has been presented at many conferences such as HICSS and AMCIS. His current research interests include socio-technical design, systems analysis and design, design science research, healthcare analytics, and mobile health and self-care technologies. Craig Webster is an Associate Professor in the Department of Management at Ball State University, USA. He has taught at Binghamton University, Ithaca College, the College of Tourism and Hotel Management, and the University of Nicosia. His research interests include the political economy of tourism, public opinion analysis, and human rights. Dr Webster is the Editor-in-Chief of Tourism Today, has published in many peer-reviewed journals internationally, and has co-edited the book Future Tourism: Political, Social, and Economic Challenges (2012), an edited book published by Routledge. He currently teaches courses in Hospitality Management at Ball State University’s Miller College of Business. Matthew Wherry graduated from Slippery Rock University, Pennsylvania, with a Bachelor of Science in Computing specializing in computer science. He is currently working as a Junior Software Developer/Analyst for Premier Comp Solutions.


The editors would like to thank all the authors who submitted their high-quality chapters and made this book possible. Special thanks go to the reviewers of the chapters who provided valuable feedback to improve the manuscripts. They also thank the robotics companies that provided photos and interviews to inform some of the chapters. The publishing team at Emerald provided invaluable support and guidance throughout. Of course, nothing would have been possible without the help of our loving families who lived through this project with us. Thank you!

Introduction: RAISA in Future Travel-related Industries Craig Webster and Stanislav Ivanov

A zeitgeist was the instigator of this book. While humans seem to have a fascination with robots, the incorporation of robots, artificial intelligence, and service automation (RAISA) into the economy in recent years has accelerated. In just a few years, technologies have increased their effectiveness and the technologies have entered into the economy in effective ways. While robots have been used extensively in manufacturing for decades, it has only been in the past few years that the service industries have seen a massive incursion of new technologies, changing the ways in which many of us do business or interact with businesses. Robots and artificial intelligence fascinate humans and are now advanced enough to replace human labor or augment human labor in the service sector, namely the travel, tourism, and hospitality sectors. In 2015, the Henn-na Hotel opened in Japan, making it the first hotel to be almost entirely staffed by robots. This meant that the technologies that would enable hotels to function mostly using RAISA were a pragmatic possibility, even if the first hotel of its kind was marketed in ways as a novelty to attract the market of robot enthusiasts. Karel Čapek had invented the concept of the robot shortly after World War One and about a century later, a hotel was staffed almost entirely by robots. In less than a century, robots went from a concept to a pragmatic labor force, even if the current version of robots we use will seem clunky, unintelligent, and awkward just a few years from now. The technological ability to make a hospitality enterprise run using mostly mechanized labor and artificial intelligence has been realized. In this edited book, we deal in depth with various issues related to this, the massive replacement and augmentation of human labor by RAISA. The book is divided into two major sections. The first section concentrates on the theoretical issues of RAISA in travel, tourism, and hospitality. The second section of the book delves into the practical applications of RAISA in travel, tourism, and hospitality. As such, the first section of the book gives insights into how new technologies can and should be applied in the economy in theory and the second section gives insights into the practicalities of such technologies in specific subsectors of the travel, tourism, and hospitality industries.

Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality, 1–3 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78756-687-320191017

2    Craig Webster and Stanislav Ivanov In the first theoretical section, Ivanov and Webster’s Chapter 1 introduces the most basic of concepts of RAISA and their incorporation into travel, tourism, and hospitality companies, illustrating the major theoretical and practical issues in their creeping incorporation into travel-related industries. In Chapter 2, Ivanov and Webster focus upon the economic fundamentals of the incorporation of RAISA into travel, tourism, and hospitality. In this chapter, the authors delve into the financial and nonfinancial costs and benefits to be considered in terms of using RAISA for the substitution and enhancement of human labor and the implications of the changeover to a more automated labor force. Kelly, Lawlor, and Mulvey discuss the basic principles of self-service technologies in tourismrelated industries in Chapter 3, highlighting the benefits and drawbacks that such technologies to service providers and customers. In Chapter 4, Kazandzhieva and Filipova delve into the attitudes and concerns of customers in the travel, tourism, and hospitality industries, presenting a theoretical framework for understanding how customers perceive and interact with robots in tourism-related industries. On a somewhat related note in the first section of the book, in Chapter 5, Gretzel and Murphy discuss the ideologies of technology and find that there is empirical evidence that certain ideologies prevail in the discourse on robots in the application of robots in service industries. In Chapter 6, Ukpabi, Aslam, and Karjaluoto concentrate upon a very specific technology, the chatbot, and theoretical issues linked with the application of the chatbot in supplying services in the tourism industry. Chapter 7 by Naumov gives a comprehensive overview of the academic literature on RAISA and industry to discuss the consensus of the academic findings and discuss the issue of the difficult balance business have to make in finding the right mix of human and digital interactions. In the final chapter in the first section, Chapter 8, Thangiah and his co-authors discuss the creation of and the capability of the Greggg robot, a robot with the capability to work within the hospitality industry. This should be a very interesting chapter for those interested in some of the key practical and theoretical issues of building a robot to satiate customer demands. In the second part of the book, the section delving into the application of RAISA in travel, tourism, and hospitality, authors deal with practical issues of incorporating RAISA technologies into industry. The chapters discuss the academic literature, the practical issues, and suggested strategies for incorporating RAISA into hotels, restaurants, travel agencies, and tourist information centers, museums, and events. The second part begins with Chapter 9 by Lukanova and Ilieva, a chapter focusing upon the incorporation of RAISA in hotels. Chapter 9 examines the academic literature and case studies linked with the implementation of RAISA in service industries to examine how hotel companies will have to consider the incorporation of RAISA technologies during each of the five stages of the guest cycle (pre-arrival, arrival, stay, departure, and assessment) to satisfy hotel customers. Chapter 10 by Berezina, Ciftci, and Cobanoglu, in comparison, focuses upon the incorporation of RAISA in restaurants by reviewing the academic literature and interviewing Juan Higueros, Chief Operations Officer of Bear Robotics. The findings discuss the possibilities of the implementation of robotics and how they can be and will be incorporated into all aspects of the

Introduction    3 restaurant (front and back of house) and which tasks will be soon relegated to robotic labor. In contrast, in Chapter 11, Ivanova discusses the current usage of RAISA in travel agencies and tourist information centers and discusses the potential for such technologies and how they will be incorporated into such establishments in the near future. In Chapter 12, Recuero Virto and Blasco Lopez focus upon RAISA and how it will increasingly be incorporated into museums, showing that the increasing use of RAISA in museums will change the museum experience and change some of the skills of the workforce that will be working in museums in the near future. The concluding chapter, Chapter 13 by Ogle and Lamb, focuses on the event industry and the incorporation of RAISA into that industry. All in all, this book should give those in academia and industry a good background in the incorporation of RAISA into travel, tourism, and hospitality. The first eight chapters, those comprising the first section of the book, should give readers good insight into the current state of the art in industry and academia on RAISA and its incorporation into industry, in general, and travel and tourismrelated industries, in particular. The final chapters (Chapters 9–13) deal more specifically with how RAISA will be incorporated into specific subsectors of the industry (hotels, restaurants, travel agencies and tourist information centers, museums, and events) that have somewhat different characteristics and expectations from customers. As such, the second part of the book should be especially interesting and useful for practitioners in the field who may want to see guidance from the consensus of those who consider such issues and insight into what the authors’ see as possibilities and practical elements of the incorporations of the technologies into industry in the near future. It has been a pleasure for us to create this book and we are thankful to those who contributed to it. We expect that the book can inform readers about the current state of the art and give some insight into how RAISA will be incorporated into operations in the near future. We expect that the readers will see the value in the contributions and hope that the components of the book are informative, interesting, and give readers an opportunity to envision a future in which RAISA is much more prolific in our lives and workplaces, especially in travel, tourism, and hospitality.

This page intentionally left blank

Section 1 Theoretical Issues of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality

This page intentionally left blank

Chapter 1

Conceptual Framework of the Use of Robots, Artificial Intelligence and Service Automation in Travel, Tourism, and Hospitality Companies Stanislav Ivanov and Craig Webster Introduction Tourism and hospitality are usually referred to as a “people business” – services provided by human service providers (receptionists, housekeepers, waiters, cooks, bartenders, guides, drivers, sales agents, event organizers, supervisors, managers, etc.) for human customers (travelers, passengers, tourists, guests, and event attendees). The traditional labor-intensive nature of the business has been necessary because of the complicated nature of many of the tasks required (e.g., changing the sheets on a bed) and nuances in communications between customers and service providers generally required a human to make judgments, interpret information, and respond to tasks that are not part of standard operational procedures. However, the technological developments at the end of the twentieth and beginning of the twenty-first century such as the Internet, websites, social media, mobile applications, virtual/augmented/mixed reality, chatbots, robotics, and self-service kiosks (Benckendorff, Xiang, & Sheldon, 2019), created an important technological layer in the interaction between companies in travel, tourism, and hospitality (TTH) and their customers. This technological layer reorganized the “human–human” interactions in TTH into “human–machine,” “human– computer,” and, more recently, into “human–robot” interactions. Moreover, the technological layer started to transform the business models of TTH companies – they began to use robots, artificial intelligence, and service automation (RAISA) technologies to design and deliver services to their human guests (Ivanov, Webster, & Berezina, 2017). Because of technological advances, the “high-touch” tourism businesses have been able to add a “high-tech” component (Naisbitt, Naisbitt, & Philips, 2001). Customers take greater role and responsibility in the

Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality, 7–37 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78756-687-320191001

8    Stanislav Ivanov and Craig Webster service-delivery process and evolve into “prosumers” (= “producers” + “consumers”) of TTH services (Ivanov, 2019), while some authors claim that robots would become and should be treated as customers too (Ivanov, 2018; Ivanov & Webster, 2017a) or even have their own rights (Gunkel, 2018). The advances in RAISA technologies (Bhaumik, 2018; Ertel, 2017; Miller & Miller, 2017; Neapolitan & Jiang, 2018; Russell & Norvig, 2016) allowed their introduction in various sectors of the economy and society such as manufacturing and smart factories (Askarpour, Mandrioli, Rossi, & Vicentini, 2019; Cubero, 2007; Diez-Olivan, Del Ser, Galar, & Sierra, 2019; Low, 2007; Pires, 2007; Wang, S., Wan, Zhang, Li, & Zhang, 2016), agriculture (Driessen & Heutinck, 2015; Slaughter, Giles, & Downey, 2008; Wolfert, Ge, Verdouw, & Bogaardt, 2017; Xiong, Peng, Grimstad, From, & Isler, 2019), warehousing and supply chain management (Mahroof, 2019; Wurman, D’Andrea, & Mountz, 2008), and autonomous vehicles (Fagnant & Kockelman, 2015; Maurer, Gerdes, Lenz, & Winner, 2016), among others. RAISA are also used by service industries (Huang & Rust, 2018; van Doorn et al., 2017; Wirtz et al., 2018), in education (Ivanov, 2016; Timms, 2016; Walkington & Bernacki, 2019), journalism (Clerwall, 2014; Latar, 2018), for trading on financial markets (Dunis, Middleton, Karathanasopolous, & Theofilatos, 2017), and provision of legal services (Remus & Levy, 2015). Robots assist surgeons in medical operations (Kaur, 2012; Mirheydar & Parsons, 2013; Schommer, Patel, Mouraviev, Thomas, & Thiel, 2017), while military drones are used for surveillance and strikes on enemy targets (Crootof, 2015; Koslowski & Schulzke, 2018; Sparrow, 2007). In April 2019, the first academy book written by artificial intelligence (AI) was published by Springer (Writer, 2019). Social robots enter our lives as companions (Nørskov, 2016; Royakkers & van Est, 2016), while sex robots redefine the meaning of love and sex (Cheok, Devlin, & Levy, 2017; Danaher & McArthur, 2017; Lee, 2017). Chatbots already take a significant share of the communication between companies and their customers not only for provision of basic information about offers, but for actual sales and customer support as well (Hill, Ford, & Farreras, 2015; Xu, Liu, Guo, Sinha, & Akkiraju, 2017). Companies adopt RAISA not only to decrease costs, eliminate waste, and improve productivity, economic efficiency, and financial bottom line, but also to streamline operations, design service experiences, and boost revenues as well, which leads to profound transformations in their business models and the nature of work (Agrawal, Gans & Goldfarb, 2018; Corea, 2017; Davenport, 2018; Daugherty & Wilson, 2018; Makridakis, 2017; Talwar, 2015; Talwar, Wells, Whittington, Koury, & Romero, 2017; Webster & Ivanov, 2020). Researchers and business leaders expect that the adoption of robotics, AI, automation technologies, Industry 4.0 (Schwab, 2016; Skilton & Hovsepian, 2018) and the Internet of things (Sendler, 2018) will speed up in the future, fueled by technological progress, the plummeting prices of these technologies and the low birth rates in developed economies (Ivanov & Webster, 2018). In the long run, this process will result in greater automation of production of goods and services, until most of the goods and services are delivered by RAISA technologies, and not by human employees – an economic system known as “robonomics” (Ivanov, 2017).

Conceptual Framework of the Use of Robots    9 TTH industries are not an exception to the adoption of RAISA (Collins, Cobanoglu, Bilgihan, & Berezina, 2017; Ivanov et al., 2017; Kuo, Chen, & Tseng, 2017; Murphy, Hofacker, & Gretzel, 2017; Murphy, Gretzel, & Pesonen, 2019). For example, tourists can search for travel information and book a trip via a chatbot (Nica, Tazl, & Wotawa, 2018). Tourists can also use virtual reality to see the attractions at the destination and the hotel they would stay. The destination advertisements they see while visiting various websites and the personalized prices they pay (Ivanov, 2019) would be determined by AI algorithms on the basis of their behavioral characteristics. At the airport, their trip is facilitated by selfcheck-in machines, self-service baggage drop-off, and automated passport control with face recognition (del Rio, Moctezuma, Conde, de Diego, & Cabello, 2016; Gures, Inan, & Arslan, 2018; Kazda & Caves, 2015; Ueda & Kurahashi, 2018). From the airport, they can reach their hotel by an autonomous vehicle (Cohen & Hopkins, 2019). Upon arrival, they are greeted at the entrance by a robotic porter, they can check-in at a self-service kiosk (Kim & Qu, 2014) and enter their rooms with a mobile application on their smartphones (Cheong, Ling, & The, 2014; Torres, 2018). Within the room, they could control the smart technologies via a mobile phone, a tablet, or a voice-controlled digital assistant. A robotic fish swims in an aquarium. The room service order is delivered by a robot. Robots clean the floors and swimming pools, and cut the grass at hotels’ green areas. In the restaurant, tourists can order their food and drinks through a kiosk or tablet on the table, or take a sushi bowl from a conveyor belt (Collins et al., 2017; Kim, Christodoulidou, & Choo, 2013). Augmented and mixed reality applications will help them see and choose their dish in an interactive way. They can receive information about the destination and offered tours, and book a suitable service from a kiosk in front of the office of a local tourist information center or a travel agency. They can have their pizza ordered through chatbots or voice-controlled digital assistant, and delivered by a drone or an autonomous car (Lui, 2016) while checking the status of their order through a mobile app. And, ultimately, they may have their trip booked before they have even thought of it – their personal digital assistant with strong predictive analytics features may identify the need for a holiday for them, check suitable dates according to tourists’ schedules, stored in assistant’s memory, search for appropriate destination according to the search behavior, preferences, and personality of their owners, and book flights and hotels. Most consumers currently would feel a bit uneasy about putting so much trust into AI, relying upon a computer’s algorithms and calculations to make such judgments and plan such a travel. While technology has not yet reached the stage where this is possible, sooner or later RAISA technologies will take over much of the decision-making process in TTH. This chapter develops a conceptual framework for the use of RAISA in TTH. It looks at the issue from both supply (companies) and demand (tourists) perspectives, in order to provide a balanced account of the use of RAISA in TTH context. The rest of the text is organized as follows. The next section “RAISA Technologies in TTH” provides an overview of the scope of RAISA technologies in the current world, introducing the basic definitions, and critically evaluating the available literature on RAISA in TTH. Section “RAISA in TTH– A Conceptual

10    Stanislav Ivanov and Craig Webster Framework” develops the conceptual framework of the use of RAISA in TTH. The last section “Concluding Remarks” summarizes and concludes the chapter.

RAISA Technologies in TTH Service Automation in TTH Service automation includes a wide variety of self-service technologies (SST) defined by Meuter, Ostrom, Roundtree, and Bitner (2000, p. 50) as “technological interfaces that enable customers to produce a service independent of direct service employee involvement.” Within TTH service automation, technologies such as check-in or information kiosks at hotels and airport, mobile check-in applications, ticket machines at train and bus stations and at theme parks, vending machines for food and drinks, baggage drop-off counters and automated biometric fingerprint scanners at airports, self-ordering kiosks and conveyor belts in restaurants, etc., are included. Figs. 1–9 present some of the SSTs used by TTH companies. A common feature of service-automation technologies is that they transfer the responsibility of the service-delivery process from the company employees to the customers and transform them into prosumers of the service process. The service encounter usually takes place in a predetermined fashion with little flexibility, the service scenario (e.g., the steps through which a customer needs to go through in order to check-in at a hotel via a self-check-in kiosk) is scripted in advance. SSTs are much less expensive and sophisticated than robots, hence they are extensively used worldwide for delivering quick and cheap services to mass tourist flows (e.g., at airports, train stations, theme parks, restaurants, and hotels). Travelers are accustomed to them although they may prefer to be served by human employees rather than use kiosks. Research on SSTs in TTH has been quite extensive, because of their early adoption by the industry. It has covered SSTs in hotels (Chen, Yen, Dunk, & Widjaja, 2015; Kattara & El-Said, 2013; Kaushik, Agrawal, & Rahman, 2015; Kokkinou & Cranage, 2013; Kucukusta, Heung, & Hui, 2014; Liu & Hung, 2019; Oh, Jeong, & Baloglu, 2013; Oh, Jeong, Lee, & Warnick, 2016; Wei, Torres, & Hua, 2016), restaurants (e.g., Ahn & Seo, 2018; Susskind & Curry, 2016; Wei et al., 2016), and airports (Bogicevic, Bujisic, Bilgihan, Yang, & Cobanoglu, 2017; Kelly, Lawlor, & Mulvey, 2017), among others. From a demand-side perspective, research shows that SST shorten customer waiting times (Kokkinou & Cranage, 2013) and generally improve customers’ experience due to convenience and ease of use (Susskind & Curry, 2016), and their overall satisfaction (Bogicevic et al., 2017), although customers’ preferences toward SSTs vary by culture, hotel category and target market segments, type of technology, or stage in the occupancy cycle in a hotel (Kattara & El-Said, 2013). From a supply-side perspective, SSTs decrease the costs and service errors and improve the profitability of TTH companies (Chathoth, 2007). Of course, before introducing any service automation, TTH companies need to implement a thorough cost-benefit analysis and evaluate the financial and non-financial consequences of such an action.

Conceptual Framework of the Use of Robots    11

Fig. 1.  Self-check-in Kiosk at Brussels Airport, Belgium (Photo Credit: Stanislav Ivanov).

12    Stanislav Ivanov and Craig Webster

Fig. 2.  Baggage Self-drop-off Counter at Brussels Airport, Belgium (Photo Credit: Stanislav Ivanov).

Fig. 3.  Communication Kiosk, Munich Airport, Germany (Photo Credit: Stanislav Ivanov).

Conceptual Framework of the Use of Robots    13

Fig. 4.  Self-service Kiosk at McDonalds, Porto, Portugal (Photo Credit: Stanislav Ivanov).

Fig. 5.  Table Menu Kiosk at Olive Garden Restaurant, Kissimmee, FL, USA (Photo Credit: Stanislav Ivanov).

14    Stanislav Ivanov and Craig Webster

Fig. 6.  Conveyor Belt, Incanto Restaurant, Bourgas, Bulgaria (Photo Credit: Stanislav Ivanov).

Fig. 7.  Automated Sleeping Cabins at Munich Airport, Germany (Photo Credit: Stanislav Ivanov).

Conceptual Framework of the Use of Robots    15

Fig. 8(a and b).  Kiosk for Automated Donations at Guildford Cathedral, UK (Photo credit: Stanislav Ivanov).

Fig. 9.  Self-check-in Kiosk for Hotels (Photo Credit: Sezam24).

Artificial Intelligence in TTH Coined by John McCarthy in 1956 (Russell & Norvig, 2016, p. 17), the term AI is defined as a computer “system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, 2019, p. 15). Such an intelligent system uses various mathematical algorithms and machine learning tools and techniques to learn from data and make decisions in order to achieve a specific goal. It should be emphasized, as Whitson (2018, p. 12) correctly points out, that

16    Stanislav Ivanov and Craig Webster when defining artificial intelligence, it is important to remember that the programs, machines, and models developed by computer scientists, engineers, and cognitive scientists do not actually have human intelligence; they only exhibit intelligent behavior. That is why, as a research area AI is “the field that studies the synthesis and analysis of computational agents that act intelligently” (Poole & Mackworth, 2017, p. 3). AI is currently applied in a broad variety of fields (see Figs. 10–12) – for image and face recognition at border control, video surveillance, medicine, autonomous robots/vehicles, or on social media websites, for speech recognition in digital assistants, for automated translations, in spam filters, online games, autonomous planning and scheduling, chatbots, weather forecasts, etc. (Kaplan, 2016; Neapolitan & Jiang, 2018; Russell & Norvig, 2016). Three types of AI can be identified in the literature (Kaplan & Haenlein, 2019): (1) Artificial Narrow Intelligence (weak AI) is AI applied in a specific field where it performs much better than humans (e.g., to identify tumors on magnetic resonance imaging scans of human brain or play chess), but it cannot be used in other fields (e.g., to identify patterns in booking data of a hotel or translate text) because it does not possess the algorithms to successfully cope in the other field. This is the current stage of development of AI. (2) Artificial General Intelligence (strong AI) is (close to) human-level intelligence (Goertzel & Pennachin, 2007). It can successfully outperform humans in several fields. (3) Artificial Superintelligence is a conscious, self-aware AI that is better than humans in all fields (Bostrum, 2014). For the moment, this type of AI is within the realm of science fiction rather than reality.

Fig. 10.  Self-boarding Facilities with Facial Recognition at Gatwick Airport, UK (Photo Credit: Stanislav Ivanov).

Conceptual Framework of the Use of Robots    17

Fig. 11(a–d).  Plovdiv City Concierge Chatbot by (Photo Credit: Stanislav Ivanov). AI is one of the most admired and feared fields of science. For example, some researchers express explicit fear of AI (Bostrum, 2014; Leonhard, 2016) and consider the self-aware AI as “our final invention” (Barrat, 2013). Those who oppose it or have opposed it are not limited to the less educated and includes such notables as Stephen Hawking and Elon Musk, among many others (Clifford, 2017). Other authors seem to be very optimistic and perceive technology as the ultimate solution to all human problems, and advocate for the merger between humans and machines (Barfield, 2015; Callaghan, Miller, Yampolskiy, & Armstrong, 2017; Kurzweil, 2005; Shanahan, 2015). Of course, more than 50 shades of gray

18    Stanislav Ivanov and Craig Webster

Fig. 12.  Digital Receptionist, Hotel Aqua, Bourgas, Bulgaria (Photo Credit: Stanislav Ivanov).

exist between these two extreme opinions, but usually AI researchers take a more pragmatic approach toward AI than these two extreme views (see, e.g., Brynjolfsson & McAfee, 2014; Frank, Roehring, & Pring, 2017; LaGrandeur & Hughes, 2017; Talwar, 2015; Talwar et al., 2017). In any case, all fears of AI are directed toward Artificial Superintelligence, which is a good inspiration for Hollywood sci-fi blockbusters; but in real life, Artificial Narrow Intelligence is what software engineers have practically achieved. That is why, in this book, we focus solely on Artificial Narrow Intelligence (weak AI). Within the realm of TTH, AI (neural networks and machine learning) have been applied in forecasting tourism arrivals/demand/expenditure (Law, 2000; Palmer, Montano, & Sesé, 2006; Sun, Wei, Tsui, & Wang, 2019), hotel occupancy (Law, 1998), waste generation rates in hotels (Azarmi, Alipour, & Oladipo, 2017), or their energy demand (Casteleiro-Roca et al., 2018). Researchers have used AI for analytical purposes, such as the identification of destination attributes (Santos Silva, Albayrak, Caber, & Moutinho, 2016), sentiment analysis of online reviews (Kirilenko, Stepchenkova, Kim, & Li, 2018; Ye, Zhang, & Law, 2009), the evaluation of the impacts of online reviews on hotel performance (Phillips, Zigan, Silva, & Schegg, 2015), the assessment of hotel employee satisfaction (Tian & Pu, 2008), and market segmentation (Kim, Wei, & Ruys, 2003), among others. Nica et al. (2018) developed a chatbot for recommendations in tourism while the application of facial recognition systems at airports has been studied by del Rio et al. (2016). As a whole, authors agree that AI provides significant opportunities for improved data analysis and decision-making in TTH companies, and allows automation of some processes in them (e.g., automated communication with customers and bookings via chatbots). However, research has largely focused on the application

Conceptual Framework of the Use of Robots    19 of AI in a travel, tourism, or hospitality setting, rather than on its impacts on the industry and the transformations it would trigger.

Robots in TTH A robot is an “actuated mechanism programmable in two or more axes with a degree of autonomy, moving within its environment, to perform intended tasks” (International Organization for Standardization, 2012, n.p.). Autonomy and the ability to sense and manipulate the environment are key features of robots. Beer, Fisk, and Rogers (2014, p. 77) define autonomy as: the extent to which a robot can sense the environment, plan based on that environment, and act upon that environment, with the intent of reaching some goal (either given to or created by the robot) without external control. Sensors for identifying objects, sound/voice, distance/location, pressure, temperature, power consumption, communicating with other devices, etc., allow the robot to obtain data about some aspects of its surrounding environment, while actuators (such as a motor, robotic arm, screen, light, loudspeaker, transmitter, etc.) help the robot affect its environment (Ben-Ari & Mondada, 2018). The term “robot” was firstly introduced in 1920 by Karel Čapek in his play R.U.R – Rossum’s Universal Robots (NPR, 2011) and popularized in science-fiction literature by Isaac Asimov. The first industrial robot (named “Unimate”) was installed in 1961 by its manufacturer Unimation at General Motors’ plant in Trenton, New Jersey, where it unloaded high temperature parts from a die casting machine – a very unpopular and dangerous task for human employees (Stone, 2005). While self-service kiosks have been used by service companies for decades, robots make their first steps into the service business (van Doorn et al., 2017; Wirtz et al., 2018). Within TTH industries, robots have various degrees of autonomy, intelligence, and interactivity. They range from basic robots for cutting grass, cleaning floors, and swimming pools that practically do not interact with humans at all, to more sophisticated room service delivery robots, robotic waiters, and humanoid robots such as Pepper that can actively communicate with humans (Figs. 13–15). For the moment, service robots seem quite clumsy in their interactions with humans and navigation through the premises of hotels, restaurants, and airports. For example, with a recent highly publicized move, the first robotic hotel (Henn-na Hotel in Japan) stopped using half of its robots because they reportedly created more work for human employees and caused problems to guests (Shead, 2019). However, the technological advances in AI and robotics will make robots more capable of serving humans and implementing various tasks beyond the 3D (dirty, dull, and dangerous) tasks, which human beings do not want to do, and this will help to overcome the temporary hiccups in robot-delivered services we currently observe, and will widen robots’ application and adoption by tourist companies (Ivanov, 2019).

20    Stanislav Ivanov and Craig Webster

Fig. 13.  Pepper Robot, Munich Airport, Germany (Photo Credit: Stanislav Ivanov).

Fig. 14.  Amy Food-serving Robot (Photo Credit: Suzhou Pangolin Robot Corp Ltd). Research on robots in TTH is still in its infancy. A recent paper by Ivanov, Gretzel, Berezina, Sigala, and Webster (2019) reviewed comprehensively all 131 publications on robotics in TTH published from 1993 until 2019. The authors concluded that research in the field had been largely driven by engineering and only recently a social science perspective was added to it. They also identified

Conceptual Framework of the Use of Robots    21

Fig. 15.  Robotic Lawnmower (Photo Credit: Stanislav Ivanov). seven research domains in the field of robots in tourism, namely, “Robot,” “Human,” “Robot manufacturer,” “Travel / tourism / hospitality company,” “Servicescape,” “External environment,” and “Education, training and research.” The first three research domains received most of the attention of researchers. Studies on service robots in tourism (outside engineering) are overwhelmingly conceptual. Murphy et al. (2017), for example, reviewed current research on robotics in tourism and identified directions for future studies. Tung and Law (2017) identified the research opportunities in human-robot interactions in tourism and hospitality, Tung and Au (2018) focused on exploring customer experiences with robotics, while Murphy et al. (2019) investigated conceptually the role of anthropomorphism in marketing service robots in tourism and hospitality. Ivanov et al. (2017) provided an overview of current practical application of robots and service automation by TTH companies, and identified areas for their potential adoption in the future. Additionally, Ivanov and Webster (2018) analyzed the costs and benefits of robot adoption for TTH companies, Ivanov and Webster (2017b) delved into the implications of robotic technologies for the design of hospitality facilities, while Ivanov (2019) discussed the impacts of automation technologies (including robots) on the business models, operations, and practices of TTH companies. Finally, Cohen and Hopkins (2019) discussed the implications of autonomous vehicles for the future of urban tourism. On an empirical level, Lu, Cai, and Gursoy (2019) developed and validated a 36-item scale for measuring willingness to use service robots in a tourism context, based on six dimensions – performance efficacy, intrinsic motivation, anthropomorphism, social influence, facilitating conditions, and emotions. Kuo et al. (2017) conducted 20 interviews with academics and practitioners in order to formulate a SWOT analysis of the adoption of robots by hospitality companies in Taiwan. Yu and Ngan (2019) delved into the appearance of humanoid robots,

22    Stanislav Ivanov and Craig Webster and the gender and cultural differences of respondents’ perceptions. Tussyadiah, Zach, and Wang (2017) assessed people’s attitudes toward self-driving taxis while Tussyadiah and Park (2018) dealt with customers’ evaluations of hotel service robots. Customers’ attitudes toward the adoption of robots by TTH companies are investigated by Ivanov, Webster, and Garenko (2018) for a sample of 260 young Russian adults, Ivanov, Webster, and Seyyedi (2018) for a sample of 393 Iranian tourists, and by Ivanov and Webster (2019a, 2019b) for a global sample of 1003 respondents. In general, the authors found that respondents had mostly positive attitudes toward robots in tourism. The services that relate to information provision, delivery of food and various items, and housekeeping were perceived by respondents as more appropriate for robotization, while services that require tourists to subordinate their bodies to a robot (e.g., massages, babysitting, or hairdressing) received most resistance by respondents. Although a number of empirical studies have been published, they are still too few and there is much ground for empirical research in the field of service robots in TTH as prior studies have already indicated.

RAISA in TTH – A Conceptual Framework Fig. 16 presents the conceptual framework of the use of RAISA in TTH. At the core of the conceptual framework is a TTH company. The company has two layers of stakeholders – internal (employees and managers) and external (RAISA suppliers, competitors, and customers). Practically, TTH company’s decision to use RAISA technologies and the outcomes of that decision depend on each of the stakeholders. RAISA suppliers are manufacturers and distributors of various RAISA solutions (robotics manufacturers, kiosk producers, software

Popularisation of RAISA solutions TTH company

Attitudes|Interactions | Robot appearance preferences| Resistance

Deskilling vs. Upskilling

Pro mote RAIS A solut ions

Service process participation Business processes Operations

Substitution vs. Enhancement Attitudes Managing resistance

Provide RAISA solutions

RAISA Attitudes

Human resources


Programme for adoption of RAISA

Marketing mix


Willingness to pay Perceived service quality


RAISA suppliers


Managers Decision to adopt RAISA


Financial performance

Cost-benefit analysis




Fig. 16.  Conceptual Framework of the Use of RAISA Technologies in TTH.

Conceptual Framework of the Use of Robots    23 engineering companies, their agents and sales representatives, etc.). They invest in RAISA research and development, and promote their products to the managers of TTH companies by emphasizing the financial and non-financial benefits and cost-savings RAISA solutions would bring to them. However, they popularize RAISA technologies to customers of TTH companies as well in order to form positive attitudes toward RAISA, address customers’ fears, and to stimulate them to use these technologies. TTH companies will use RAISA technologies if customers accept them. If customers have more positive attitudes toward RAISA technologies in general, they will be more likely to use them in TTH context (see, e.g., Ivanov, Webster, & Garenko, 2018; Ivanov, Webster, & Seyyedi, 2018). Moreover, if customers consider specific activities as appropriate for automation, they would be more willing to use automation technologies for them (Ivanov & Webster, 2019b). Customers’ attitudes toward RAISA influence and are shaped by their actual interactions with RAISA technologies and their service process participation, and may lead to customers’ resistance to use them. For instance, customers may have some initial expectations about and attitudes toward service robots in restaurants, shaped by RAISA suppliers’ and TTH companies’ promotional activities, and media publications. However, their actual interactions with the service robots may push their attitudes into more positive or more negative direction. In a similar vein, a complicated and nonintuitive interface of a self-check-in kiosk, or a chatbots that does not provide the information that customers need, may cause frustration of customers who struggle with the self-service process. Managers and employees have attitudes toward RAISA as well. However, these attitudes may be driven by different factors. On one hand, managers would perceive RAISA as a tool to improve productivity, service quality and, ultimately, financial results of the company (Ivanov, 2019). They perform cost-benefit analysis of the adoption of RAISA (Ivanov & Webster, 2018) and take decision to use or not RAISA, which is practically implemented through a program for adoption of RAISA. On the other hand, employees may have more negative attitudes toward RAISA, because they may perceive RAISA as an instrument for their substitution as employees, not as a way to enhance them at their job, thus causing fear of losing their jobs and increasing their turnover intentions (DeCanio, 2016; Li, Bonn, & Ye, 2019; McClure, 2018). And TTH employees may have reasonable grounds for their concerns. RAISA technologies have significant advantages compared to human employees elaborated in details in Ivanov (2017, 2019). RAISA technologies, for instance, could: work 24 hours a day 7 days a week; implement various tasks and expand their scope with software and hardware upgrades; fulfill their work correctly following the scripts of service procedures and do this in a timely manner; provide constant or improving quality of their work; and do routine work repeatedly. They also do not go on strikes, spread rumors, discriminate against customers or employees, quit their job without notice, show negative emotions, shirk from work, ask for pay increases, sue their employer, get ill, etc., although some publications claim that AI is prone to discrimination (Paterson & Maker, 2018). Of course, RAISA technologies have some significant disadvantages as well. They can generally only work in highly structured situations, lack

24    Stanislav Ivanov and Craig Webster creativity and personal approach. RAISA technologies will not be any time soon completely independent of human supervision and may (will) be perceived as a threat by human employees. While current robotic technologies are challenged by simple tasks most humans can easily do (such as turning a handle and opening a door), in the long term, the technological advances in RAISA may decrease or eliminate some of the disadvantages of RAISA technologies compared to human employees in TTH. In short, technological advances are expected to make RAISA more attractive for TTH companies. From a human resource management perspective, the use of RAISA leads to changes in the skills required to work in TTH due to deskilling (the work can be performed by a less skillful employee) and up-skilling of jobs (more competences required for human employees). Both deskilling and up-skilling of TTH jobs due to RAISA adoption have their trade-offs. On the one hand, deskilling of jobs allows TTH companies to use less skilled employees for the provision of required services. Doing so expands the pool of potential employees TTH companies can hire, thus increasing the competition among employees, depressing their salaries, keeping costs under control, and allowing customers to enjoy low prices, if the savings are passed on to customers. On the other hand, deskilling makes employees substitutable, increases their fear of automation, and may stimulate their turnover intentions (see also Li et al., 2019), which increases the recruitment and training costs of TTH companies, and may create negative psychological climate in the companies, hence stifling innovation and hurting service quality. Looking at up-skilling, similar trade-offs can be observed but in the opposite direction. Using more sophisticated technologies requires companies to invest in training of employees, which increases employees bargaining power, competitiveness on the labor market, and salaries. The costs and consumer prices of TTH services would be kept the same or lower if only the combined productivity of up-skilled human employees and RAISA increases more than the costs for human labor and RAISA. At any rate, TTH companies need to manage employees’ resistance to RAISA through engaging in proper internal communications (promoting openness and employee involvement in the decision-making process), reducing uncertainty from the introduction of RAISA, offering appropriate training courses, reallocating labor to other job positions, offering compensation of substituted employees, etc. (see also Burnes, 2017). The adoption of RAISA technologies has significant impacts on the business processes of companies in all functional areas – operations (including the design of facilities), marketing, human resources and financial management, elaborated in details in Ivanov (2019) and summarized in Table 1. The transformation of business processes can be reflected in changes in a company’s competitiveness and financial performance. If competitors use RAISA and this provides them with a sustainable competitive advantage, probably the TTH company should use such solutions as well or reposition itself to a “high touch” company (Ivanov, 2019; Naisbitt et al., 2001). The pricing of RAISA-delivered services (as an element of the marketing mix of TTH companies) needs to consider not only costs but customers’ perceived quality of RAISA-delivered services and their willingness to pay for non-human delivered tourism/hospitality services.

Conceptual Framework of the Use of Robots    25 Table 1.  Impacts of RAISA Introduction on Business Processes of TTH Companies. Functional area Operations management

Impacts of RAISA •S  ervice is delivered by a non-human agent – a robot, chatbot, kiosk, vending machine, etc. • I ncreased service capacity of TTH companies – more customers can be served simultaneously and for a particular period of time → increased productivity •E  asier scheduling and planning of operations due to the advantages of RAISA compared to human employees •R  eengineering of service-delivery processes – new processes, activities, procedures, controls, new service operations manuals to be introduced in TTH companies • I ncreased role of the customer in the service delivery → the consumer is transformed into a “prosumer” (= “producer” + “consumer”) → co-creation of value • I mproved environmental sustainability of operations due to decreased use of resources, reduced waste, elimination of unnecessary activities, etc. • Decreased flexibility of the service delivery system • Necessity to create robot-friendly hospitality facilities for the mobile robots of TTH companies and their customers

Human resource management

• RAISA would save employees’ time from performing 3D tasks (dull, dirty, and dangerous), which they could use for other more creative and revenue generating activities. • Enhancing, rather than replacing the employees • RAISA would solve some the problems with hiring and firing of employees, especially the seasonal employees • RAISA might require reorganization of companies – new departments, job positions, communication links between them • Changes in the number of employees in the various departments → zero-employee hotels •R  esistance of employees who perceive RAISA as threat for their jobs • Changes in the required skills of employees → required changes in the curricula of the tourism and hospitality programs in higher education institutions

26    Stanislav Ivanov and Craig Webster Table 1.  (Continued) Functional area Marketing management

Impacts of RAISA •C  hanged customer expectations about the tourism / hospitality product → redefinition of the scope of the product of a tourist company (e.g., should a hotel company be able to provide robot repair service or a sex robot?) • RAISA could enhance the perceived service quality through new attractive and interactive ways of service delivery, communicating, and engaging with customers:   • Robots, chatbots, service kiosks could communicate in different languages and do this 24/7   • RAISA can create value for the customers by making the service deliver process funny and entertaining • Division of tourism/hospitality companies into two main large groups – “high-tech” versus “high-touch” companies with various shades of gray in between them • Pricing:   • RAISA allow automated pricing without human involvement   • RAISA allow personalized/individual pricing (perfect price discrimination)   • Lower prices for mass “high-tech” TTH products   • Higher prices for exclusive “high-touch” TTH products • Distribution:   • Predictive analytics through AI   • Automated allocation of available capacity by distribution channel via intelligent channel managers   • Distribution via digital voice assistants (e.g., Amazon Alexa, Apple Siri, and Google Assistant) • Communications, image, positioning:   • The company that adopts RAISA would boast positive word-of-mouth due to its image of an innovative high-tech company.   • The company may also suffer negative publicity as it may be perceived as a company that puts profits before humans   •A  utomated communications with customers via chatbots, voice assistants, or robots

Conceptual Framework of the Use of Robots    27 Table 1.  (Continued) Functional area Financial management

Impacts of RAISA •L  abor costs savings – RAISA work 24/7 and may serve numerous customers simultaneously. • I ncreased sales – customers’ curiosity in seeing the robots, 24/7 availability •F  inancial costs, associated with RAISA – acquisition, installation, maintenance, software update, for creating robot-friendly facilities, for up-skilling human resources, insurance costs for robots/kiosk and for damages caused by robots.

Source: Table developed by the authors on the basis of Ivanov (2019).

In a broader perspective, the adoption of RAISA technologies in TTH is influenced by various macroenvironmental drivers. Besides advances in technology, demography seems to be the most important of them. Fertility rates in Europe, China, Japan, South Korea, and other countries have dropped dramatically since 1960s (World Bank, 2019) and in many of them they fell below the replacement rate of 2.1 children per woman (the minimum number of children needed to keep the population size stable). The observed increase in the population size in these countries is large due to prolonged life expectancy and immigration, rather than high birth rates. This demographic situation means that Europe, China, Japan, South Korea, and other countries with plummeting birthrates can expect serious disruptions of their labor markets – in the near future there might not be sufficient number of human employees to work in their economies. In a pragmatic sense, there are three possible solutions to the expected labor force crisis in those countries (largely the wealthier/more developed ones) (see Fig. 17) – “produce people,” “import people,” and “substitute people.” The first solution relies on the natural biological way of human reproduction, although technology may assist in this. However, in modern democratic societies, having children is considered an individual decision and any governmental intervention in the family planning process would be considered as political pressure. However, even if people start having more children, it would take at least two decades before their children enter the labor market. Furthermore, such a tendency needs to continue for at least two more decades in order to have any significant impacts on the labor market. In addition, there seems to be not only social/cultural choices to either have fewer or no children, but also worrying reproductive trends that may make reproduction for many much harder than before, such as the precipitous drops in sperm counts in recent decades in many of the world’s most advanced countries (Kelland, 2017). Hence, although biologically preferred in the long-term, the first option (“produce people”) does not seem viable.

28    Stanislav Ivanov and Craig Webster

Fig. 17.  Solutions to the Labor Force Crisis. The second solution (“import people”) steps on immigration and the free movement of labor across borders. For the host country with labor shortages, the use of immigrants would have positive economic impacts, but it can cause significant social problems, especially if the immigrants come from countries with different cultural and religious background than the host country. While many support the development of multicultural and multiethnic societies, there are practical implications and costs that accompany the mass importation of labor that may result in political and social resistance. Ultimately, the third solution proposes companies to substitute labor as a production factor; hence, instead of finding human employees, companies reorganize their operations through the use of RAISA technologies, so that they would need fewer employees. RAISA are also a “politically correct” solution to labor shortages especially when considering the social tensions that might be caused by immigration. Therefore, the third proposed solution (“substitute people”) seems as the most viable in the short and long term. It enhances and replaces labor while at the same time avoiding the political, social, and economic costs of building a multicultural/multiethnic society. This is especially true for those countries that do not have long and sustained histories as lands of immigration and with a stronger sense of race, ethnicity, and culture. The plummeting birth rates in Europe, China, Japan, South Korea, and pretty much every other developed country mean that human reproduction rates are below the replacement rate and this has massive implications for social policy and labor. In addition, local residents’ resistance to immigration to replace an aging workforce, surging number of tourists, and strong competition with other sectors of the economy for skilled labor, create huge misbalances on the TTH labor market and they will only be aggravated in the future. The demand for skilled TTH employees surpasses their supply. This situation forces TTH companies to use RAISA technologies in order to decrease their dependence on

Conceptual Framework of the Use of Robots    29 human labor. The rigid labor laws in the European Union and other countries in regards to hiring and firing employees and other elements of the social safety net (social security, generous pension schemes, and medical insurance costs) make RAISA technologies even more attractive than human employees. The technological progress combined with RAISA’s decreasing costs and increasing human employee costs would make RAISA technologies even more attractive to TTH companies in the future and would further stimulate their adoption. Xenophobia and anti-immigration sentiments seem to be on the rise and the OSCE (2018) is concerned about this. The technological solution to the labor shortage would reduce ethnic/cultural strife in developing countries, although, admittedly, it creates a problem for those countries that typically send their excess labor force to developed countries.

Concluding Remarks This chapter presented the conceptual framework of the use of RAISA in TTH. It elaborated in the issues related to the use of RAISA in TTH industry, including the scope of RAISA technologies, advantages, and disadvantages of RAISA technologies compared to human employees, decisions that manager need to take, the impacts of RAISA on business processes, and the drivers of RAISA adoption. It showed how macroenvironmental pressures shape the microeconomic decisions to use RAISA in TTH context. Although human employees in TTH will not go the way of the horses (Brynjolffson & McAfee, 2015), RAISA would definitely change the nature of work in TTH and the way TTH companies do business. While current robots and AI technologies can perform many tasks and can be effective, we will likely look back at the current technologies and be surprised at how clunky and slow these technologies look. Yet, in the not so distant future, we can expect to see increasingly sophisticated technologies to do tasks that are automated, satisfying customer demands. However, there are several things that will have to happen in order for RAISA to become increasingly prominent and common in TTH. First, there will have to be RAISA technologies developed that are practical, work well, and are inexpensive to purchase. There is some resistance to the further development of RAISA, not only from Luddites who fear for their replacement in the workforce with robots and AI, but also respected intellectuals and thinkers who see dangers in the further development of a technology that may become self-aware. The practical need for cheap labor and the proximity of the technology to being able to replace expensive human labor with inexpensive technological solutions will propel the technological development into the future. So, while there is concern about the technologies, the need for automation of tasks and the technical ability to achieve it will ensure that more tasks will become more automated as time goes on. However, when many more tasks are automated, there may be the opportunity for the development of a market niche that gives consumers the chance to spend big money and experience high-touch hospitality, rather than the high-tech hospitality the common consumer will experience.

30    Stanislav Ivanov and Craig Webster Second, there will be difficult political decisions for dealing with the economic, social, and political puzzle created by populations that have gone through a demographic transition. Paradoxically, it may be that the RAISA technologies, void of any religion, ethnicity and race, may be the solution that is used to replace the humans that should have been brought into the labor force but were never born, and the wealthy country’s second favorite choice to fill in empty labor slots, immigration. Third, another major issue of RAISA adoption in TTH is how quickly TTH companies and consumers come to embrace RAISA. The consumers will become supportive of the use of RAISA if they see financial advantage in using RAISA. If industry passes on the savings it saves in labor to the consumers, the latter may be increasingly willing to embrace RAISA solutions. Eventually, following exposure to the technologies and as they become normalized and more effective and efficient in TTH, the consumers will come around and embrace them, but to kick start the process, it is likely that consumers should be communicated with regards to the benefits of the technologies. The most direct path to the consumers’ hearts is likely going to be through their bank accounts with the TTH industry showing consumers that the cost savings will save money and will have little or no negative impact upon service quality. All-in-all, we are moving into a brave new world of RAISA in TTH (Wirtz et al., 2018). While we are still in the first stages of the incorporation of RAISA in the industry in a big way, we can only expect an increasing incorporation of such technologies into the industry. Human technological capabilities, a lack of serious and organized resistance to the technology’s incorporation into industry, and the need to find solutions to use human labor more efficiently in developed countries are drivers for the technology’s increasing presence in industry. While some of the externalities can be expected, it will lead to many challenges with regards to how it impacts upon the labor market, workplace culture, and international relations. Although the future is unwritten, it is quite certain that RAISA technologies will become increasingly used in TTH and accepted by consumers, many of which will not remember hotels and hospitality without robotic services 50 years from now.

References Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Boston, MA: Harvard Business Review Press. Ahn, J. A., & Seo, S. (2018). Consumer responses to interactive restaurant self-service technology (IRSST): The role of gadget-loving propensity. International Journal of Hospitality Management, 74, 109–121. Askarpour, M., Mandrioli, D., Rossi, M., & Vicentini, F. (2019). Formal model of human erroneous behavior for safety analysis in collaborative robotics. Robotics and Computer-Integrated Manufacturing, 57, 465–476. Azarmi, S. L., Alipour, H., & Oladipo, A. A. (2017). Using artificial neural network and desirability function to predict waste generation rates in small and large hotels during peak and lean seasons. In Proceedings of the 7th advances in hospitality & tourism

Conceptual Framework of the Use of Robots    31 marketing & management (AHTMM) conference, Famagusta, Cyprus, 10–15 July 2017 (pp. 539–547). Eastern Mediterranean University and Washington State University. Barfield, W. (2015). Cyber-humans. Our future with machines. Cham, Switzerland: Springer. Barrat, J. (2013). Our final invention: Artificial intelligence and the end of the human era. New York, NY: Macmillan. Beer, J. M., Fisk, A. D., & Rogers, W. A. (2014). Toward a framework for levels of robot autonomy in human–robot interaction. Journal of Human–Robot Interaction, 3(2), 74–99. Ben-Ari, M., & Mondada, F. (2018). Elements of robotics. Cham, Switzerland: Springer International Publishing. Benckendorff, P. J., Xiang, Z., & Sheldon, P. J. (2019). Tourism information technology (3rd ed.). Wallingford: CABI. Bhaumik, A. (2018). From AI to robotics: Mobile, social, and sentient robots. Boca Raton, FL: CRC Press. Bogicevic, V., Bujisic, M., Bilgihan, A., Yang, W., & Cobanoglu, C. (2017). The impact of traveler-focused airport technology on traveler satisfaction. Technological Forecasting and Social Change, 123, 351–361. Bostrum, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford: Oxford University Press. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York and London: WW Norton & Company. Brynjolffson, E., & McAfee, A. (2015). Will humans go the way of horses? Labor in the second machine age. Foreign Affairs, 94(4), 8–14. Burnes, B. (2017). Managing change (7th ed.). Harlow: Pearson. Callaghan, V., Miller, J., Yampolskiy, R., & Armstrong, S. (Eds.). (2017). The technological singularity: Managing the journey. Berlin, Germany: Springer. Casteleiro-Roca, J. L., Gómez-González, J. F., Calvo-Rolle, J. L., Jove, E., Quintián, H., Martín, J. F. A., … Méndez-Perez, J. A. (2018). Prediction of the energy demand of a hotel using an artificial intelligence-based model. In International conference on hybrid artificial intelligence systems (pp. 586–596). Cham, Switzerland: Springer. Chathoth, P. (2007). The impact of information technology on hotel operations, service management and transaction costs: A conceptual framework for full-service hotel firms. International Journal of Hospitality Management, 26(2), 395–408. Chen, J. V., Yen, D., Dunk, K., & Widjaja, A. E. (2015). The impact of using kiosk on enterprise systems in service industry. Enterprise Information Systems, 9(8), 835–860. Cheok, A. D., Devlin, K., & Levy, D. (Eds.). (2017). Love and sex with robots. Revised selected papers of the second international conference, LSR 2016, 19–20 December. London: Springer. Cheong, S. N., Ling, H. C., & Teh, P. L. (2014). Secure encrypted steganography graphical password scheme for near field communication smartphone access control system. Expert Systems with Applications, 41(7), 3561–3568. Clerwall, C. (2014). Enter the robot journalist: Users’ perceptions of automated content. Journalism Practice, 8(5), 519–531. Clifford, C. (Wed, 8 Nov 2017). Hundreds of A.I. experts echo Elon Musk, Stephen Hawking in call for a ban on killer robots. Retrieved from ai-experts-join-elon-musk-stephen-hawking-call-for-killer-robot-ban.html Cohen, S. A., & Hopkins, D. (2019). Autonomous vehicles and the future of urban tourism. Annals of Tourism Research, 74, 33–42. Collins, G. R., Cobanoglu, C., Bilgihan, A., & Berezina, K. (2017). Chapter 12: Automation and robotics in the hospitality industry. In Hospitality information technology: Learning how to use it (8th ed., pp. 413–449). Dubuque, IA: Kendall/Hunt Publishing Co.

32    Stanislav Ivanov and Craig Webster Corea, F. (2017). Artificial intelligence and exponential technologies: Business models evolution and new investment opportunities. Cham, Switzerland: Springer. Crootof, R. (2015). War, responsibility, and killer robots. North Carolina Journal of International Law and Commercial Regulation, 40(4), 909–932. Cubero, S. (Ed.). (2007). Industrial robotics: Theory, modelling and control. Mammendorf, Germany: pro literature Verlag Robert Mayer-Scholz. Danaher, J., & McArthur, N. (Eds.). (2017). Robot sex: Social and ethical implications. Boston, MA: MIT Press. Davenport, T. H. (2018). The AI advantage. How to put artificial intelligence revolution to work. Cambridge, MA: The MIT Press. Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Boston, MA: Harvard Business Review Press. DeCanio, S. J. (2016). Robots and humans—Complements or substitutes? Journal of Macroeconomics, 49, 280–291. del Rio, J. S., Moctezuma, D., Conde, C., de Diego, I. M., & Cabello, E. (2016). Automated border control e-gates and facial recognition systems. Computers & Security, 62, 49–72. Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019). Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Information Fusion, 50, 92–111. Driessen, C., & Heutinck, L. F. M. (2015). Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms. Agriculture and Human Values, 32(1), 3–20. Dunis, C. L., Middleton, P. W., Karathanasopolous, A., & Theofilatos, K. A. (Eds.). (2017). Artificial intelligence in financial markets: Cutting edge applications for risk management, portfolio optimization and economics. London: Palgrave Macmillan. Ertel, W. (2017). Introduction to artificial intelligence (2nd ed.). Cham, Switzerland: Springer. Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. Frank, M., Roehring, P., & Pring, B. (2017). What to do when machines do everything: How to get ahead in a world of AI, algorithms, bots and big data. Hoboken, NJ: John Wiley & Sons, Inc. Goertzel, B., & Pennachin, C. (Eds.). (2007). Artificial general intelligence. New York, NY: Springer. Gunkel, D. J. (2018). Robot rights. Boston, MA: MIT Press. Gures, N., Inan, H., & Arslan, S. (2018). Assessing the self-service technology usage of Y-Generation in airline services. Journal of Air Transport Management, 71, 215–219. Hill, J., Ford, W. R., & Farreras, I. G. (2015). Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 49, 245–250. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. International Organization for Standardization. (2012). ISO 8373:2012(en) Robots and robotic devices – Vocabulary. Retrieved from ui/#iso:std:iso:8373:ed-2:v1:en:term:2.2 Ivanov, S. (2016). Will robots substitute teachers? Yearbook of Varna University of Management, 9, 42–47. Ivanov, S. (2017). Robonomics – Principles, benefits, challenges, solutions. Yearbook of Varna University of Management, 10, 283–293.

Conceptual Framework of the Use of Robots    33 Ivanov, S. (2018). Tourism beyond humans – Robots, pets and teddy bears. Paper presented at the international scientific conference on “tourism and innovations”, 14–15 September, College of Tourism – Varna, Varna, Bulgaria. Ivanov, S. (2019). Ultimate transformation: How will automation technologies disrupt the travel, tourism and hospitality industries? Zeitschrift für Tourismuswissenschaft, 11(1), 25–43. Ivanov, S., Gretzel, U., Berezina, K., Sigala, M., & Webster, C. (2019). Progress on robotics in hospitality and tourism: A review of the literature. Journal of Hospitality and Tourism Technology (forthcoming). Ivanov, S., & Webster, C. (2017a). The robot as a consumer: A research agenda. In Proceedings of the “marketing: experience and perspectives” conference, 29–30 June, University of Economics – Varna, Bulgaria (pp. 71–79). Ivanov, S., & Webster, C. (2017b). Designing robot-friendly hospitality facilities. In Proceedings of the scientific conference on “Tourism. Innovations. Strategies”, 13–14 October, Bourgas, Bulgaria (pp. 74–81). Ivanov, S., & Webster, C. (2018). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies – A cost-benefit analysis. In V. Marinov, M. Vodenska, M. Assenova, & F. Dogramadjieva (Eds.), Traditions and innovations in contemporary tourism (pp. 190–203). Cambridge: Cambridge Scholars Publishing. Ivanov, S., & Webster, C. (2019a). What should robots do? A comparative analysis of industry professionals, educators and tourists. In J. Pesonen & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, Nicosia, Cyprus, 30 January–01 February (pp. 249–262). Ivanov, S., & Webster, C. (2019b). Perceived appropriateness and intention to use service robots in tourism. In J. Pesonen, & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, Nicosia, Cyprus, 30 January–01 February (pp. 237–248). Ivanov, S., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27/28, 1501–1517. Ivanov, S., Webster, C., & Garenko, A. (2018). Young Russian adults’ attitudes towards the potential use of robots in hotels. Technology in Society, 55, 24–32. Ivanov, S., Webster, C., & Seyyedi, P. (2018). Consumers’ attitudes towards the introduction of robots in accommodation establishments. Tourism, 63(3), 302–317. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. Kaplan, J. (2016). Artificial Intelligence: What everyone needs to know. Oxford: Oxford University Press. Kattara, H. S., & El-Said, O. A. (2013). Customers’ preferences for new technology-based self-services versus human interaction services in hotels. Tourism and Hospitality Research, 13(2), 67–82. Kaur, S. (2012). How medical robots are going to affect our lives. IETE Technical Review, 29(3), 184–187. Kaushik, A. K., Agrawal, A. K., & Rahman, Z. (2015). Tourist behaviour towards selfservice hotel technology adoption: Trust and subjective norm as key antecedents. Tourism Management Perspectives, 16, 278–289. Kazda, A., & Caves, R. E. (2015). Airport design and operation (3rd ed.). Bingley: Emerald Group Publishing. Kelland, K. (2017, July 26). Sperm count dropping in western world. Scientific American. Retrieved from

34    Stanislav Ivanov and Craig Webster Kelly, P., Lawlor, J., & Mulvey, M. (2017). Customer roles in self-service technology encounters in a tourism context. Journal of Travel & Tourism Marketing, 34(2), 222–238. Kim, J., Christodoulidou, N., & Choo, Y. (2013). Factors influencing customer acceptance of kiosks at quick service restaurants. Journal of Hospitality and Tourism Technology, 4(1), 40–63. Kim, M., & Qu, H. (2014). Travelers’ behavioral intention toward hotel self-service kiosks usage. International Journal of Contemporary Hospitality Management, 26(2), 225–245. Kim, J., Wei, S., & Ruys, H. (2003). Segmenting the market of West Australian senior tourists using an artificial neural network. Tourism Management, 24(1), 25–34. Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012–1025. Kokkinou, A., & Cranage, D. A. (2013). Using self-service technology to reduce customer waiting times. International Journal of Hospitality Management, 33, 435–445. Koslowski, R., & Schulzke, M. (2018). Drones along borders: Border security UAVs in the United States and the European Union. International Studies Perspectives, 19(4), 305–324. Kuo, C.-M., Chen, L.-C., & Tseng, C.-Y. (2017). Investigating an innovative service with hospitality robots. International Journal of Contemporary Hospitality Management, 29(5), 1305–1321. Kucukusta, D., Heung, V. C. S., & Hui, S. (2014). Deploying self-service technology in luxury hotel brands: Perceptions of business travelers. Journal of Travel and Tourism Marketing, 31(1), 55–70. Kurzweil, R. (2005). The singularity is near. When humans transcend biology. London: Duckworth Overlook. LaGrandeur, K., & Hughes, J. J. (Eds.). (2017). Surviving the machine age. Intelligent technology and the transformation of human work. London: Palgrave Macmillan. Latar, N. L. (2018). Robot journalism: Can human journalism survive? Singapore, Singapore: World Scientific. Law, R. (1998). Room occupancy rate forecasting: A neural network approach. International Journal of Contemporary Hospitality Management, 10(6), 234–239. Law, R. (2000). Back-propagation learning in improving the accuracy of neural networkbased tourism demand forecasting. Tourism Management, 21(4), 331–340. Lee, J. (2017). Sex robots: The future of desire. London: Palgrave Macmillan. Leonhard, G. (2016). Technology vs. humanity. London: Fast Future Publishing. Li, J. J., Bonn, M. A., & Ye, B. H. (2019). Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172–181. Liu, C., & Hung, K. (2019). Understanding self-service technology in hotels in China: Technology affordances and constraints. In J. Pesonen, & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, Nicosia, Cyprus, 30 January–01 February (pp. 225–236). Cham, Switzerland: Springer. Low, K.-H. (Ed.). (2007). Industrial robotics: Programming, simulation and applications. Mammendorf, Germany: pro literature Verlag Robert Mayer-Scholz. Lu, L., Cai, R., & Gursoy, D. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 80, 36–51. Lui, K. (2016, November 15). Watch the world’s first commercial pizza delivery by drone. Fortune. Retrieved from Accessed on November 5, 2018. Mahroof, K. (2019). A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse. International Journal of Information Management, 45, 176–190.

Conceptual Framework of the Use of Robots    35 Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. Maurer, M., Gerdes, J. C., Lenz, B., & Winner, H. (Eds.). (2016). Autonomous driving: Technical, legal and social aspects. Berlin, Germany: Springer Open. McClure, P. K. (2018). “You’re Fired,” says the robot: The rise of automation in the workplace, technophobes, and fears of unemployment. Social Science Computer Review, 36(2), 139–156. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50–64. Miller, M. R., & Miller, R. (2017). Robots and robotics: Principles, systems, and industrial applications. New York, NY: McGraw-Hill Education. Mirheydar, H. S., & Parsons, J. K. (2013). Diffusion of robotics into clinical practice in the United States: Process, patient safety, learning curves, and the public health. World Journal of Urology, 31(3), 455–461. Murphy, J., Hofacker, C., & Gretzel, U. (2017). Dawning of the age of robots in hospitality and tourism: Challenges for teaching and research. European Journal of Tourism Research, 15, 104–111. Murphy, J., Gretzel, U., & Pesonen, J. (2019). Marketing robot services in hospitality and tourism: The role of anthropomorphism. Journal of Travel & Tourism Marketing, 1–12. doi:10.1080/10548408.2019.1571983 Naisbitt, J., Naisbitt, D., & Philips, D. (2001). High tech high touch: Technology and our accelerated search for meaning. London: Nicolas Brealey Publishing. Neapolitan, R. E., & Jiang, X. (2018). Artificial intelligence: With an introduction to machine learning (2nd ed.). Boca Raton, FL: CRC Press. Nica, I., Tazl, O. A., & Wotawa, F. (2018). Chatbot-based tourist recommendations using model-based reasoning. In A. Felfernig, J. Tiihonen, L. Hotz, & M. Stettinger (Eds.), Proceedings of the 20th international configuration workshop, Graz, Austria, 27–28 September (pp. 25–30). Nørskov, M. (2016). Social robots: Boundaries, potential, challenges. New York, NY: Routledge. NPR. (2011) Science diction: The origin of the word ‘robot.” Retrieved from https://www. Oh, H., Jeong, M., & Baloglu, S. (2013). Tourists’ adoption of self-service technologies at resort hotels. Journal of Business Research, 66(6), 692–699. Oh, H., Jeong, M., Lee, S., & Warnick, R. (2016). Attitudinal and situational determinants of self-service technology use. Journal of Hospitality & Tourism Research, 40(2), 236–265. OSCE. (2018). Xenophobia, radicalism, and hate crime in Europe. Annual report. Retrieved from: Palmer, A., Montano, J. J., & Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781–790. Paterson, J. M., & Maker, Y. (2018). Why does artificial intelligence discriminate? Pursuit. The University of Melbourne. Retrieved from Accessed on December 23, 2018. Phillips, P., Zigan, K., Silva, M. M. S., & Schegg, R. (2015). The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis. Tourism Management, 50, 130–141. Pires, J. N. (2007). Industrial robots programming: Building applications for the factories of the future. New York, NY: Springer. Poole, D. L., & Mackworth, A. K. (2017). Artificial intelligence: Foundations of computational agents (2nd ed.). Cambridge: Cambridge University Press. Remus, D., & Levy, F. (2015). Can robots be lawyers? Computers, lawyers, and the practice of law. Retrieved from

36    Stanislav Ivanov and Craig Webster Royakkers, L., & van Est, R. (2016). Just ordinary robots: Automation from love to war. Boca Raton, FL: CRC Press. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Harlow: Pearson Education Limited. Santos Silva, M., Albayrak, T., Caber, M., & Moutinho, L. (2016). Key destination attributes of behavioural intention: An application of neural networks. European Journal of Tourism Research, 14, 16–28. Schommer, E., Patel, V. R., Mouraviev, V., Thomas, C., & Thiel, D. D. (2017). Diffusion of robotic technology into urologic practice has led to improved resident physician robotic skills. Journal of Surgical Education, 74(1), 55–60. Schwab, K. (2016). The fourth industrial revolution. Geneva, Switzerland: World Economic Forum. Sendler, U. (Ed.). (2018). The internet of things: Industrie 4.0 unleashed. Berlin, Germany: Springer Vieweg. Shanahan, M. (2015). The technological singularity. Cambridge, MA: The MIT Press. Shead, S. (2019). World’s first robot hotel fires half of its robots. Forbes. Retrieved from Skilton, M., & Hovsepian, F. (2018). The 4th industrial revolution: Responding to the impact of artificial intelligence on business. Cham, Switzerland: Palgrave Macmillan. Slaughter, D. C., Giles, D. K., & Downey, D. (2008). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61(1), 63–78. Sparrow, R. (2007). Killer robots. Journal of Applied Philosophy, 24(1), 62–77. Stone, W. L. (2005). The history of robotics. In T. R. Kurfess (Ed.), Robotics and automation handbook (pp. 1–12). Boca Raton, FL: CRC Press. Sun, S., Wei, Y., Tsui, K. L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10. Susskind, A. M., & Curry, B. (2016). An examination of customers’ attitudes about tabletop technology in full-service restaurants. Service Science, 8(2), 203–217. Talwar, R. (Ed.). (2015). The future of business. London: Fast Future Publishing. Talwar, R., Wells, S., Whittington, Al, Koury, A., & Romero, M. (2017). The future reinvented. Reimagining life, society, and business. London: Fast Future Publishing. Tian, X., & Pu, Y. (2008). An artificial neural network approach to hotel employee satisfaction: The case of China. Social Behavior and Personality: An International Journal, 36(4), 467–482. Timms, M. J. (2016). Letting artificial intelligence in education out of the box: Educational cobots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701–712. Torres, A. M. (2018). Using a smartphone application as a digital key for hotel guest room and its other app features. International Journal of Advanced Science and Technology, 113, 103–112. Tung, V. W. S., & Au, N. (2018). Exploring customer experiences with robotics in hospitality. International Journal of Contemporary Hospitality Management, 30(7), 2680–2697. doi: Tung, V. W. S., & Law, R. (2017). The potential for tourism and hospitality experience research in human–robot interactions. International Journal of Contemporary Hospitality Management, 29(10), 2498–2513. Tussyadiah I. P., & Park S. (2018). Consumer evaluation of hotel service robots. In B. Stangl, J. Pesonen (Eds.), Information and communication technologies in tourism 2018 (pp. 308–320). Cham, Switzerland: Springer. Tussyadiah, I. P., Zach, F. K., & Wang, J. (2017). Attitudes toward autonomous on demand mobility system: The case of self-driving taxi. In R. Schegg, & B. Strangl (Eds.),

Conceptual Framework of the Use of Robots    37 Proceedings of the international conference on information and communication technologies in tourism 2017, Rome, Italy, 24–26 January (pp. 755–766). Ueda, K., & Kurahashi, S. (2018). Agent-based self-service technology adoption model for air-travelers: Exploring best operational practices. Frontiers in Physics, 6, 5. van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo Arigato Mr. Roboto emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of Service Research, 20(1), 43–58. Walkington, C., & Bernacki, M. L. (2019). Personalizing algebra to students’ individual interests in an intelligent tutoring system: Moderators of impact. International Journal of Artificial Intelligence in Education, 29(1), 58–88. Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination. Computer Networks, 101, 158–168. Webster, C., & Ivanov, S. (2020). Robotics, artificial intelligence, and the evolving nature of work. In B. George, J. & Paul (Eds.), Business transformation in data driven societies [Digital Transformation in Business and Society Theory and Cases]. Palgrave-MacMillan Retrieved from Wei, W., Torres, E., & Hua, N. (2016). Improving consumer commitment through the integration of self-service technologies: A transcendent consumer experience perspective. International Journal of Hospitality Management, 59, 105–115. Whitson, G. M. (2018). Artificial intelligence. In D. R. Franceschetti (Ed.), Principles of robotics and artificial intelligence (pp. 12–17). Ipswich, MA: Salem Press. Wirtz, J., Patterson, P., Kunz, W., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69–80. World Bank. (2019). Fertility rate, total (births per woman). Retrieved from https://data. year=2016 Writer, B. (2019). Lithium-ion batteries. A machine-generated summary of current research. Cham, Switzerland: Springer. Retrieved from content/pdf/10.1007%2F978-3-030-16800-1.pdf Wurman, P. R., D’Andrea, R., & Mountz, M. (2008). Coordinating hundreds of cooperative, autonomous vehicles in warehouses. AI Magazine, 29(1), 9–19. Xiong, Y., Peng, C., Grimstad, L., From, P. J., & Isler, V. (2019). Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper. Computers and Electronics in Agriculture, 157, 392–402. Xu, A., Liu, Z., Guo, Y., Sinha, V., & Akkiraju, R. (2017). A new chatbot for customer service on social media. In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 3506–3510). New York, NY: ACM. Ye, Q., Zhang, Z., & Law, R. (2009). Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Systems with Applications, 36(3 Part 2), 6527–6535. Yu, C. E., & Ngan, H. F. B. (2019). The power of head tilts: Gender and cultural differences of perceived human vs human-like robot smile in service. Tourism Review, 74(3), 428–442. doi:10.1108/TR-07-2018-0097

This page intentionally left blank

Chapter 2

Economic Fundamentals of the Use of Robots, Artificial Intelligence, and Service Automation in Travel, Tourism, and Hospitality Stanislav Ivanov and Craig Webster Introduction From the viewpoint of a travel, tourism, or hospitality (TTH) company robots, self-service kiosks, and AI software packages are considered assets (McLaney & Atrill, 2008). Therefore, a company would invest in them if the investment improves its financial performance and creates value for its shareholders (Berk & DeMarzo, 2017). If the introduction of robots, artificial intelligence, and service automation (RAISA) does not contribute positively to the financial results of a company (e.g., by generating revenues, cutting costs, improving competitiveness, and/or guest experience, among others), it may not have economic stimuli for their adoption. That is why, this chapter focuses on the economic aspects of the use of RAISA technologies by TTH companies. It elaborates on the decisions managers need to make and the factors they have to consider when implementing cost-benefit analysis of the use of RAISA in their companies (Ivanov & Webster, 2018). The decisions are neither simple nor easy. Often, basic economic reasons (e.g., lack of value for customers or high costs) sink RAISA projects. For example, in a highly commented move, in January 2019 Henn-na Hotel stopped using nearly half of its robots because they reportedly created more work for human employees and caused problems to guests (Shead, 2019). Similarly, cheaper and more skilled competitors like Amazon’s Echo, kicked Jibo social robot out of the market (Mitchell, 2018). Therefore, it is important that managers of TTH companies do not follow the media hype on RAISA technologies, rather evaluate what will be the specific financial and non-financial costs and benefits from the introduction of a particular RAISA technology (e.g., a self-check-in kiosk at the reception, a robot concierge, or a new revenue management software with

Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality, 39–55 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78756-687-320191002

40    Stanislav Ivanov and Craig Webster artificial intelligence) in their particular company. In may turn out that a particular RAISA technology would be worth introducing in one company, but not in another one. That is why, TTH managers need to assess various factors such as customers’ preferences toward automation of specific activities (Ivanov & Webster, 2019a, 2019b), characteristics of the company, its positioning, employees’ attitudes toward automation and potential resistance, and other factors elaborated further in this chapter, when taking the decision to invest in RAISA technologies.

Economic Fundamentals of RAISA – A Framework Fig. 1 presents the economic framework of RAISA adoption in TTH companies. It steps on and is a continuation of the conceptual framework of the use of RAISA technologies in TTH, elaborated in Chapter 1 (see Fig. 16 in Chapter 1). The decision to invest in RAISA technologies is based on cost-benefit analysis (see section “Cost-Benefit Analysis of Investing in RAISA Technologies”). Managers of a TTH company evaluate the financial and non-financial benefits and costs related to the investments in a specific RAISA technology. Most importantly, they will decide to invest in RAISA if the use of RAISA in a particular company improves its financial performance and creates value for its shareholders. The financial performance is increased if a specific RAISA technology contributes positively to the competitiveness of the company, compared to other TTH companies that serve the same market segments, and if it improves the business process in the company (operations, human resources management, marketing, and financial management). For example, the use of a particular RAISA technology may lead to decreased costs by eliminating unnecessary steps in the service-delivery process, use of fewer employees, or by transferring some of the activities to consumers through self-service technologies, and involving them in co-creation of TTH experiences. In the latter case, customers are transformed into “prosumers” (= “producers” + “consumers”) of TTH services (Ivanov, 2019). The particular RAISA solution may also increase revenues (e.g., generating higher sales volumes and/or sales at higher prices) or improve perceived service quality (see Table 1 in Chapter 1). A TTH company uses human employees and RAISA technologies as production factors to deliver services to tourists. The economic relationship between human employees and RAISA is a complex one but, in general, its essence is the degree to which RAISA technology enhances or substitutes human employees. Factors such as productivity of RAISA and human employees, marginal rate of substitution, costs, economies of scale and scope, service capacity of the TTH company, deskilling and up-skilling of jobs, and possibility of automation of specific tasks compared to automation of jobs, determine the relationship and will be elaborated further in the text. The use of human employees generates demand for them on the labor market. The demand for labor faces the supply of human employees in terms of their quantity (number), skills and required wages, and establishes equilibrium on the labor market. In a similar manner, the decision of a TTH company to use RAISA creates demand for RAISA

Economic Fundamentals of the Use of Robots    41 Demand for TTH products



Service proces s partici Perceive pation d service quality

Service proces s partici Perceive pation d service quality Willingne ss to pay

Human resources

Marketing Busin ess proc esses

Willingne ss to pay




Cost-benefit analysis


Human resources

Marketing Busin ess proc esses

Financial performance



Decision to invest

Production factors Substitution vs. Enhancement

Human employees

Demand for human employees

Productivity Marginal rate of substitution Total, fixed, variable, average and marginal costs Economies of scale Economies of scope Service capacity Automation of tasks vs. automation of jobs Deskilling vs. Upskilling

Supply of human employees


Demand for RAISA

Supply of RAISA Technical characteristics

Number Skills


Mutual influences

Required user skills Prices

Fig. 1.  Economic Framework of RAISA Adoption in TTH. technologies. The supply of RAISA technologies, with their technological characteristics, required user skills, and prices, will be the limiting factor on what is practically achievable through RAISA in a TTH company. The supply of human employees and the supply of RAISA technologies are mutually dependent. For example, RAISA technologies put specific requirements about the skills of the employees who are going to use them – for example, TTH employees may be required to have more skills in order to use a particular RAISA solution (up-skilling), hence decreasing the potential supply of human employees with

42    Stanislav Ivanov and Craig Webster such skills. Alternatively, RAISA technologies may simplify the operations and put lower skill requirements on human employees (deskilling), thus expanding the supply of potential human employees. Additionally, high human employee wages make RAISA solutions more price competitive and increase their supply. Moreover, the use of RAISA has a negative impact on wages as prior studies have already found (DeCanio, 2016) although some economists do not necessarily agree that robots and automation should be blamed for lower wages, but power and politics (Krugman, 2019). We now turn to elaborate on each of the elements of the economic framework of RAISA.

Cost-benefit Analysis of Investing in RAISA Technologies RAISA technologies are long-term (non-current) assets that require considerable amount of financial resources to purchase and maintain. That is why, the decision to invest in them needs to be well justified in financial terms. However, the evaluation of their attractiveness as an investment opportunity should go beyond the financial metrics such as Net Present Value, Internal Rate of Return, Payback Period, and adopt a broader perspective. This section outlines the financial and non-financial benefits and costs that TTH managers need to consider when they take a decision to use RAISA (Ivanov & Webster, 2018; Kulatilaka, 1984; Liu & Hung, 2019). It should be emphasized that the costs and benefits would be different and specific for each RAISA technology and would be different and specific for each TTH company – not all RAISA solutions would be evaluated in the same way by every TTH company.

Financial and Non-financial Benefits of RAISA Adoption The most obvious financial benefit is the labor costs savings due to the advantages of RAISA technologies – robots, kiosks, or chatbots work 24/7, and have very high service capacity (i.e., they can serve numerous customers simultaneously or for a particular period of time). Labor costs (salaries/wages, social security and health insurance payments, labor-related taxes, and pension contributions) could be decreased in the most direct way by reducing the number of human employees needed in various positions by outsourcing tasks to RAISA. For example, instead of a person sitting in a concierge position for a number of hours a day, a hotel can use a chatbot or a self-service kiosk that can operate well beyond the standard hours of a 40-hour work week of a human employee. In fact, a chatbots concierge can operate 24 hours a day, 7 days a week and communicate with numerous customers simultaneously. A hotel would need at least five human employees if its managers wants to provide a 24/7 human-delivered concierge service. Of course, a chatbot cannot provide that flexibility of a concierge service that a human concierge can do, but it can successfully provide much of the information hotel guests would need (e.g., information about the hotel, transportation, attractions, or events in the destinations) and even make bookings (Oh, 2018). Therefore, the adoption of a chatbot (digital concierge) may have a direct financial benefit

Economic Fundamentals of the Use of Robots    43 for a hotel in the form of saved labor costs. In a similar vein, a conveyor belt reduces the number of human waiters a sushi restaurant needs, while self-checkin kiosks, baggage self-drop-off counters, and self-boarding machines reduce the number of human employees that airports need to serve the same or an increasing number of passengers. In addition, besides the direct labor costs savings through decreased number of human employees, the substitution of human employees with RAISA technologies may have some indirect financial benefits by decreasing other labor-related costs such as costs for employee benefits and perks (a car, a business mobile phone, or a card for a spa or a fitness center), or space dedicated to employees, such as break rooms. So, while wages are the most probable first reduction that should be considered, there is also the issue of the costs of employing humans, many of which are indirect. For example, an indirect result of relegating more tasks to RAISA is that the staff of Human Resource departments would be reduced, since the supporting paperwork and training would be vastly reduced when more tasks are done by robots, kiosks, and artificial intelligence, rather than humans. However, labor costs saving can be achieved not only by reducing the number of employees, but also by increasing the productivity of human employees as well. The use of a room service robot, for instance, allows the F&B staff of a hotel to serve more guests with the same number of employees, thus decreasing the labor costs per served customer. Another financial benefit is that the introduction of RAISA could contribute to sales due to the following reasons. First, as already discussed, RAISA technologies increase the service capacity of TTH companies, they can speed up the service process, shorten queues, and decrease customers’ waiting time. For example, a set of several tens of automated ticket kiosks at theme parks, strategically located in several clusters, can easily handle the flow of thousands of customers per day without much human intervention. Additionally, within the theme park itself the use of kiosks, vending machines, delivery robots, and self-scan apps saves time to park visitors and they spend more for food, beverages, and merchandise within the park (Omnico, 2017). Second, RAISA technologies can make the service process funny and entertaining, thus stimulating customer purchases (see Chtourou & Souiden, 2010, for a detailed discussion on the inclusion of “fun” into technology acceptance models). For instance, Tanuki restaurant in Dubai Mall uses a robot to welcome guests, to present the menu, and offer discount vouchers. Additionally, the robot dances, recognizes and addresses guests by name during their repeat visits. Furthermore, hotel guests’ curiosity to see a robot deliver their orders may result in increased room service sales. The adoption of RAISA technologies has significant non-financial benefits for TTH companies that have indirect financial percussions as well. Most importantly, RAISA technologies improve the quality of employees’ work. RAISA help employees to avoid or decrease the mistakes in the service process, for example, recording wrong order in a restaurant or inputting wrong customer data in hotel’s property management system (Ivanov & Russell, 2018), thus decreasing costs for serving customers. Moreover, improved demand forecasts due to the use of advanced revenue management software (Ivanov, 2014), can help hotel managers

44    Stanislav Ivanov and Craig Webster take better pricing decisions, and improve the financial performance of their properties in terms of occupancy, revenue, and profit. RAISA technologies also save time to employees, liberating them from 3D tasks (dull, dirty, and dangerous) and allowing them to focus on more revenue generating activities. For example, a room service robot eliminates the necessity of a human employee to deliver the order and saves time that may be used for other tasks in the F&B department. Vacuum cleaning and pool cleaning robots may help shorten the time necessary to clean the rooms, corridors, lobby, restaurants, and swimming pool of a hotel, because they will take over a time-consuming and tedious task from the human employees in the Housekeeping department, who will have more time for other tasks which robots cannot currently perform – for example, preparing beds and pillow menus in guest rooms. From a marketing perspective, the use of RAISA could contribute to positive publicity and word-of-mouth, especially if that technology is extremely novel. The company can use this in its brand positioning by presenting itself as an early adopter of new technologies and create image of innovate high-tech TTH company. The first robotized hotel (Henn-na Hotel in Japan,, for example, received unprecedented media coverage since its opening in 2015 due to its novelty, much more than its advertising budget could buy. The company then has the opportunity to monetize its popularity by attracting more customers at higher prices. Furthermore, RAISA would enhance perceived service quality with attractive, efficient, and interactive ways of delivering service, engagement, and communications with customers (Kuo, Chen, & Tseng, 2017). Many of the technologies such as chatbots, robots, and self-service kiosks could have multilingual capacities beyond the limited linguistic capabilities of human employees. Therefore, RAISA technologies could help overcome some of the linguistic barriers in the interactions between the TTH company and its customers. Finally, one of the non-financial benefits of using RAISA technologies is the savings of time, energy, and costs that go into the human resource management. RAISA help solve some the problems with hiring and firing of employees, especially the seasonal employees. Hiring and firing human employees has many legal considerations, such as the legality of the labor being hired, the legality of the mode by which labor is being paid, the salary/wage being paid, negotiations with labor unions, compliance with labor law, and the unpleasantness and legal repercussions from the termination of a labor contract. This is especially valid for countries with complicated labor laws, inflexible bureaucracy, and strong labor unions. The complications of using human labor, which includes personal frictions, risks of poor behavior in the workplace, the time and effort in recruiting labor, and many other labor-related issues, can be avoided, even partially by the use of RAISA technologies, although the latter will require time and effort for upgrades, repairs, and replacement. “Firing” a robot, a kiosk, or a chatbot, if this term can be used for the case of RAISA technologies at all, is fairly easy – it just needs to be turned off. There will be no complaints by the robot/kiosk/chatbot, no strikes, no accusations in discrimination, no protests from the side of labor unions, or “robotic” unions. Therefore, RAISA technologies can help reduce some of the problems HR managers have.

Economic Fundamentals of the Use of Robots    45 Financial and Non-financial Costs of RAISA Adoption Buying, using and maintaining a robot, kiosk, or an AI software package is not cheap for the TTH company and requires various financial costs that need to be considered when taking a decision for adoption of a particular RAISA technology: ⦁⦁ Acquisition costs – these include costs for purchasing a robot or kiosk, for



⦁⦁ ⦁⦁

purchasing a chatbots, or payment for AI software development. The acquisition costs can be significant. A quick search of online retailers reveals that a self-check-in kiosk can easily cost above 1,000 USD, although cheaper and more basic versions also exist. A restaurant waiter robot can be purchased at prices between 2500 and 7500 USD, while more complicated humanoid robots (e.g., Pepper by SoftBank Robotics) will require much more investment. A basic chatbot can be developed by the TTH company free of charge by using some of the chatbots platforms such as Chatfuel (, Aivo (, ManyChat (, and Botsify (, among others. These platforms give the opportunity to create free of charge a chatbot with limited functionalities, which may be more than sufficient for a small hotel, restaurant, or travel agent. Paid options can be quite expensive and are justified if only the TTH company is sufficiently large and wants to use the chatbot for tasks beyond the simple provision of information and redirection to its website. Installation costs – a robot or a kiosk needs to be delivered physically to the property where it would operate and be properly installed. For a chatbot, the installation costs are virtually zero because in most platforms the actual installation happens with a click of a mouse by linking a particular social media page to the chatbot. Maintenance costs – these costs include electricity consumption of the robot/ kiosk, spare parts, periodic maintenance, repair works if the machines break down, etc. The direct financial maintenance costs might be negligible for a chatbot if TTH company employees can update the information in it, but might be high if the chatbot blocks need to be reworked by a software company. Software update costs – for installation of new and updated versions of the software used by robots, kiosks, AI packages, chatbots, etc. Costs for adapting the premises to facilitate robot’s mobility – while these costs will be zero for kiosks and chatbots, they have to be considered for mobile robots. Hotels, airports, restaurants, and other companies that use mobile service robots need to invest in the robot-friendliness of their facilities (Ivanov & Webster, 2017), for example, use of proper surface materials for the swimming pool to allow cleaning by a robot, or removing any barriers for robot’s movement and installing sensors on walls to help robot’s navigation throughout the premises of a hotel, airport, or restaurant. Although expensive, the robotfriendliness of hospitality facilities may be one of the competitive advantages TTH companies would boast in the future, because it will allow the companies to use cheaper robots, and will let travelers bring their own mobile robots (for more details, see Ivanov & Webster, 2017).

46    Stanislav Ivanov and Craig Webster ⦁⦁ Costs for hiring specialists to operate and maintain the robots/kiosks/chatbots.

For a small company hiring such a specialist on a full-time contract may not be financially justified and outsourcing the activity is more reasonable. ⦁⦁ Costs for staff training (initial and periodic training) who need to know how to effectively and efficiently operate the new technology. ⦁⦁ Insurance costs should not be neglected. They include insurance for the robots/ kiosks as assets, and insurance for damages caused by a robot during its operation. The above list clearly indicates that financial costs associated with the use of RAISA technologies by TTH companies are significant. This will require companies to think about the costs and benefits of having in-house expertise or using services of outside suppliers. Because of the immense and rapid technological changes, it may be more financially sound not to buy a robot or a kiosk but rather to rent it. Such Robot-as-a-Service (RaaS) or Kiosk-as-a-Service (KaaS) options are already provided by RAISA producers (such as SoftBank Robotics, and Sezam24) and they make RAISA technologies much more affordable for TTH companies. From the viewpoint of a TTH company, outsourcing RAISA would be a good way to use the latest technological solutions without locking much financial resources in outdated technology. It also transforms the high initial investment into more manageable monthly lease payments. Moreover, the monthly payments for RaaS and KaaS allow the direct comparison between RAISA-associated costs and human employee costs, and level off TTH company’s cash flow by eliminating large one-off cash outflows for acquiring robots or kiosks. From the viewpoint of RAISA suppliers, leasing robots and kiosks creates a more regular and long-term cash inflow from fees of the lessees. Therefore, lease may be beneficial for both RAISA suppliers and TTH companies. At any rate, the technological progress drives down costs for RAISA solutions, making them more affordable for TTH companies and improve their price competitiveness compared to human employees. Adopting RAISA technologies in a TTH company may incur some significant non-financial costs. First and foremost, employees may perceive technologies as a threat for their jobs and resist using them. Although RAISA technologies could make the employees more productive, and relieve them from repetitive and dull tasks, employees could consider the introduction of RAISA in a company as an initial step for their substitution. If that is the case, a neo-Luddite boycott of RAISA is easy to expect and company’s investment in RAISA technology remains in vain. Therefore, TTH companies need to develop elaborate programs for introduction of RAISA technologies that consider possible resistance and sabotage by human employees (Ivanov & Webster, 2018). Training staff to use the new technology, open and transparent communications between managers and employees, clear and well communicated plans regarding what is going to happen after the introduction of RAISA technology, reallocating displaced human employees to other job positions or other properties of the company, involvement of employees in the decision-making process, and compensation for employees with terminated labor contracts are just some of the instruments that could be utilized to decrease employees’ resistance to change or at least to mitigate the negative consequences of it.

Economic Fundamentals of the Use of Robots    47 As already discussed in Chapter 1, the adoption of RAISA technologies by TTH companies is influenced by customers’ acceptance of these technologies. Therefore, resistance of customers who do not want to be served by a robot, kiosk, chatbot, or another non-human agent may be a major non-financial cost of RAISA adoption. They may switch to another service provider that focuses on high-touch human-delivered services, and avoid the high-tech technologydelivered services. That is why, the program for introduction of RAISA needs to consider activities for educating consumers to use the new technology, address their concerns, and emphasize on customer value created through these new technologies (saved time, cheaper product, consistent service quality, fun and entertainment in the service process, opportunities for service customization and personalization, customer control over the service-delivery process, etc.). However, despite all the efforts, resistance by some customers is inevitable and they may switch to competitors, thus eroding the customer base of the TTH company. This may be accompanied by negative publicity because a TTH company that adopts RAISA solutions may be perceived as a company that puts profits before its human employees and customers. While employees and customers may resist the use of RAISA for many reasons, it may also be that there is a generalized public resistance to the use of RAISA. Thus, public opinion may be a non-financial cost and a threat to the implementation of RAISA technologies in TTH. Public opinion may be considered to be suspicious of, for example, self-driving cars (Abraham et al., 2017; Tussyadiah, Zach, & Wang, 2017) or privacy protection (Tussyadiah, Li, & Miller, 2019), and it could pressure legislators to demand greater restrictions on the technology or even outright bans. There are embedded interests that are the natural nemesis of RAISA technologies, namely organized labor. To deal with this, RAISA manufacturers, supported by TTH companies, may have to spend time and effort to lobby regulatory agencies, labor unions, and the general public to educate and convince them that RAISA technologies are safe, helpful to the public, and do not need unneeded regulation. Finally, adoption of RAISA technologies may make a TTH company highly dependent on its supplier of such technology. When the products of RAISA manufacturers are not compatible of each other, a TTH company finds itself in a vendor lock-in situation (Farrell & Klemperer, 2007; Simpson, 2019) – the switching costs for changing a supplier a prohibitively high and a TTH company continues to use the services of its RAISA vendor, although the quality of these services may be falling and newer RAISA solutions may be available on the market. Therefore, before choosing a RAISA supplier, a TTH company must assess the costs associated with terminating the relationships with that supplier and moving to another one.

Human Employees and RAISA Technologies – Substitution Versus Enhancement One of the main economic questions regarding RAISA technologies is whether they enhance or substitute human employees. At first glance, the answer seems obvious very straightforward – if RAISA technologies improve the productivity

48    Stanislav Ivanov and Craig Webster of human employees in TTH and allow them to serve more guests and do this more efficiently without decreasing their number, we can say that RAISA technologies enhance human employees. If, on the other hand, RAISA technologies lead to a decrease in the number of human employees in a TTH company, we can say that they substitute human employees. However, the answer is neither easy, nor simple. In practice, RAISA technologies both enhance and substitute human employees simultaneously. For instance, one self-ordering kiosk in a fast food restaurant can help staff during busy periods, but more kiosks may lead to firing of some of them, that is, some employees are substituted while those that remain are enhanced. The balance between these two effects depends on various considerations discussed below. First, we need to answer whether RAISA technologies can really automate whole jobs. In a recent highly cited publication, Frey and Osborne (2017) report that 47% of US jobs are susceptible to computerization. Every job is a set of tasks that employees need to perform. When we talk about automation, we usually mean automation of tasks (inputting data in a reservation system, cleaning the floor, delivering a pizza, producing a sales forecast, etc.), rather than automation of whole jobs. By transferring some of the tasks to RAISA technologies, the set of tasks that humans need to perform decreases. This means that the particular jobs can be performed by less trained human employee (a process called “deskilling” – see Chapter 1) who will receive a low salary. For the TTH company, this means that it can rely on a larger pool of potential employees and minimize its dependency on high-skilled labor, but if the job and its respective salary are not attractive for human employees the company risks not finding someone to do it. If most of the tasks attributable to a job position can be performed by technology, the whole job will be automated and the human employees will be replaced. Therefore, deskilling of jobs is the pathway to substitution of human employees by automation technologies, provided a TTH company can afford an investment in automation technologies. If the tasks of a job cannot be easily performed by RAISA technologies, human employees will continue to perform it with the help of RAISA, that is, they are being enhanced by RAISA. However, human employees will need to upgrade their skills in order to be able to effectively and efficiently use the new technology – a process called “up-skilling.” Therefore, enhancement of employees by RAISA is accompanied by improvement of their skills. In the long-term, technological progress will lead to more and more tasks being automated and transferred to RAISA; hence, we can expect significant substitution of human employees for the currently existing jobs, that is, the substitution effect may prevail over the enhancement effect. In extreme cases, this will lead to the proliferation of fully automated zero-employee hotels and restaurants. However, this does not mean that all TTH jobs will be eliminated – those involving emotional intelligence, communication skills, creativity, and innovation will continue to be in demand. New TTH jobs will also appear that require TTH employee to upgrade further their knowledge and skills in fields such as software engineering, robotics, data analytics, marketing, or robot repair. Second, the balance between substitution and enhancement effect will depend on the relative productivity of RAISA and human employees. Productivity is

Economic Fundamentals of the Use of Robots    49 measured by the amount of output per unit of input (Low, 2000). Within TTH the input, output and productivity can be measured by various monetary and non-monetary variables some of which are presented in Table 1 (Barros & Alves, 2004; Ivanov, 2014). However, currently used metrics mostly reflect the productivity of human employees or company as a whole. The proposed metrics in italic can be used to measure input, output, and productivity of RAISA technologies. If the revenue per dollar costs for RAISA technologies are greater than the revenue per dollar of labor costs, obviously RAISA technologies are more productive, meaning that the TTH company would have economic stimuli to use them instead of human employees. From an accounting point of view, the costs and revenues associated to RAISA technologies need to be clearly determined in order for managers to take appropriate decision for their adoption or not. For some costs (acquisition costs, installation costs, etc.), there is no doubt, but this is not the case with staff training costs – they can be considered both as laborrelated costs (because they refer to training of human employees) and RAISArelated costs (because these costs are made for training employees to use RAISA). In a similar vein, some of the revenues such as sales via kiosks and chatbots can be clearly attributed to RAISA technology, while others such as revenue from robot-delivered room service are a result of the combined contribution of human employees and robots. That is why, TTH companies need to have a clear and consistent accounting policy regarding the measuring, recording, and classifying of costs and revenues attributable to human employees and RAISA technology, in order to measure correctly their productivity. Third, the effect of the use of RAISA technologies depends on the service capacity of a company. Slack, Brandon-Jones, and Johnston (2013, p. 324) define capacity as “the maximum level of value-added activity over a period of time that the process can achieve under normal operating conditions.” Within TTH industries, capacity is the number of customers that can be served simultaneously (e.g., number of rooms in a hotel, number of seats in a restaurant) and/or for a given period of time (e.g., number of passengers that can be served at an airport per year). The cost structure of TTH companies, with its high fixed and low variable costs (Ivanov, 2014), means that they experience economies of scale and have stimuli to increase their capacity and its utilization in order to distribute fixed costs among larger sales volume and decrease the costs of serving one customer. If RAISA technologies can expand the service capacity (e.g., online bookings of tours and hotel accommodation via travel agency chatbots) or its utilization (e.g., a robot for room service delivery), without the need to hire additional staff or when the marginal revenue RAISA generate outweigh the marginal costs for additional human employees, RAISA will decrease the overall average costs to serve one customer. In that situation, the enhancement effect will prevail. When a service or a task has a fixed and well-utilized capacity that cannot be increased by RAISA technology (e.g., a hotel with a very high occupancy rate), or the maximum demand is limited by that fixed capacity (e.g., concierge services in a hotel), the use of kiosks, robots, or chatbots may not attract much additional number of customers. Hence, the focus of RAISA adoption would not be expanding the service capacity but to make the service process more efficient (e.g., by use of

50    Stanislav Ivanov and Craig Webster Table 1.  Sample Monetary and Non-monetary Variables Used to Measure Input, Output, and Productivity in Travel, Tourism, and Hospitality. Monetary Variables

Non-monetary Variables


• Fixed costs • Variable costs • Labor costs • Non-labor related costs • Costs of goods sold • Energy costs • Expenditures by department (e.g., Rooms Division, F&B, and casino) • RAISA technology related costs

• Number of employees • Number of rooms in a hotel • Number of seats in a restaurant • Number of robots/kiosks


• Total revenues • Revenues by revenue center (e.g., Rooms Division, F&B, and casino)

• Number of served customers • Number of overnights • Market share •N  umber of robot/kiosk/ chatbot interactions with customers

Productivity • Total revenue per employee • Total revenue per dollar of labor costs • Department revenues per employee • Department revenues per dollar of labor costs • Total revenue per available room • Revenue per available room • Revenue per available seat • Average daily rate • Gross operating profit per available room • Revenue per available seat-hour • Costs per one served customer • Revenue per robot/kiosk/chatbot •R  evenue per dollar costs for RAISA technologies •C  osts per one served customer by RAISA technologies

• Number of served customers per employee • Number of served • Number of overnights per employee • Occupancy rate • Table turnover •N  umber of served customers per robot / kiosk •N  umber of customers served per dollar costs for RAISA technologies •C  onversion rate (share of customers who bought a service after interaction with a robot/kiosk/chatbot)

self-check-in kiosk instead of check-in by a receptionist) – hence, the substitution effect might be stronger than enhancement effect. The use of RAISA technologies has significant external economic implications. When a TTH company decides to use a particular RAISA technology, it generates demand for it and helps with its popularization in the industry.

Economic Fundamentals of the Use of Robots    51 Obviously, one installed robot, kiosk, voice-controlled digital assistant, or chatbot cannot make a tangible change, because there needs to be a critical mass of such technologies introduced by TTH companies in order to have a significant impact on the sales of such technologies. In their search for market within TTH, RAISA suppliers adapt their products to serve the needs of the sector and partner with large corporations (e.g., hotel chains) that can provide that critical mass of sales. Considering that large technological companies enjoy economies of scale as well, the additional demand that TTH companies create for their products helps decrease the costs of RAISA solutions, thus making technology even more price competitive compared to human employees and affordable to smaller TTH companies as well. Furthermore, the use of RAISA changes the demand and salaries for specific job positions for human employees – the tasks included in job descriptions change and the skills requirements toward potential candidates. When a job is deskilled through the elimination and simplification of tasks included in it, the salary for it falls, or at least does not increase in line with other salaries in the company. When a job is up-skilled through additional skill requirements for human employees who perform it, salaries would increase. Of course, not in all cases up-skilling would lead to salary increases. When a technology becomes widespread in the industry, the ability to use it becomes part of the general requirements and does not give competitive advantage to people who can use it. For example, the ability to use an operating system, spreadsheet or text processing program, or a computer reservation system does not give a sustainable competitive advantage to a human employee, because there are millions of others who have the same skills, although probably not at the same high level. On the other hand, skills related to data analytics, programing a robot, or using revenue-management software are rarer and having them may lead to a higher salary.

What is the Adoption of RAISA Technologies Worth? There are many different monetary and non-monetary benefits and costs of the use of RAISA technologies as the preceding analysis elaborated. The decision to adopt RAISA technologies should not be taken lightly, since it is a major financial investment and creates all sorts of changes in the culture of the organization. Because all firms have a different size and culture and work in with different market segments, a “one-size fits all” response is not appropriate. A TTH company needs to consider the following factors that influence the cost-benefit ratio of adoption of a particular RAISA technology (see also Ivanov & Webster, 2018). One of the key considerations that TTH companies will have to consider is their size, market positioning, and corporate culture. It would be a mistake for companies to embrace RAISA at any cost just because it is a trendy thing to do; on the contrary – a company needs to invest in RAISA technologies if only such a decision supports its market positioning, is in line with its corporate culture, and RAISA technologies can contribute to positive experiences of their target market segments. For a small family-owned hotel or restaurant, for example, it may not make sense to invest in automation, since such an investment is expensive

52    Stanislav Ivanov and Craig Webster and most effective in large properties where economy of scale can be attained. For small properties, investing in expensive new technologies may be cost-prohibitive and may not affect the bottom line in a positive way. Furthermore, in a small-scale operation the investment in RAISA technologies may face a disappointment on the part of customers who expect high-touch human contact, rather than a high-tech interaction with a kiosk or a robot. On the other hand, large hotels, airports, and theme parts that deal with large numbers of customers benefit from the economies of scale and for them the financial benefits from adoption of RAISA technologies will be significant. For a TTH company that is positioned as an innovative company, its makes sense to invest in new technologies, including RAISA, because the investment will support its market positioning. If its positioning is based on personalized high-touch service (e.g., ultra deluxe hotels in Jumeira Beach, Dubai), it would be best if the company invests in RAISA technologies only for its back office operations. The technological complexity of RAISA solutions is another major consideration. One of the most fundamental factors with regards to the adoption of new technology is the ease of use of the new technology (Venkatesh & Davis, 2000). While there will be training for employees for almost any technological innovation that will be used in the workplace, those technologies that are easy to use by customers and employees, require the least training and can be serviced/repaired quickly and cheaply will have an advantage and will appeal to TTH companies. The cultural characteristics of customers and service providers may play a key role in terms of how RAISA technologies are perceived and used (Lee, Trimi, & Kim, 2013). While some societies seem to be very accepting of innovations and new technologies (such as Japan, South Korea, and the USA), there are more conservative cultures that would likely be skeptical of or simply reject such new technologies (see, e.g., the ranking of the countries in World Economic Forum 2017’s Global Information Technology Report). It is likely no coincidence that the first predominantly robot-operated hotel in the world was in Japan, a country with a culture that is accepting of new technologies and robots. Those countries with veneration for science fiction and engineering will likely be those where the introduction of RAISA technologies by TTH companies will face least resistance. The safety characteristics should be a critical concern for some RAISA technologies, especially those that have the potential for causing physical harm to humans (customers or employees). While such innovations as chatbots may annoy some customers, it is unlikely that a person would suffer physical harm from them. The same could not be said for a room-service delivery robot or an automated vacuuming machine. The first generation of widely deployed robots will meet with hiccups until design problems are identified when they are employed at a large scale. There will be issues with food safety for robotic chefs and bartenders and concerns about people tripping over service robots that meander the hallways doing their jobs such as delivering food or vacuuming the floors. Despite safety tests and certifications of robots, complete elimination of robot malfunctions is impossible, and human injuries and deaths are inevitable.

Economic Fundamentals of the Use of Robots    53 Public opinion is very sensitive toward the safety characteristics of autonomous vehicles in particular, especially after some accidents that led to loss of human lives (Economist, 2018). The fear of potential harm, justified or not, caused by a robot could stimulate technophobia among customers and employees, and hinder RAISA adoption in TTH.

Conclusion This chapter contributes to the discussion of the implementation of RAISA in TTH by looking into the financial and non-financial costs and benefits to the adoption of RAISA for firms. The discussion illustrates that there are many considerations that companies have to make when deciding whether to incorporate new technologies, to wait for a better time to adopt them, or to simply reject using such technologies. It would be a mistake for TTH companies to blindly adopt RAISA technologies and it would be foolish for them not to entertain the notion that some of the technologies could improve their business operations at little cost. Extreme technophobia and extreme technophilia in decision-making are very real dangers in an era in which massive technological gains have been made but the technologies are far from perfect and come at a cost. Hype and high levels of expectations for new technologies often lead to disappointment. Shortly after the introduction of new technologies and the inflated expectations that people have with them, a trough of disillusionment usually follows (Gartner, 2016). It is understandable that RAISA suppliers will over-emphasize the benefits that the new technologies will offer businesses and their management. The benefits and the novelty of RAISA technologies, and the promises of those who have a financial interest in promoting such new technologies, may entice TTH managers to adopt technological solutions without fully considering the practical implications or the externalities involved with their use. The fact that the first hotel almost entirely staffed by robots, Henn-na Hotel “laid off ” about half of its staff in 2019, less than five years after opening its doors, due to customer complaints, is a good example that hype and excessive expectations should not interfere in decision process whether to adopt a particular technology, but rather comprehensive, elaborate, and in-depth cost-benefit analysis needs to be implemented. It is undisputable that technological progress is unstoppable. Robots, AI algorithms, chatbots, and kiosks will have improved technical characteristics and will be more affordable, which will make them more widely used in TTH (Ivanov, Webster, & Berezina, 2017; Murphy, Hofacker, & Gretzel, 2017). The introduction of RAISA will lead to inevitable loss of jobs, but many new jobs that currently do not exist will appear. Probably, we will not feel sorry for dish washers and cleaners if robots do their work, or if we use chatbots and kiosks that provide a quicker, cheaper, and more efficient service than human employees. However, we should not forget that technology is a tool, not a goal. That is why, the economic assessment of the costs and benefits of RAISA adoption needs to go beyond the usual financial metrics and have a broader perspective presented in this chapter.

54    Stanislav Ivanov and Craig Webster

References Abraham, H., Reimer, B., Seppelt, B., Fitzgerald, C., Mehler, B., & Coughlin, J. F. (2017). Consumer interest in automation: Preliminary observations exploring a year’s change. MIT AgeLab White Paper. Retrieved from MIT%20-%20NEMPA%20White%20Paper%20FINAL.pdf Barros, C. P., & Alves, F. P. (2004). Productivity in the tourism industry. International Advances in Economic Research, 10(3), 215–225. Berk, J., & DeMarzo, P. (2017). Corporate finance. (4th Global ed.). Harlow: Pearson. Chtourou, M. S., & Souiden, N. (2010). Rethinking the TAM model: Time to consider fun. Journal of Consumer Marketing, 27(4), 336–344. DeCanio, S. J. (2016). Robots and humans—Complements or substitutes? Journal of Macroeconomics, 49, 280–291. Economist (2018). Why Uber’s self-driving car killed a pedestrian. Retrieved from https:// Accessed on April 14, 2019. Farrell, J., & Klemperer, P. (2007). In M. Armstrong & R. Porter (Eds.), Coordination and lock-in: Competition with switching costs and network effects. Handbook of industrial organization (Vol. 3, pp. 1967–2072). Amsterdam: North-Holland. Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. Gartner. (2016). Gartner’s 2016 hype cycle for emerging technologies identifies three key trends that organizations must track to gain competitive advantage. Retrieved from Accessed on May 30, 2017. Ivanov, S. (2014). Hotel revenue management: From theory to practice. Varna, Bulgaria: Zangador. Ivanov, S., & Russell, C. (2018). Worth IT? The usefulness of information technology to read ID cards and passports for hoteliers in Bulgaria. Tourism and Hospitality Management 24(2), 375–386 Ivanov, S., & Webster, C. (2017). Designing robot-friendly hospitality facilities. In Proceedings of the scientific conference on “Tourism. Innovations. Strategies”, 13–14 October, Bourgas, Bulgaria (pp. 74–81). Ivanov, S., & Webster, C. (2018). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies – A cost-benefit analysis. In V. Marinov, M. Vodenska, M. Assenova, & E. Dogramadjieva (Eds.), Traditions and innovations in contemporary tourism (pp. 190–203). Cambridge: Cambridge Scholars Publishing. Ivanov, S., & Webster, C. (2019a). What should robots do? A comparative analysis of industry professionals, educators and tourists. In J. Pesonen & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, Nicosia, Cyprus, 30 January–01 February (pp. 249–262). Cham, Switzerland: Springer. Ivanov, S., & Webster, C. (2019b). Perceived appropriateness and intention to use service robots in tourism. In J. Pesonen & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, Nicosia, Cyprus, 30 January–01 February (pp. 237–248). Cham, Switzerland: Springer. Ivanov, S., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27/28, 1501–1517. Krugman, P. (2019). Don’t blame robots for low wages. The New York Times. Retrieved from

Economic Fundamentals of the Use of Robots    55 Kulatilaka, N. (1984). Financial, economic and strategic issues concerning the decision to invest in advanced automation. International Journal of Production Research, 22(6), 949–968. Kuo, C.-M., Chen, L.-C., & Tseng, C.-Y. (2017). Investigating an innovative service with hospitality robots. International Journal of Contemporary Hospitality Management, 29(5), 1305–1321. Lee, S. G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business, 48(1), 20–29. Liu, C., & Hung, K. (2019). Understanding self-service technology in hotels in China: Technology affordances and constraints. In J. Pesonen & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, Nicosia, Cyprus, 30 January–01 February (pp. 225–236). Cham, Switzerland: Springer. Low, L. (2000). Economics of information technology and the media. Singapore, Singapore: Singapore University Press and World Scientific Publishing Co. McLaney, E., & Atrill, P. (2008). Accounting: An introduction (4th ed.). Harlow: Prentice Hall. Mitchell, O. (2018). Jibo social robot: Where things went wrong. The Robot Report, 28 June 2018. Retrieved from Murphy, J., Hofacker, C., & Gretzel, U. (2017). Dawning of the age of robots in hospitality and tourism: Challenges for teaching and research. European Journal of Tourism Research, 15, 104–111. Oh, S. (2018). Malaysia Airlines unveils new flight booking chatbot in partnership with Amadeus. Retrieved from Omnico. (2017). Theme park ROI barometer. Retrieved from https://www.omnicogroup. com/wp-content/uploads/2017/12/The-Omnico-Theme-Park-ROI-Barometer.pdf Shead, S. (2019). World’s first robot hotel fires half of its robots. Forbes. Retrieved from Simpson, P. (2019). Buying hospitality technology in the future. Hotel Yearbook 2020. Special edition on technology (pp. 14–16). Lausanne, Switzerland: WIWIH AG. Retrieved from Slack, N., Brandon-Jones, A., & Johnston, R. (2013). Operations management (7th ed.). Harlow: Pearson. Tussyadiah, I., Li, S., & Miller, G. (2019). Privacy protection in tourism: Where we are and where we should be heading for. In J. Pesonen & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, Nicosia, Cyprus, 30 January–01 February (pp. 278–290). Cham, Switzerland: Springer. Tussyadiah, I. P., Zach, F. K., & Wang, J. (2017). Attitudes toward autonomous on demand mobility system: The case of self-driving taxi. In R. Schegg & B. Strangl (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2017, Rome, Italy, 24–26 January (pp. 755–766). Cham, Switzerland: Springer. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. World Economic Forum. (2017). The global information technology report 2016. Geneva, Switzerland: World Economic Forum. Retrieved from docs/GITR2016/WEF_GITR_Full_Report.pdf. Accessed on June 30, 2017.

This page intentionally left blank

Chapter 3

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors: Principles and Practice Petranka Kelly, Jennifer Lawlor and Michael Mulvey Introduction The development of service automation continues to underpin the travel, t­ ourism, and hospitality (TTH) sectors. An example of such service automation is the proliferation of self-service technologies (SSTs), such as online booking facilities, “bag and tag” kiosks at airports, and e-ticketing kiosks, which are the norm in the contemporary tourism sector (Kelly, Lawlor, & Mulvey, 2017; Rosenbaum & Wang, 2015). These technologies are diverse in nature and can be implemented at various stages of a tourism service encounter, that is, reservation, holiday at the destination, and post-holiday experience (Rosenbaum & Wang, 2015). Hence, this chapter examines the principles and practices underpinning the usage of SSTs in the TTH sectors. The chapter begins with a definition and ­classifications of SSTs, followed by the benefits and drawbacks from SST usage for stakeholders in the tourism system. Since customer adoption of SSTs is essential to their successful implementation, a conceptual framework of SST adoption is discussed with a view to identifying an agenda for further research. This model integrates SST-adoption factors with customer SST roles to provide a more nuanced understanding of SST adoption and highlight areas for further research.

Definition and Classifications of SSTs SSTs started to attract research attention in the 1980s with Bateson (1985) ­recognizing automated teller machines (ATMs) and pay-at-the-pump automated facilities as customer self-service options. Dabholkar (1994) introduced the term “technology-based self-service” (TBSS) to reflect self-service processes involving

Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality, 57–78 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78756-687-320191003

58    Petranka Kelly et al. technological interfaces. Dabholkar and Bagozzi (2002, p. 184) define TBSS as including “on-site” and “off-site” elements: Technology-based self-service includes “on-site” options such as touch screens in department stores, information kiosks at hotels, and self-screening in grocery stores and libraries; it also includes “off-site” options such as telephone and on-line banking and shopping on the Internet. “Self-service technologies” is another term reflecting the self-service customertechnology interaction, which was introduced by Meuter, Ostrom, Bitner, and Brown (2000, p. 50) and defined as: Technological interfaces that enable customers to produce a service independent of direct service employee involvement. Examples of SSTs include automated teller machines (ATMs), automated hotel checkout, banking by telephone, and services over the Internet, such as Federal Express package tracking and online brokerage services. The labels of “SSTs” and “TBSS” are used interchangeably in the research literature although Reinders, Dabholkar, and Frambach (2008) argue that TBSS is a more accurate term because it accentuates the self-service process rather than the technology. Recent studies have displayed a preference for the term SSTs (see, e.g., Cunningham, Young, & Hu, 2013; Elliott, Hall, & Meng, 2013; Leung & Matanda, 2013). Therefore, the term that will be used throughout this chapter is that of SSTs in order to reflect the latter school of thought. SSTs have been increasingly embedded in organizations operating within the tourism industry and are changing the roles of customers and companies in providing tourism services (Lawlor, 2010). Air travel is perhaps the most forwardthinking transport sector in terms of technology implementation, with airports worldwide offering innovative applications of SSTs throughout the customer journey. The concept of a “smart airport,” where SSTs are introduced at every stage of the customer journey, is already in implementation at Hamad International Airport, Qatar (Al Meer & Manuell, 2017). Dublin International Airport is the first airport outside of the US to implement one of the latest airport technological innovations, namely express self-service bag-drop facilities (Gittens, 2016). Meanwhile, Hitachi has developed a multi-lingual customer assistance robot that is being tested in Japanese airports (BBC News, 2017). A further technological innovation is available at Miami Airport where they are the first to introduce immigration and customers’ clearance facilitated by a mobile phone application (SITA News Release, 2017). Traditional hotel services are being transformed by technologies, such as automated check-in and check-out facilities, automated room service ordering systems, automated messaging services, and automated house-keeping services (Beatson, Lee, & Coote, 2007; Oh, Jeong, & Baloglu, 2013). Furthermore, in

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    59 July 2015, the first robot-staffed hotel, Hen-na, opened in Japan (Rajesh, 2015). The hotel introduced robot front desk check-in and concierges who utilize speech recognition in a number of languages to assist customers (Rajesh, 2015). In the restaurant sector, Kodisoft’s interactive restaurant tables have been implemented in 11 restaurants across Europe and the Middle East (Woods, 2017). These interactive tables allow users to place their orders and offer various games to entertain guests while they wait (Woods, 2017). The pizza chain, Domino’s Pizza, facilitates customer orders via computer, mobile or a smart watch, while customers’ data are carefully analyzed by data scientists to deliver a superior experience (Maze, 2016). The tourism attractions sector is also embracing technology with large ­museums, such as the British Museum and the Brooklyn Museum, offering interactive augmented reality and beacon technology applications (Shu, 2015). These applications guide visitors through the museum and allow users to gain additional information about exhibits. The Louvre Museum in France has partnered with Nintendo to use the 3DS XL technology for their interactive guides (Tieryas, 2015). The overview of SSTs in tourism outlined above reveals that they are very diverse in nature and implementation. Therefore, the remainder of this section will review SST classifications as proposed by the SST literature. One of the first classifications of SSTs is proposed by Dabholkar (1994). She suggests a classification of technology-based services along the dimensions of where and how the ­service is delivered. “Where” the service is delivered divides SSTs into those delivered at the service site and those delivered from the customer’s home or work place (Dabholkar, 1994). For example, an ATM or an airport check-in kiosk would be classified as SSTs at the service site, while internet shopping and ­distance learning would be at the customer’s site (Anselmsson, 2001). “How” the service is delivered includes the options of direct interaction with the technology or indirect contact (Dabholkar, 1994). The SST service may be direct when the customer directly interacts with the technology, such as self-scanning, or indirect when the customer is not in direct interaction with the technology, such as automated time schedules (Anselmsson, 2001). A popular typology of SSTs is proposed by Meuter et al. (2000) who categorize SSTs according to the dimensions of interface (e.g., telephone/interactive voice response, online/internet, interactive kiosks, and video/CD) and purpose (e.g., customer service, transactions, and self-help). The interface dimension reflects the types of technological interfaces that customers interact with, and the dimension of purpose reflects the tasks that customers can achieve by using the SST. Furthermore, Schumann, Wünderlich, and Wangenheim (2012) propose that SSTs can be classified based on the provision of the technological interface by the service provider (i.e., provider-based self-services) or by the consumer (i.e., consumer-based self-services). The ownership of the technology will have an effect on the level of responsibility for the management of technological breakdowns (Schumann et al., 2012). It is noticeable from later reviews of SST interfaces (see, e.g., Castro, ­Atkinson,  & Ezell, 2010) that the video/CD interface is not as prominent and

60    Petranka Kelly et al. other interfaces such as smartphones and mobile devices are discussed. Since tourism traditionally involves a stay away from home, the smartphone SST platform has specific significance to tourism (Gretzel, Sigala, Xiang, & Koo, 2015). Furthermore, recent developments have seen the introduction of the “Internet of Things” (IoT) and robotics to tourism operations (Aluri, 2016; Ivanov & W ­ ebster, 2018; Ivanov, Webster, & Berezina, 2017). SST classifications may have to become even more complicated with the continuous integration of technologies and development of ever more innovative platforms for electronic service. Please refer to Table 1, for examples of SSTs. Although SSTs are diverse in nature, there are some common benefits and drawbacks from their implementation in services, which will be reviewed in the following section.

Benefits and Drawbacks from SST Implementation The introduction of SSTs in a service operation is associated with strategic changes requiring consumers to undertake an active production role and obliging companies to undertake a facilitator role (Hilton, Hughes, Little, & Marandi, 2013). There are certain benefits and drawbacks for both consumers and service providers, which need to be evaluated and understood in order to ensure effective and sustainable SST adoption in tourism organizations (Lawlor, 2010). Although SST implementation may affect all stakeholders in a tourism system, that is, ­service providers, tourists, destination management organizations, and the local population (Boes, Buhalis, & Inversisni, 2017), this section will focus predominantly on service providers and customers as key stakeholders in SSTs.

Benefits for Service Providers A major advantage for service providers in successfully implementing SSTs is encouraging customers to fulfill a “partial employee” role of the company by undertaking some responsibilities previously performed by company employees (Collier & Kimes, 2013). The ensuing reduction of company employees needed in the service operation provides for operational cost savings. Furthermore, SSTs may relieve staff from performing routine duties, and make them available to concentrate on other aspects of the service where personal touch is more important (Lee & Allaway, 2002). SSTs can be the engine of innovation in service creation and delivery; thus, differentiating the company through the development of niche offerings (Oh et al., 2013). For example, the Inamo restaurants in London have become famous because they offer an innovative, technology-facilitated, self-service restaurant experience (Neuhofer, Buhalis, & Ladkin, 2013). Self-service technology usage is part of the core Inamo guest experience and has become a major competitive advantage for the company in offering the world’s first interactive ordering system (Aucoin, 2017). The innovative implementation of SSTs also contributes to the overall image of the provider as a technologically advanced leader in electronic service (Castillo-Manzano & Lopez-Valpuesta, 2013).

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    61 Table 1.  Examples of SSTs. Authors Dabholkar (1994) Anselmsson (2001)



Where is the service delivered (at service site and at customer’s home or work)?

At service site – ATMs, tourist information kiosks, and interactive tables At customer’s site – internet booking, financial transactions, and smartphone apps

How the service is delivered (direct and indirect)?

Direct – self-scanning at airport and online information Indirect – telephone banking and automated time schedules

Purpose (customer service, transactions, and self-help)

Customer service – order status, account information, and ATMs Transactions – financial transactions and ticketing kiosks Self-help – internet information search and tourist information

Interface (telephone/interactive voice response, online/internet, interactive kiosk, and video/CD)

Telephone – telephone banking and information telephone lines Online – retail purchasing and order tracking Kiosk – ATMs, hotel checkouts, and blood pressure machines Video/CD – tax preparation software and CD-based training

Castro et al. (2010)

Interface (electronic kiosks, internet applications, mobile devices, and telephone applications)

Electronic kiosks – photo printing kiosks, food ordering kiosks, and airport kiosks Internet applications – online banking, e-learning, e-commerce, ticketing, and reservation Mobile devices – mobile payments and hotel smart cards Telephone applications – dual tone multi-frequency and interactive voice response

Schumann et al. (2012)

Ownership of the technology (provider-based and customer-based)

Provider-based – ATMs Customer-based – online banking

Meuter et al. (2000)

Source: The authors.

62    Petranka Kelly et al. SSTs also provide an opportunity for extended contact with the customer across multiple channels, which increases cross-channel synergies and opportunities for realizing more service offerings (Van Birgelen, de Jong, & de Ruyter, 2006). When entering information via an SST interface, customers also provide information which can be utilized by service providers in better targeting their customers and offering more customized service (Neuhofer et al., 2013). Furthermore, this information may be integrated into big data systems and used by local authorities to enable their efforts of lean and sustainable development of destinations (Gretzel et al., 2015). For example, an analysis of tourist behavior may be enabled by observing their online research and booking activities (e.g., Ho, Lin, & Chen, 2012).

Benefits for Customers From a user’s perspective, SSTs may be more convenient than their personal alternative because they extend service access and reduce customer effort to obtain the service (Collier & Kimes, 2013). An SST, such as an ATM, makes a service available 24 hours a day, 7 days a week, rather than being restricted to the daytime working hours of the bank. Furthermore, customer involvement in the service delivery may contribute to better customization and satisfaction with the service experience (Lawlor, 2010). This increase in quality, accompanied by lower prices, can lead to higher customer satisfaction and loyalty (Bitner, Ostrom, & Meuter, 2002). The option to complete services at the customer’s convenience may reduce the overall stress and hassle of service encounters (Cunningham et al., 2013). Increased access to the service may also refer to improved accessibility for people with certain ­disabilities, that is, mobility, sight, speech, and hearing (Buhalis & Law, 2008). Furthermore, tourists in a foreign country may find an information kiosk or car rental kiosk with a multitude of language options very helpful (Castro et al., 2010). Using SSTs can also provide some customers with intrinsic benefits such as feelings of independence and enjoyment (Dabholkar, Bobbitt, & Lee, 2003; Meuter, Ostrom, Bitner, & Roundtree, 2003). Bateson (1985) confirmed the existence of a segment of self-service users who enjoy self-service even when there are no particular utilitarian benefits, such as lower price, control, or convenience. Some consumers even admit to using SSTs in order to avoid the personal contact with service employees (Cunningham, Young, & Gerlach, 2009; Dabholkar et al., 2003). This is because customers suggest that they can deliver the service in a more efficient and accurate manner when using SSTs on some occasions (Meuter et al., 2003). Despite all the benefits gained from introducing SSTs in the service encounter, research also demonstrates that certain limitations and drawbacks for stakeholders, in particular customers and service providers, need to be overcome or taken into account. These drawbacks will be discussed in the following sections.

Drawbacks for Service Providers The implementation of SSTs may present certain drawbacks in terms of expense and threats to the quality of the provided service. Service companies may need to change their customer relationship strategies and adapt them to

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    63 the machine-customer encounter (Halstead & Richards, 2014; Schumann et al., 2012). The reduced customer-employee contact leaves fewer chances for early detection of complaints and opportunities for service recovery (Dabholkar & Spaid, 2011). Girman, Keusch, and Kmec (2009) found that complaint rates for minor faults with SSTs were very low, so service providers should be proactive and fix faults in routine checks before they appear rather than wait for them to happen. As such, before implementing SSTs, service managers should consider carefully if the potential savings are going to be higher than the revenue lost to complaints and lost custom (Makarem, Mudambi, & Podoshen, 2009). Furthermore, the immediate job losses associated with SST implementation may affect the image of the business if the transition is not communicated in a positive manner by companies (Castro et al., 2010). Other drawbacks for companies in SST implementation relate to investment expenses, and staff and customer training (Bitner et al., 2002; Lee & Allaway, 2002). If the service technology is not adopted and used effectively by customers, the ­company may face increased expenses because it needs to keep operational staff, as well as pay for the new technology (Lee & Allaway, 2002). In order for customers to be effective in their usage of SSTs, they need to know their production role, be able to perform it, and feel motivated to do so (Meuter et al., 2005). While the technology side of the encounter is wholly within the power of the service provider, the customer side is dependent on the active participation of a human resource which is not officially employed by the organization (Hilton & Hughes, 2013).

Drawbacks for Customers Despite the numerous identified potential benefits for the customer from SST usage, they may not always be perceived as such by customers (Walker, CraigLees, Hecker, & Francis, 2002). Customers avoid SSTs when they do not perceive any benefit from, or need to use them (Gerrard, Cunningham, & Devlin, 2006; Liljander, Gillberg, Gummerus, & Riel, 2006). There are learning barriers which customers need to be willing and able to overcome in order to use the SST (Walker et al., 2002). SSTs also require higher levels of customer participation and responsibility, so they may be perceived as riskier than personal services (Lee & ­Allaway, 2002). As such, SSTs may result in negative psychological outcomes for the customer such as anxiety (Meuter et al., 2003) or embarrassment (Forbes, 2008). Another negative psychological outcome for the customer may be the perceived loss of freedom when service providers force SST usage on them (Reinders et al., 2008). Forced SST usage may also raise technology anxiety levels in customers, especially if they do not have previous experience with the SST or fall-back options are not available (Liu, 2012). Even though SSTs are suggested to increase customization opportunities (Jacob & Rettigner, 2011), they may nevertheless be insufficient in their ability to meet all customer requirements. On such occasions, customers may wish to resort to a personal service channel (Walker et al., 2002). Furthermore, there are perceptions among customers of financial, privacy, and security threats when using SSTs (McKnight, Choudhury, & Kacmar, 2002). There is a risk of ordering the wrong product or service online and having a credit

64    Petranka Kelly et al. card compromized (Cases, 2002). It is also much harder for customers to verify the trustworthiness of online vendors, and thus they may feel they are vulnerable to becoming victims of fraud (Connolly & Bannister, 2008). The high level of failure associated with SSTs still represents a drawback for customer SST usage (Forbes, 2008; Zhu, Nakata, Sivakumar, & Grewal, 2013). Customers experience SST failures associated with “technology failure,” “process failure,” “poor design,” and “customer-driven failure” (Meuter et al., 2000, p. 56). Furthermore, customers may feel abandoned when an SST fails and there are no employees around or another means of contact to solve the problem (Forbes, 2008). Customers may also experience helplessness during SST failures and negative outcomes from inefficient service recovery (Bitner et al., 2002; Forbes, 2008). Despite the numerous benefits for both service providers and customers from SST usage, the identified drawbacks may challenge effective SST adoption and usage. The following section discusses a conceptual model of customer SST adoption.

SST Adoption and Usage by Customers SST adoption by customers is a key component in their successful implementation; hence, the subject has been attracting much research attention since the 1990s (see, e.g., Dabholkar, 1995; Meuter et al., 2000). Fig. 1 presents a conceptual framework proposed by the authors of this chapter which will guide the

Fig. 1.  Conceptual Framework of Customer SST Adoption. Source: The authors.

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    65 discussion of SST adoption research. This framework integrates the main stream of SST adoption factors research with emergent research on customer SST roles. Since SSTs have been perceived as constituting technological innovations in services, a large stream of SST customer behavior research has been based on technology acceptance theories and innovation adoption frameworks (Eastlick, Ratto, Lotz, & Mishra, 2012; Meuter et al., 2005). One of the established theories in SST research is the Technology Acceptance Model (TAM) (Davis, Bagozzi, & Warshaw, 1989). TAM suggests that the extent to which a consumer believes a technology is useful and easy to use helps form an attitude toward that information system and the behavioral intention to use it (Davis et al., 1989). The TAM variables of ease of use and usefulness of the SSTs have proven to be good theoretical foundations for predicting customer acceptance of new technology systems (see, e.g., Gefen, Karahanna, & Straub, 2003; Hsiao & Tang, 2015; Lin & Chang, 2011; Yang, Lee, Park, & Lee, 2014). In addition, situational factors may affect the influence of ease of use and usefulness on the user trial decisions, for example, previous experience, length of queue and other accompanying people (Wang, Harris, & Patterson, 2012). The introduction of a new SST also represents a technological, service innovation; therefore, the Diffusion of Innovations Theory (DOI) (Rogers, 1995) has been adapted by Meuter et al. (2005) and Bitner et al. (2002) as another suitable theoretical foundation to study SST adoption. DOI-based research presents SST adoption as a process consisting of innovation adoption phases, which may be affected by different customer and SST characteristics (Wang et al., 2012). An early conceptualization model of customer adoption of SSTs based on DOI was introduced by Bitner et al. (2002) who propose a six-stage model, namely, awareness, investigation, evaluation, trial, repeated use, and commitment. This SST user adoption process may be influenced by SST characteristics (e.g., compatibility, relative advantage, complexity, observability, trialability, and perceived risk) and individual differences (such as inertia, technology anxiety, need for interaction, previous experience, age, gender, education, and income). A challenge for SST research has been to establish the influence of the various adoption factors on SST adoption and usage. This may partly be explained with the vast variety of SSTs and service situations where they are implemented (Chiu, Fang, & Tseng, 2010; Liljander et al., 2006). Furthermore, SSTs and user experiences of them keep evolving, thus affecting the influence of adoption factors over time (Robertson, McDonald, Leckie, & McQuilken, 2016; Wang et al., 2012). The remainder of this section will discuss seven SST adoption factors featured in seminal research articles on SST adoption, including technology anxiety, technology readiness, preference for personal contact, demographic characteristics, trust, risk, and situational influences. The authors of this chapter reviewed the SST literature over the past 15 years and identified over 60 peer-reviewed publications relating to research into SST adoption factors. This review produced over 20 different SST adoption factors. The seven factors that feature in this chapter were chosen because of their frequent inclusion in SST adoption research and will be reviewed below.

66    Petranka Kelly et al. The first of seven SST adoption factors under consideration is technology anxiety (TA). This is one of the SST adoption factors with most consistent negative influence on SST usage. Meuter et al. (2003) and Lu and Su (2009) report a strong negative impact of TA on SST usage intentions. As such, although the influence of TA on SST usage is explicit, its antecedents require further research (Meuter et al., 2003). For example, the design of the interface, customer training, provision of service choices, and incentives may include possible factors influencing TA (Meuter et al., 2003). The second of the seven SST adoption factors relating to the user perceptions of technology is technology readiness (TR), which is a measure of general customer beliefs and attitudes toward technology. TR was introduced by ­Parasuraman (2000) and it has found extensive application in SST research (see, e.g., Elliott et al., 2013; Liljander et al., 2006). Unlike TA, the findings on the impact of TR on intentions toward using SST are conflicting (Elliott et al., 2013). For example, some customers may experience discomfort with purchasing online, but may feel comfortable purchasing online from a particular company (Chen, Chen, & Chen, 2009). Also, hospitality customers suggest that they may be taking a “technological pause” while on vacation although they display overall positive attitude toward technology and high scores on TR (Rosenbaum & Wang, 2015). The third factor that may impact SST adoption is the user preference for ­personal contact. The findings on the effect of customer preference for personal contact on SST usage have been conflicting, with some studies providing empirical evidence for this influence (see, e.g., Dean, 2008; Ko, 2017; Lee, Cho, Xu, & Fairhurst, 2010), while others finding no significant effects (see, e.g., Eastlick et al., 2012; Curran & Meuter, 2005). The inconsistency in results regarding the influence of preference for personal contact on SST usage has been suggested to be context bound (Eastlick et al., 2012). For example, Eastlick et al. (2012) suggests that the context of their research was characterized by the physical presence of employees, so customers could interact with them if they required staff assistance before they got to the self-service checkout. The fourth of the seven SST adoption factors considered are customer demographic characteristics. Although demographic characteristics of SST users are a convenient way for market segmentation, findings regarding the link between demographics and SST usage have not been consistent, which poses a limitation to their applicability in practice. Some studies do confirm differences associated with age (see, e.g., Dean, 2008; Chang & Samuel, 2004; Simon & Usunier, 2007). An increase in age was found to have a negative effect on consumers’ preferences for SSTs to human contact, customers’ confidence to use SSTs, and their belief in the benefit from using technology (Dean, 2008; Lee et al., 2010). Gender may have a connection to the personality traits of technology innovativeness and technology anxiety, with men exhibiting a greater level of technology innovativeness and less technology anxiety (Lee et al., 2010). Similar results have been reported by Rosenbaum and Wang (2015) who found that TR is higher among young males with higher education levels. Income may affect technology anxiety, with higher income earners being less anxious about technology (Lee et al., 2010).

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    67 Furthermore, women, older people, and consumers with lower incomes displayed greater levels of TA (Lee et al., 2010). Findings from other studies into consumer demographics and SST usage did not report significant relationships; for example, Moital, Vaughan, and Edwards’s (2009) study shows that demographic variables like age, education, and gender had no influence on online purchasing behavior. Similar results are reported by Weijters, Rangarajan, Falk, & Schillewaert (2007) and Dabholkar et al. (2003), whose research in a retail context confirms that demographics have no significant effect on beliefs and attitudes toward SSTs. Even if customer demographics display some limited influence on the adoption of SSTs, their influence may be weakened when consumers gain more SST usage experience (Dabholkar et al., 2003; Moital et al., 2009; Nilsson, 2007). The fifth SST adoption factor is defined as the user perceptions of trust, which may also affect their SST adoption behaviors. Trust may be measured in terms of the trustworthiness (such as integrity, benevolence, ability, and p ­ redictability) of the online vendor, and the user’s propensity to trust others (e.g., Gefen et  al., 2003; McKnight et al., 2002). In internet banking, the trustworthiness of the ­provider is mediated by the user perceptions of security and privacy of the ­website (Yousafzai, Pallister, & Foxhall, 2009). The trust construct is often researched together with risk and the relationship between them is confirmed to be reciprocal, meaning that increasing trust reduces risk and vice versa (Chang & Chen, 2008). Therefore, risk is the sixth SST adoption factor in this review. Dimensions of risk affecting user adoption of SSTs include performance risk, security risk, financial risk, physical risk, and psychological risk (Kim, Qu, & Kim, 2009). Kim et al. (2009) concluded that security risk was of primary importance in purchasing airtickets online. Furthermore, risk perceptions may fluctuate at the various stages of purchasing airline tickets from problem recognition, information search, and evaluation of alternatives, to purchase and post-purchase ­(Cunningham, G ­ erlach, & Harper, 2004). The risk perceptions of customers changed from r­elatively high in stage one, lowered at stage two when consumers gathered information, peaked at stages three and four when the customer had to decide between alternatives and purchase, and lowered but remained high, in the last post-purchase stage (Cunningham et al., 2004). The seventh SST adoption factor that may be considered is situational factors, such as waiting time, crowdedness, time of day, and employee presence (see, e.g., Collier, Moore, Horkey, & Moore, 2015; Dabholkar & Bagozzi, 2002; Yang et al., 2014; Wang et al., 2012). For example, if customers perceive that waiting at a personal service desk will take longer, they will be more inclined to develop a positive attitude regarding their intention to use SSTs (Dabholkar & Bagozzi, 2002; Wang et al., 2012). This decision may be influenced by the physical length of the queues at a personal check-out in comparison to the kiosks or the accompanying customers (Wang et al., 2012). Furthermore, complex service tasks, requiring more decision-making input, such as booking a honeymoon, may discourage users from using SSTs (Kelly, Lawlor, & Mulvey, 2013). Yang et al. (2014) also found that if customers have already used the service in another service channel

68    Petranka Kelly et al. of the company, they are more inclined to trust the SST channel, then if they have no relationship with a company. The crowdedness of a service site (i.e., presence of other customers) may discourage novice users from trying SSTs, but its influence on SST usage diminishes with increased familiarity (Kinard, Capella, & Kinard, 2009). Furthermore, employee presence, location convenience, tolerance to wait, and small order size exert significant positive influences on customer attitude toward using an SST (Collier et al., 2015). It appears that customer attitudes toward SSTs are not ­stable and are affected by the specific service context and the situation when the service is delivered (Collier et al., 2015). Although SST adoption research provides valuable insights into the customer SST usage behavior, it has limitations in providing consistent and in-depth understanding of the user experience (Hilton et al., 2013). This gap in SST user research has invited qualitative exploration of the processes of creating the SST experience, which will be reviewed in the following section.

Value Co-creation and Customer Roles in SSTs The vast majority of early SST adoption research focuses on the study of adoption factors, so that service managers may manipulate these factors and encourage customer adoption of SSTs (see, e.g., Meuter et al., 2005). Other studies have addressed customer resistance to SST usage (see, e.g., Patsiotis, Hughes, & ­Webber, 2013) and how to reduce the negative effects from “forcing” customers to use SSTs (see, e.g., Liu, 2012; Reinders et al. 2015). This concept of f­orcing ­consumers to use SSTs appears to contradict the claims that SSTs empower ­consumers by providing them with more customization options, convenience, and control over the service (Meuter et al., 2005). This raises questions as to whether customers are comfortable with their active SST production role, despite the increasing implementation of SSTs in tourism (Lawlor, 2010). SST adoption factor research is limited to establishing what makes users accept or avoid SSTs, and does not reflect that a user and a service provider c­ o-create a service experience via an SST (Hilton et al., 2013). Hence, recent research is starting to draw on customer-centric theories such as the Service-Dominant (S-D) Logic of marketing in order to understand co-creation in SSTs (Akesson, Edvardsson, & Tronvoll, 2014; Heidenreich & Handrich, 2015; Hilton & Hughes, 2013). The essence of the co-creation concept is in representing service consumption as a process of interactions between consumers, service providers, and other stakeholders who all contribute to the final value realized by the consumer. This perspective reflects more accurately the role of the SST user, rather than the view that the service provider creates a service product and the consumer passively consumes it (Kelly et al., 2017). Through the lens of co-creation, the importance of studying the customer ­experience and the customer perspective on their role as co-creator of value is further emphasized (see, e.g., McColl-Kennedy, Vargo, Dagger, Sweeney, & ­ ­Kasteren, 2012; Payne, Storbacka, & Frow, 2008). Kelly et al. (2017) explore SST user experiences from a customer roles perspective in order to provide

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    69 an understanding of how users co-create value in SSTs. The understanding of ­co-creation in SSTs may also be presented in terms of drivers of co-creation, including informational, technological, relational, and organizational drivers (Akesson et al., 2014). These drivers are displayed in the context of each SST usage role, depending on the customer perceptions of their activities and the SST service process. The analysis of the customer perspective on their role in SST usage reveals the complexity of user interpretations of SST encounters (Kelly et al., 2017). These authors identified six roles that customers undertake in SST encounters, namely convenience seeker, motivated worker, judge, enforced worker, unskilled worker, and assistance provider. Each of the SST user roles and the key perceptions and experiences which define them are summarized in Table 2 and shall be outlined in this section.

Convenience Seeker Role The first customer SST role is that of a convenience seeker whereby the customer creates value by seeking out SSTs to achieve the most effective service process from their perspective. The user experiences an SST process as being clear, intuitive, reliable, cost and time effective, and evaluates it as the most efficient channel for obtaining a service. One interesting aspect of the convenience seeker role is the extent to which the customer acts as a technological sweeper in combining technological channels to achieve the most effective service. For example, a hotel guest may book their hotel via a third-party website, followed by online checkin via the hotel mobile application and finally obtain their room key via a selfservice kiosk at the hotel lobby. Provided there is sufficient integration between these SSTs, the user will experience empowerment to smoothly and independently navigate the service.

Motivated Worker Role The second customer SST role, that of a motivated worker, reflects a perception that the SST user does the work of a company employee in return for service benefits, such as cheaper prices or access to products not available in other service channels. Although the user is motivated to co-create, they do not perceive the same feeling of empowerment as in the previous role; hence, the value for the consumer while undertaking this role is not as high.

Judge Role The third customer role, namely a judge, represents the SST user as being ­competent and able to provide expert opinion and offers innovative solutions to SST process issues. This role may be undertaken in a positive or negative guise depending on the opportunities for SST users to contribute their opinions and feedback. SST users may feel recognized and competent, or they may end up feeling ignored and critical of the service company.

70    Petranka Kelly et al. Table 2.  SST User Roles and Perceptions. SST User Role Convenience seeker

Key Determining Perceptions The SST provider is competent in designing the process to suit users

Experience Value Very high

Easy, intuitive, efficient, reliable SST process User is clear and at ease interacting with the SST Motivated worker

SST process requires some effort, but it is beneficial


User is clear on the process, but feels like they have ‘work’ to do Other channels to obtain the service are more expensive or unavailable Judge

The user feels competent to evaluate SSTs

High or low

The user displays enthusiasm to inform or co-operate in SST development/ improvement Enforced worker

The SST provider is demanding users to interact with SSTs


The user likes to have a choice of service channels The user is not comfortable using the SST, or does not feel it is of benefit to them Unskilled worker

The user feels intimidated by the SST process


The user is apprehensive interacting with the SST The user may resort to assistance from employees, other customers or their social circle Assistance provider

The user interacts with the SST on behalf of someone else or assists them in the interaction Users experiencing difficulties are present at the service site or are in their social circles The user feels obligation or is delighted to help others

Source: Adapted from Kelly et al. (2017).

High or low

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    71 Enforced Worker Role The fourth customer role is that of an enforced worker which reflects a perspective that the service provider is dominating the SST process and “forcing” consumers into undertaking unwanted responsibilities in SSTs. Although consumers may unwillingly use an SST due to a lack of other options, they do not feel empowered, but rather unappreciated by service providers. In fact, users may perceive SST providers as trying to take advantage of customers, in terms of the responsibilities being imposed upon them.

Unskilled Worker Role The fifth customer role is that of an unskilled worker when SST users are struggling to understand and operate an SST. Although the user may manage to obtain a service via an SST, this is at the cost of increased anxiety, having to look for help or spending excessive time in operating the SST. In this situation, users are motivated to use an SST, but the SST does not facilitate a pleasant experience in their specific situation.

Assistance Provider Role The sixth customer role is that of an assistance provider whereby they willingly or reluctantly assist other people in SST usage. Assistance providers may be enthusiastic to help fellow SST users when they perceive their contribution as genuinely helping people in need. However, if assisting other users is viewed as compensating for the SST provider’s inadequate user support provision, then customers may feel resentful and reluctant. Having reviewed the main concepts in SST research from a customer perspective, the following section considers their implications and presents an agenda for further research.

Conclusion and Agenda for Further Research Service automation has been rapidly infiltrating every aspect of the tourism sector, even in areas traditionally associated with personal service delivery, such as concierge and restaurant services. One of the significant consequences from this increased automation is the growing role of the customer in service delivery, which has been facilitated by SSTs (Kelly et al., 2017). SSTs have enabled the customer to deliver services independently of company employees in a more timely, economical and efficient manner (Meuter et al., 2000). The fast development of technology has provided for the design of a wide diversity of SSTs, which has generated a research necessity to classify them across a number of dimensions. Some of these dimensions include the interface type (Meuter et al., 2000), purpose of the SST transaction (Meuter et al., 2000), where the SST is situated and whether the user interacts directly with the interface ­(Dabholkar, 1994). Furthermore, SSTs may be classified depending on whether

72    Petranka Kelly et al. the SST is provided by the service company or owned by the customer (Schumann et al., 2012). SST classifications will require regular re-visiting and re-evaluation with the continuous development of service automation. However, despite the pronounced benefits for customers from SST usage, there are also drawbacks which discourage customer SST usage and hence reduce SST adoption (Meuter et al., 2000). Successful SST implementation is reliant on customer adoption and usage of these technologies (Lee & Allaway, 2002). Therefore, SST research has been focused on understanding the customer decision-making processes leading to trial and acceptance of these technologies (see, e.g., Bitner et al., 2002; Wang, 2012). Although SST adoption research has provided valuable insights into customer decision-making, it provides limited understanding of how customers co-create the service (Hilton & Hughes, 2013). From a co-creation research perspective, Kelly et al. (2017) find that SST users may undertake six different roles, thus suggesting the multi-faceted nature of SST co-creation. Indeed, SST users often describe their role as that of a “worker” who acts similarly to an employee of the company in the service delivery process (see, e.g., Kelly et al., 2017). Hence, SSTs do not empower consumers by default, but rather the co-creation interactions associated with SST usage trigger customer evaluation processes forming the experience (Akesson et al., 2014). From this stance, future research may explore sustainable customer empowerment in SSTs as embedded in customer SST roles, in addition to the SST adoption factors facilitating this empowerment. Co-creation via SSTs may involve a number of touch points where the user and the company may interact to co-create value, including before, during and after a service interaction (Akesson et al., 2014). For example, users interact with most online reservation websites by firstly entering search terms to obtain the desired service; then, they enter specific information to complete the purchase and finally the website may offer an opportunity for the user to leave a review or complete a survey. Indeed, many service companies nowadays provide the opportunity for users to create a profile which allows them to manage and track their bookings without having to call the company. Further research may identify creative ways to implement SSTs where they will empower users, rather than force them into SST usage. For example, courier service tracking applications and taxi mobile applications provide transparency to customers by providing them detailed ­information on the status of their service; thus, empowering them to make changes if necessary. The effective empowerment of SST users also depends on careful understanding of user perceptions of empowerment in SSTs. Further research may be merited to explore whether and how an SST may be re-framed so that it is perceived as empowering rather than enforcing additional work on customers. Ensuring that SST users not only adopt SSTs, but are effectively empowered by them, will promote a more sustainable SST implementation where users take “ownership” of their SST usage as opposed to using them on service provider terms. New technologies, such as virtual reality, artificial intelligence, IoT, and robotics are only a few of the innovations expected to shape the future outlook of the tourism sector (Sabre, 2017). Invariably, these innovations herald new advantages,

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    73 and may result in concerns regarding their implementation. The personal interaction between the customer and the service provider, which is at the core of tourism and hospitality service delivery is already experiencing significant transformation with self-service kiosks, mobile phone applications, and robotics fundamentally changing the traditional tourism service encounter. Future tourism research will have many questions to answer as to where and how technology automation is appropriate, and whether it augments the tourism experience rather than hindering it. This will require a fine balance between the delivery of a personalized and smarter customer experience and technology applications that are unobtrusive and which pose no ethical, moral or privacy concerns.

References Akesson, M., Edvardsson, B., & Tronvoll, B. (2014). Customer experience from a self-­ service system perspective. Journal of Service Management, 25(5), 677–689. Al Meer, B., & Manuell, R. (2017). Hamad International Airport: Uncovering the world’s leading smart airport. International Airport Review [online]. Retrieved from https:// Accessed on August 20, 2017. Aluri, A. (2016). The future of IoT in hospitality. Tech Trends Connectivity Special Report [online]. Retrieved from Accessed on March 10, 2018. Anselmsson, J. (2001). Customer-perceived service quality and technology-based self-service. Doctoral dissertation, Lund University, Lund, Sweden. Aucoin, J. (2017). How Inamo Restaurant has embraced interactive dining. The Rail Media [online]. Retrieved from Accessed on March 10, 2018. Bateson, J. (1985). Self-service consumer: An exploratory study. Journal of Retailing, 61(3), 49–76. BBC News. (2017). Lost at the airport? This robot can help [online]. Retrieved from Accessed on August 19, 2017. Beatson, A., Lee, N., & Coote, L. (2007). Self-service technology and the service encounter. The Service Industries Journal, 27(1), 75–89. Bitner, M., Brown, S., & Meuter, M. (2000). Technology infusion in service encounters. Journal of the Academy of Marketing Science, 28(1), 138–149. Bitner, M., Ostrom, A., & Meuter, M. (2002). Implementing successful self-service technologies. Academy of Management Executive, 16(4), 96–108. Boes, K., Buhalis, D., & Inversisni, A. (2016). Smart tourism destinations: Ecosystems for tourism destination competitiveness. International Journal of Tourism Cities, 2(2), 108–124. Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the internet – The state of eTourism research. Tourism Management, 29(4), 609–623. Cases, A. S. (2002) Perceived Risk and Risk-reduction Strategies in Internet Shopping, The International Review of Retail, Distribution and Consumer Research, 12(4), 375–394. Castillo-Manzano, J., & Lopez-Valpuesta, L. (2013). Check-in services and ­passenger behaviour: Self service technologies in airport systems. Computers in Human Behavior, 29, 2431–2437.

74    Petranka Kelly et al. Castro, D., Atkinson, R., & Ezell, S. (2010). Embracing the self-service economy. Washington, DC: The Information Technology and Innovation Foundation. Chang, H., & Chen, S. (2008). The impact of online store environment cues on purchase intention: Trust and perceived risk as a mediator. Online Information Review, 32(6), 818–841. Chang, J., & Samuel, N. (2004). Internet shopper demographics and buying behaviour in Australia. The Journal of American Academy of Business (September), 171–176. Chen, S., Chen, H., & Chen, M. (2009). Determinants of satisfaction and continuance intention towards self-service technologies. Industrial Management and Data Systems, 109(9), 1248–1263. Chiu, Y., Fang, S., & Tseng, C. (2010). Early versus potential adopters: Exploring the antecedents of use intention in the context of retail service innovations. International Journal of Retail and Distribution Management, 38(6), 443–459. Collier, J., & Kimes, S. (2013). Only if it is convenient: Understanding how convenience influences self-service technology evaluation. Journal of Service Research, 16(1), 39–51. Collier, J., Moore, R., Horkey, A., & Moore, M. (2015). Why the little things matter: Exploring situational influences on customers’ self-service technology decisions. Journal of Business Research, 68(3), 703–710. Connolly, R., & Bannister, F. (2008). Factors influencing Irish consumers’ trust in internet shopping. Management Research News, 31(5), 339–358. Cunningham, L., Gerlach, J., & Harper, M. (2004). Assessing perceived risk of consumers in internet airline reservations services. Journal of Air Transportation, 9(1), 21–35. Cunningham, L., Young, C., & Gerlach, J. (2009). A comparison of consumer views of traditional services and self-service technologies. Journal of Services Marketing, 23(1), 11–23. Cunningham, L., Young, C., & Hu, Z. (2013). Comparing hybrid services in the United States and China. International Journal of Information Systems in the Service Sector, 5(1), 17–32. Curran, J., & Meuter, M. (2005). Self-service technology adoption: Comparing three ­technologies. Journal of Services Marketing, 19(2), 103–113. Dabholkar, P. (1994). Technology-based service delivery: A classification scheme for developing marketing strategies. In T. Swartz, D. Bowen, & S. Brown (Eds.), Advances in services marketing and management (Vol. 3, pp. 241–271). Greenwich, CT: JAI. Dabholkar, P., & Bagozzi, R. (2002). An attitudinal model of technology-based self-­ service moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184–201. Dabholkar, P., Bobbitt, L., & Lee, E. (2003). Understanding consumer motivation and behavior related to self-scanning in retailing. International Journal of Service Industry Management, 14(1), 59–95. Dabholkar, P., & Spaid, B. (2012). Service failure and recovery in using technology-based self-service: Effects on user attributions and satisfaction. The Service Industries Journal, 32(9), 1415–1432. Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. Dean, D. (2008). Shopper age and the use of self-service technologies. Managing Service Quality, 18(3), 225–238. Eastlick, M., Ratto, C., Lotz, S., & Mishra, A. (2012). Exploring antecedents of attitude toward co-producing a retail checkout service utilizing a self-service technology. The International Review of Retail, Distribution and Consumer Research, 22(4), 337–364. Elliott, K., Hall, M., & Meng, J. (2013). Customers’ intention to use self-service technology: The role of technology readiness and perceptions towards self-service technology. Academy of Marketing Studies Journal, 17(1), 129–143.

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    75 Forbes, L. (2008). When something goes wrong and no one is around: Non-internet ­self-service technology failure and recovery. Journal of Services Marketing, 22(4), 316–327. Gefen, D., Karahanna, E., & Straub, D. (2003) Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51–90. Gerrard, P., Cunningham, B., & Devlin, J. (2006). Why consumers are not using internet banking: A qualitative study. Journal of Services Marketing, 20(3), 160–8. Girman, M., Keusch, P., & Kmec, P. (2009). Faults, failures and availability in self-service technology. Management Services (Winter), 44–47. Gittens, G. (2016). Dublin airport becomes first in the world to introduce touchless self– service bag drop. The Independent, July 6 [online]. Retrieved from http://www. Accessed on July 23, 2016. Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: Foundations and developments. Electronic Markets, 25, 179–188. Halstead, D., & Richards, K. (2014). From high tech to high touch: Enhancing customer service experiences via improved self-service technologies. Innovative Marketing, 10(4), 16–27. Heidenreich, S., & Handrich, M. (2015). Adoption of technology-based services: The role of customers’ willingness to co-create. Journal of Service Management, 26(1), 44–71. Hilton, T., & Hughes, T. (2013). Co-production and Self-service: The Application of Service-Dominant Logic, Journal of Marketing Management, 29(7–8), 861–881. Hilton, T., Hughes, T., Little, E., & Marandi, E. (2013). Adopting self-service technology to do more with less. Journal of Services Marketing, 27(1), 3–12. Ho, C., Lin, M., & Chen, H. (2012). Web users’ behavioural patterns of tourism information search: From online to offline. Tourism Management, 33(6), 1468–1482. Hsiao, C., & Tang, K. (2015). Investigating factors affecting the acceptance of self-service technology in libraries: The moderating effect of gender. Library Hi Tech, 33(1), 114–133. Ivanov, S., & Webster, C. (2018). Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies – A cost-benefit analysis. In V. Marinov, M. Vodenska, M. Assenova, & E. Dogramadjieva (Eds.), Traditions and innovations in contemporary tourism (pp. 190–203). Cambridge: Cambridge Scholars Publishing. Ivanov, S., Webster, C., & Berezina, K. (2017). Adoption of robots and service automation by tourism and hospitality companies. Revista Turismo & Desenvolvimento, 27/28, 1501–1517. Jacob, F., & Rettinger, B. (2011). The role of customer co-production in value creation. In E. Gummesson, C. Mele, & F. Polese (Eds.), Service dominant logic, network & systems theory and service science: Integrating three perspectives for a new service agenda. Napoli, Italy: Giannini. Kelly, P., Lawlor, J., & Mulvey, M. (2013). Customer decision-making processes and motives for self-service technology usage in Multichannel hospitality environments. International Journal of Electronic Customer Relationship Management, 7(2), 98–116. Kelly, P., Lawlor, J., & Mulvey, M. (2017). Customer roles in self-service technology encounters in a tourism context. Journal of Travel and Tourism Marketing, 34(2), 222–238. Kim, L., Qu, H., & Kim, D. (2009). A study of perceived risk and risk reduction of purchasing air-tickets online. Journal of Travel and Tourism Marketing, 26, 302–324. Kinard, B., Capella, M., & Kinard, J. (2009). The impact of social presence on technology based self-service use: The role of familiarity. Services Marketing Quarterly, 30, 303–314.

76    Petranka Kelly et al. Ko, C. (2017). Exploring how hotel guests choose self-service technologies over service staff. International Journal of Organizational Innovation, 9(3), 16–27. Lawlor, J. (2010). The role of the consumer as a quasi-employee in service organizations: Research agenda. In G. Gorham & Z. Mottiar (Eds.), Contemporary issues in Irish and global tourism and hospitality (pp. 179–187). Dublin, Ireland: Dublin Institute of Technology. Lee, J., & Allaway, A. (2002). Effects of personal control on adoption of self-service technology innovations. Journal of Services Marketing, 16(6), 553–573. Lee, H., Cho, H., Xu, W., & Fairhurst, A. (2010). The influence of consumer traits and demographics on intention to use retail self-service checkouts. Marketing Intelligence and Planning, 28(1), 46–58. Leung, L., & Matanda, M. (2013). The impact of basic human needs on the use of retailing self-service technologies: A study of self-determination theory. Journal of Retailing and Consumer Services, 20, 549–559. Liljander, V., Gillberg, F., Gummerus, J., & Riel, A. (2006). Technology readiness and the evaluation and adoption of self-service technologies. Journal of Retailing and Consumer Services, 13(3), 177–191. Lin, J., & Chang, H. (2011). The role of technology readiness in self-service technology acceptance. Managing Service Quality, 21(4), 424–444. Liu, S. (2012). The impact of forced use on customer adoption of self-service technologies. Computers in Human Behavior, 28, 1194–1201. Lu, H., & Su, Y. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research, 19(4), 442–458. Makarem, S., Mudambi, S., & Podoshen, J. (2009). Satisfaction in technology-enabled ­service encounters. Journal of Services Marketing, 23(3), 134–144. Maze, J. (2016). How Domino’s became a tech company. Nation’s Restaurant News [online]. Retrieved from Accessed on July 23, 2016. McColl-Kennedy, J., Vargo, S., Dagger, T., Sweeney, J., & Kasteren, Y. (2012). Health care  customer value cocreation practice styles. Journal of Service Research, 15(4), 370–389. McKnight, D., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334–359. Meuter, M., Bitner, M., Ostrom, A., & Brown, S. (2005). Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. Journal of Marketing, 69(2), 61–83. Meuter, M., Ostrom, A., Bitner, M., & Roundtree, R. (2003). The influence of technology anxiety on consumer use and experiences with self-service technologies. Journal of Business Research, 56(11), 899–906. Meuter, M., Ostrom, A., Roundtree, R., & Bitner, M. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64(3), 50–64. Moital, M., Vaughan, R., & Edwards, J. (2009). Using involvement for segmenting the adoption of e-commerce in travel. The Service Industries Journal, 29(5–6), 723–739. Neuhofer, B., Buhalis, D., & Ladkin, A. (2013). High tech for high touch experiences: A case study from the hospitality industry. In L. Cantoni & Z. Xiang (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2013, Innsbruck, Austria, 23–25 January. Berlin, Germany: Springer. Nilsson, D. (2007). A cross-cultural comparison of self-service technology use. European Journal of Marketing, 41(3/4), 367–381. Oh, H., Jeong, M., & Baloglu, S. (2013). Tourists’ adoption of self-service technologies at resort hotels. Journal of Business Research, 66, 692–699.

Self-service Technologies in the Travel, Tourism, and Hospitality Sectors    77 Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. Patsiotis, A., Hughes, T., & Webber, D. (2013). An examination of consumers’ resistance to computer-based technologies. Journal of Services Marketing, 27(4), 294–311. Payne, A., Storbacka, K., & Frow, P. (2008). Managing the co-creation of value. Journal of the Academy of Marketing Science, 36, 38–96. Rajesh, M. (2015). Inside Japan’s first robot-staffed hotel. The Guardian, August 14. Retrieved from Accessed on July 23, 2016. Reinders, M., Dabholkar, P., & Frambach, R. (2008). Consequences of forcing consumers to use technology-based self-service. Journal of Service Research, 11(2), 107–123. Reinders, M., Frambach, R., & Kleijnen, M. (2015). Mandatory use of technology-based selfservice: Does expertise help or hurt? European Journal of Marketing, 49(1/2), 190–211. Robertson, N., McDonald, H., Leckie, C., & McQuilken, L. (2016). Examining customer evaluations across different self-service technologies. Journal of Services Marketing, 30(1), 88–102. Rogers, E. (1995). Diffusion of innovations (4th ed.). New York, NY: Free Press. Rosenbaum, M., & Wang, I. (2015). If you install it, will they use it? Understanding why hospitality customers take ‘technological pauses’ from self-service technology. Journal of Business Research, 68(9), 1819–2044. Sabre (2017). Sabre Labs Radar 2017 Report [online]. Retrieved from https://www.sabre. com/labs/radarreport/. Accessed on February 18, 2018. Schumann, J., Wünderlich, N., & Wangenheim, F. (2012). Technology mediation in service delivery: A new typology and an agenda for managers and academics. Technovation, 32, 133–143. Shu, L. (2015). Van Gogh vs. Candy Crash: How museums are fighting tech with tech to win your eyes. Digital Trends, May 1. Retrieved from cool-tech/how-museums-are-using-technology. Accessed on July 23, 2016. Simon, F., & Usunier, J. (2007). Cognitive, demographic, and situational determinants of service customer preference for personnel-in-contact over self-service technology. International Journal of Research in Marketing, 24(2), 163–173. SITA News Release. (2017). Miami International Airport first worldwide to provide fast entry process into USA via its mobile app [online]. Retrieved from pressroom/news-releases/miami-international-airport-first-worldwide-to-provide-fastentry-process-into-usa-via-its-mobile-app. Accessed on February 16, 2018. Tieryas, P. (2015). How navigating the Louvre with a Nintendo 3DS made me rethink the future of gaming, art, and virtual reality. TOR [online]. Retrieved from https://www. Accessed on October 24, 2017. Van Birgelen, M., de Jong, A., & de Ruyter, K. (2006). Multi-channel service retailing: The effects of channel performance satisfaction on behavioral intentions. Journal of Retailing, 82(4), 367–377. Walker, R., Craig-Lees, M., Hecker, R., & Francis, H. (2002). Technology-enabled service delivery: An investigation of reasons affecting customer adoption and rejection. International Journal of Service Industry Management, 13(1), 91–106. Wang, C., Harris, J., & Patterson, P. (2012). Customer choice of self-service technology: The role of situational influences and past experience. Journal of Service Management, 23(1), 54–78. Weijters, B., Rangarajan, D., Falk, T., & Schillewaert, N. (2007). Determinants and outcomes of customers’ use of self-service technology in a retail setting. Journal of Service Research, 10(1), 3–21.

78    Petranka Kelly et al. Woods, R. (2017). Microsoft Build 2017: Hands on with Kodisoft’s interactive table running Windows 10 IoT Core [online]. Retrieved from microsoft-build-2017-hands-on-with-kodisofts-interactive-table-running-windows10-iot-core. Accessed on August 19, 2017. Yang, H., Lee, J., Park, C., & Lee, K. (2014). The adoption of mobile self-service technologies: Effects of availability in alternative media and trust on the relative importance of perceived usefulness and ease of use. International Journal of Smart Home, 8(4), 165–178. Yousafzai, S., Pallister, J., & Foxhall, G. (2009). Multi-dimensional role of trust in internet banking adoption. The Service Industries Journal, 29(5–6), 591–605. Zhu, Z., Nakata, C., Sivakumar, K., & Grewal, D. (2013). Fix it or leave it? Customer recovery from self-service technology failures. Journal of Retailing, 89(1), 15–29.

Chapter 4

Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality: A Conceptual Framework Velina Kazandzhieva and Hristina Filipova Introduction Since the inception of the concept of the robot about 100 years ago, robots have been characters in the plots of science-fiction movies and novels. Apart from the world of art and entertainment, they have been widely used in industrial production (Miller & Miller, 2017). In this decade, robots enter the service industries (Wirtz et al., 2018). Thanks to artificial intelligence (AI), “socially intelligent robots” will help people in an interactive way. Humans will interact with robots all around, will share the same environment with them, and will communicate emotionally with them. Without any doubt, the use of robots and AI in p ­ eople’s everyday routine leads to significant changes with both positive and negative consequences for humans, although some authors hint that the introduction of robots in tourism and hospitality would have a “dehumanization effect” on humans (Papathanassis, 2017). The technological revolution in tourism and hospitality services, as in other industries, is an undisputed fact and an objective reality. Technological innovations are leading to a future economy in which robots and AI will be used at all stages of the service process in travel, tourism, and hospitality (TTH) – before, during and after a tourist’s trip (Tutek, Gebbie, Chan, & Durand, 2015). The  efficiency and feasibility of robots’ introduction is strongly dependent on the ­decision of customers to accept and use them, as well as on the practical and financial benefits and effects from their application in TTH industries. Engineers have designed social robots in travel and tourism in order to provide services in diverse environments: public areas in airports and hotels; guided tours in museums and sightseeing trips; on board of ships and others (Joosse, Waveren, Zaga, & Evers, 2017). The most widely used types of robots are those for personal and professional services. In the future, tourists will meet them even more often

Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality, 79–92 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78756-687-320191004

80    Velina Kazandzhieva and Hristina Filipova and will have to either accept or reject them (Barnett, Foose, Gruber, Keeling, & Nasr, 2015; Oborn, Barrett, & Darzi, 2011). Customers create attitudes toward robots’ competences and skills on the basis of their initial perceptions (Haring, Tawil, Watanabe, Takahashi, & Velonaki, 2015). Consequently, attitudes can be adjusted, or strengthened, based on users’ impressions, feelings, and experiences related to the first interactions with robots. Investments in robotics to improve TTH services create competitive advantages for TTH companies. Intelligent automation with the help of robots, AI, and the Internet of things leads to high-quality services and product at optimized costs which is beneficial for the customers (World Economic Forum, 2017). A  global survey, conducted by Travelzoo in 2016 in nine countries in Europe, Asia, and the Americas, examines the attitude toward robots and the extent to which customers believe that robots will improve their experiences (Singer, 2016). The results of the survey show that 80% of respondents expect to see more robots during their travels in the near future. Nearly two-thirds determine that they feel comfortable with a robot-based service. In general, customers have a positive attitude toward them. In some countries, the introduction and adoption of robots is faster: China, Brazil, and the USA. In others like Germany and France, this process is slower (Murison, 2016). The positive attitude among respondents is due mainly to the robots’ efficiency and their ability to store, process and use extensive amounts of information and data. The negative attitude among respondents is related to the fact that 75% of the respondents consider robots to be impersonal and unable to understand informal language, local dialects, and different variations in speech such as sarcasm, irony, etc. (Hodges & Higgins, 2016). For 53% of the respondents, the personal service offered by robots offers faster and more reliable services (Boomer, 2016). While EU citizens generally have a positive attitude toward robots, only 3% believe that they should be used in the entertainment sector (European Commission, 2012). By 2020, the number of industrial and service robots is expected to grow to three million units globally. The bulk of them are going to be located in Asia, especially in South Korea, Singapore, and Japan (International Federation of Robotics [IFR], 2018). Europe’s most robot-populated country is expected to be Germany. Customer attitudes and perceptions toward robots in everyday life are the subject of a number of studies (Katz & Halpern, 2014; Melson, Kahn, Beck, & Garrett, 2005). However, in the field of TTH, they have not attracted much attention – the available publications are of limited scope and content (e.g. Ivanov, Webster, & Garenko, 2018; Ivanov, Webster, & Seyyedi, 2018; Tussyadiah & Park, 2018; Tussyadiah, Zach, & Wang, 2017), probably because with a few exceptions, robots are not commonly seen in tourism and hospitality. When available, robots are applied to parts of the service process and are not continuously applied to the overall service process. However, since the customer’s attitude toward the use of robots for services in the industry is so critical to the success of the robotization of the industry, studying customers’ attitudes toward robot-delivered services is important in order to define the tasks that are most appropriate for robotization (Ivanov & Webster, 2019a, 2019b). This chapter aims to define customer attitudes toward robots in TTH and analyze attitudes’ most significant characteristics. Specifically, this chapter defines

Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality    81 “attitude” as a psychological concept, characterizes customer attitudes toward robots in TTH, and elaborates on the future research directions on the topic. The attitudes toward robots can be divided in three main groups: the attitudes of customers toward robots in TTH services; the attitudes of employees toward robotization of labor in TTH companies; and the attitudes of managers toward implementation of robots in TTH. This chapter provides a theoretical analysis of the attitudes in the first group, because they influence most of the decisionmaking process about robotics in the TTH context.

Definition and Main Characteristics of Attitudes Attitudes express an individual’s inside feelings and perceptions that show ­favorable or unfavorable disposition to a certain object, product, service, a­ ctivity, and others (Ajzen & Fishbein, 2000). Attitudes are not inborn, on the ­contrary – they are acquired in the process of developing one’s personality and the way one communicates with people around (Eagly & Chaiken, 1998). Attitudes have three dimensions: cognitive, affective, and conative. In most cases, customers’ attitudes are not an exact behavioral indicator, because sometimes identified research indicators concentrate upon one of the three dimensions only (Fabrigar, ­MacDonald, & Wegener, 2005). Attitudes predetermine an individual’s thoughts and actions. They result from a complex psychological process that has been the studied by the theory of consumer behavior (Blackwell, Miniard, & Engel, 2001). Attitudes influence one’s psychic and emotional condition; yet, they are not equivalent to a person’s behavior. They have a hypothetical characteristic because they cannot be observed directly (Perloff, 2003). They are implied through an individual’s reactions and actions. In academic literature, an attitude is defined in various ways, most often as: ⦁⦁ an association – an interrelationship between different notions (at least two)

in which the presence of a single one, related to a certain object, brings in another one, relating to its assessment (Eagly & Chaiken, 1998). This definition focuses on the perceptions and expectations on a mental level as a result of the association. ⦁⦁ an overall psychological assessment of a certain set or a separate object (a person, a place, or an issue) defined through a degree of favorability or lack of it (Fabrigar et al., 2005). In this case, attitudes refer mainly to a consumer’s individual judgment that has a summarizing character. ⦁⦁ an acquired predisposition to favorable or unfavorable reactions to a certain object, process, or activity (Ajzen & Fishbein, 2000). This definition emphasizes individual’s behavioral characteristics and how attitudes provoke and predetermine one’s behavior. These definitions of attitudes show that customer attitudes create frameworks, which provoke people to be attracted to and consume certain products or services and dislike and reject others. The same goes for the ways in which products and services are prepared and delivered. Attitudes are assumed and suggested by

82    Velina Kazandzhieva and Hristina Filipova people’s reactions and actions. In this sense, we assume that the assessments and conclusions regarding customer attitudes toward robots in TTH are a result of indirect research and analysis.

Customer Attitudes Toward Technology Modern robots are a set of machines and technologies for automating various movements and technological operations. In order to determine clients’ attitudes toward robots in TTH, one initially has to define their general attitudes toward technologies. At its most basic level, technologies are a collective notion that unites the means for reaching a certain goal (Arthur, 2009). Technologies are the complex set of tools for finding solutions to problems, including ones in tourism and hospitality. Modern innovations are the consequence of combining complementary technologies in a new and modern way, as well as developing new innovations. The clients’ attitudes and decisions to adopt and use innovations in TTH enable the new technologies to be introduced quickly and effectively. These decisions, based on customer attitudes and expectations, are analyzed in various theoretical frameworks for technology adoption such as Technology Acceptance Model (TAM), Innovation Diffusion Theory, and the Unified Theory of Acceptance and Use of Technology (UTAUT). The most widely used model is the TAM (Davis, 1989; Venkatesh, 2000; Wang & Qualls, 2007), based on the Theory of Reasoned Action (Kim & Noh, 2004; Ryu & Jang, 2006). It posits that each individual has specific attitudes and intentions that guide one’s actions, and are subject to the influence of subjective norms and psychological factors that determine an individual’s behavior, including one’s role as a consumer (Varol & Tarcan, 2009). The TAM focuses on two main aspects: how easily a person can use a technology and how useful it is to use a certain technology. This model has been applied in various fields in order to study aspects of the attitude toward technology; they include, among others, banking, healthcare, e-services (Featherman & Wells, 2010; Giovanis, B ­ inioris, & Polychronopoulos, 2012; Wu, Li, & Fu, 2011). According to this model, p ­ erceived usefulness and perceived ease-of-use of a new technology are of key significance in determining consumers’ attitudes to it. TAM 2 is the updated and more developed version and includes the perceived technological characteristics that have a direct influence on intentions and attitudes for using technology by consumers (Venkatesh & Davis, 2000). An especially strong impact on perceived ease-ofuse is exerted by personal innovativeness. Its foundation is the positive attitudes of consumers in using technologies for implementing innovative ideas in everyday activities, services, work, etc. The technological perfection and expansion of e-business and e-commerce lead to another enrichment and enlargement of TAM. The added components in TAM 3 are: the effects of trust and perceived risk from using new technologies by consumers (Venkatesh & Bala, 2008). Upgrading and tying various models for adopting and using technologies led to establishing the UTAUT (Venkatesh, Morris, Davis, & Davis, 2003). ­Generally, it analyzes critical factors and unforeseen circumstances for forecasting intentions and expectations toward technologies in organizational contexts.

Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality    83 Expanding UTAUT and involving variables of consumer use context led to UTAUT 2 (Moura, Gosling, Christino, & Macedo, 2017). In UTAUT 2, the leading determinants are: hedonic motivation, price value, experience, and habit for using technologies by consumers (Venkatesh, Thong, & Xu, 2012). Another important element observed by researchers regarding the topic is the consumer’s readiness. The model used for that is Technology Readiness Index (TRI) (Parasuraman, 2000; Pradhan, Oh, & Lee, 2018; Wang & Sparks, 2014). The model is a multidimensional psychographic variation of TAM and combines the following four categories (Edison & Geissler, 2003): ⦁⦁ ⦁⦁ ⦁⦁ ⦁⦁

optimism – positive attitude toward technology; innovativeness – to be first in adapting new technology; discomfort – feeling of loss of control over technology; insecurity – having no trust in technology.

These elements of the attitudes describe the predisposition of consumers toward the use of technology (Kim, Christodoulidou, & Brewer, 2012). Some consumers are more eager to implement technological innovations in their lives than others are. Apart from the practical side, technology’s hedonistic side can be analyzed in relation to attitudes (Venkatesh & Brown, 2001). Consumers can feel joy, pleasure, and other happy emotions while using new and innovative technology products. Updating and rationalizing TRI led to establishing TRI 2.0 (Parasuraman & Colby, 2014), which is considered a reliable and useful tool for segmenting consumers. While the aforementioned are generalized approaches to measure consumer attitudes toward new technologies, two psychological scales have been designed specifically to measure consumer attitudes to robots. The first one measures ­negative attitudes toward robots (Katz & Halpern, 2014; Nomura & Kanda, 2003), whereas the second one, the Robot Anxiety Scale, shows the anxiety and worries in communicating with a robot (Nomura, Kanda, Suzuki, & Kato, 2006). Studying negative emotions and attitudes in human-robot interactions (HRIs), as well as tracing consecutive behavior for avoiding communication with robots are important in robotics design (Nomura et al., 2008). All of the above-presented major models and theories for accepting and using technologies are a basis upon which researchers can develop appropriate measures to survey consumer attitudes toward robots in TTH.

Customer Attitudes Toward Robots in TTH: Definition and Characteristics Unlike in manufacturing, in TTH the processes of robotization and automation are still in their initial stages; that is why, one cannot make definitive conclusions about customer attitudes toward robots in a TTH context, since few consumers have faced many robotized services in TTH. The few surveys on how robots are accepted/perceived by consumers in hospitality rely on assumed preferences and attitudes rather than on real ones (e.g. Ivanov et al., 2018; Ivanov et al., 2018),

84    Velina Kazandzhieva and Hristina Filipova since very few respondents have been actually had contact with such automated services. It is not yet clear enough about how individuals respond to contacts with robots, despite the fact that psychological tests and measurements of ­physical parameters (e.g. pulse) in real and virtual environments have been conducted (Ng, Duffy, & Yucel, 2011). Such surveys, in real environments, but at a very small scale, are carried out in entirely “automated:” establishments. Emblematic in this respect is the pioneering Japanese hotel, Henn-na Hotel. By applying animatronics, the designers are trying to create a specific and pleasant atmosphere, predisposing to a curious and interesting experience through communication (conversation) with robots, while the latter work and attend to the customers (Pierce, 2015). Customers’ attitudes toward technology have been analyzed in many fields and tourism is no exception. The studies focus on variety of innovations related to various products and services in this sector. The examples include attitudes toward: robots (Ivanov et al., 2018; Ivanov et al., 2018), geotag (Chung, Tyan, & Han, 2017), mobile tourist guide (Trakulmaykee, Trakulmaykee, & Hnuchek, 2015), entertainment robotics (Graf & Barth, 2002), and museum tour-guide robot (Yamazaki, Yamazaki, Burdelski, Kuno, & Fukushima, 2010), among ­others. Technology has become a major factor for the development of tourism and, therefore, exploring the opinion of travelers toward it is important. It is constantly changing and adapting and it is difficult to be analyzed as a whole, with one of the newer elements being robots. A deeper knowledge of the attitudes toward them is very important. We can define tourists’ attitudes toward robots as learned and acquired g­ eneral assessments and opinions about the benefits and effects of robots’ application in travel, tourism and hospitality. These assessments influence the thoughts and reactions of tourists, their experiences and perceptions, and motivate their future actions in regards to purchasing and consuming TTH services. The latter are offered through service processes that are partially or completely automated. Robotization complies with the particularities of specific tourist activities. The positive assessments, service convenience, speed, and comfort presuppose favorable consumers’ attitudes toward robots in TTH. In this case, we have complete acceptance, approval of and maximum benefit from service robots’ advantages, and consumers would purposefully search for robot-delivered TTH services. The tourist and hospitality business, therefore, has to provide the suitable technol­ ogies, and has to ensure that the necessary specific environment for consuming such services operates seamlessly. While, on the other hand, negative, unpleasant, and frustrating experiences, as well as negative opinions and assessments will tend to condition unfavorable attitudes toward robots in TTH. Such conditions lead to lack of approval, avoiding or simply rejecting the use of robots in TTH, because consumers focus on service robots’ disadvantages, deficiencies, and imperfections. Customer attitudes toward robots are acquired through the introduction and use of smart machines in TTH during the search for, purchase of and consumption of TTH services. Confirming or rejecting certain prejudices toward robots is an immediate result of the acquired experience. The positive and negative attitudes toward robots are thought to be based on the provoked emotions, thoughts,

Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality    85 and perceptions acquired by the customers in HRIs. In this sense, attitudes toward robotization and automation of processes and activities in TTH, are acquired and learned above all with the active customer participation in the different stages of the service delivery process (Lukanova, 2017). Robots’ functions and the conditions of the social context where HRIs (direct and indirect) take place, determine the attitudes toward robotization of TTH services (Thrun, 2004; Tung & Au, 2018). It is possible that an individual’s attitudes vary within broad limits, as a result of the influence of different factors from the cultural, social, religious, and technological environment (Kimura, 2017; Kuo, Chen, & Tseng, 2017; Li, Rau, & Li, 2010; Rau, Li, & Li, 2009; Wang, Rau, Evers, ­Robinson, & Hinds, 2010). However, there is significant evidence that trust, perceived intelligence, and perceived safety influence customer attitudes and acceptance of robots in hospitality (Tussyadiah & Park, 2018). The appearance of robots can have an impact on tourists’ attitudes toward them (Murphy, Gretzel, & Pesonen, 2019). As a whole, research shows that a human-looking robots invoke positive perceptions and attitudes toward them (Bartnek, Kulic, Croft, & Zoghbi, 2009), while attributing human c­ haracteristics to inhuman objects increases consumers’ emotional attachment to robots (Goetz, Kiesler, & Powers, 2003). Similarities between human behavior and robots ­facilitate robot acceptance and lead to positive consumer attitudes. From a strategic point of view, humanoid robots are particularly suitable for positive and effective integration in service industries (Duffy, 2003). The fact that not all tourists have enough experience with robot-delivered ­services demonstrates that considerable changes in their behavior are less probable, especially in a short-term aspect. Customer attitudes and expectations are not leaning toward a full robotization of the service, but are directed toward combined services, joining mixed forms of service: robots assisted by human ­employees are an optimal combination for satisfying the present customer attitudes ­(IBM-Hilton Field Test, 2016). Robots themselves will probably support and enrich the customer experience, and they will make the process easier to accept. Certain companies develop prototypes focusing on recreating humanoid features, so that machines get positive responses from consumers. If the morphology of robots complies with and resembles the human appearance and movements, the human emotional reaction toward the robots would be more positive, which will form more acceptable customer attitudes toward these smart machines (Lopez, Perez, Zalama, & Bermejo, 2013). Consumers expect interactions with robots to simulate human interactions; they demand to communicate with the robots in the same way they communicate with their relatives, friends, neighbors, and colleagues (Murphy, Hofacker, & Gretzel, 2017b). The entertaining aspect of robotized and automated services and the “halo effect,” meaning that positive general attitudes toward robots plays a major positive role in conditioning positive attitudes toward the use of robots for specific purposes (Ivanov et al., 2018), are not to be underestimated, because it influences customer attitudes through emotions (Chtourou & Souiden, 2010). Customer attitudes toward robots have a specific structure, consisting of different components, combined into a uniform system that includes: specific beliefs,

86    Velina Kazandzhieva and Hristina Filipova

Fig. 1.  Interactions between the Components of Consumer Attitudes Toward Robots in TTH. *TTH – Travel, Tourism, and Hospitality. feelings, and sensations toward modern technologies as a whole; intentions about specific behavior and actions in using them; the actual behavior in purchasing robot-delivered services in tourism and hospitality (Fig. 1). Figure 1, based on the structure of attitudes in the context of consumer behavior theory (Blackwell et al., 2001), shows that assessments, behavioral intentions, and actual behavior are projection of customer attitudes toward robots in TTH. Attitudes include the beliefs, thoughts, and emotions formed in customers’ minds. The elements of the attitudes are interconnected and change in one of them leads to a change in the overall customer attitudes toward robots in TTH. The attitude of customers toward robots is based on different interactions with them during the process of organizing travels. These interactions have a positive, negative, or neutral character based on past experience with robotics. In this regard, Fig. 2 illustrates a matrix of users’ attitude and behavior when using robots in TTH. The users vary from “robophobes” to “robophiles.” Robophobes feel very uncomfortable and have a negative attitude toward robots. They feel threatened by technological progress and the capabilities of robots. At the other end of the scale, robophiles are users who accept robots positively, feel completely comfortable and relaxed when robots are used in TTH. They are highly active in their behavior when interacting with robots and AI in all stages of planning and conducting travels and holidays. From the viewpoint of social psychology and marketing theory, customer attitudes are both conditioned by and dependent upon customers’ demographic and behavioral characteristics. Prior studies have found that attitudes toward robots in tourism depend on tourists’ gender, age, nationality, and attitudes toward such automated technologies in general (Ivanov et al., 2018; Ivanov et al., 2018). Generally, studies found that males and younger generations seem to be more supportive of robotization of TTH services (Ivanov & Webster, 2019a, 2019b). Attitudes toward robots also depend on the combined impact of factors such as: consumers’ prior cognitive beliefs and social effects; perceived usefulness and

Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality    87

Fig. 2.  Dynamics of Consumer Attitudes and Behavior Toward Robots in TTH. the perceived ease of use of smart machines. The impacts of these factors culminate during the experience of tourists in the service process (Tung & Law, 2017). As a matter of fact, Nomura, Suzuki, Kanda, Yamada, and Kato (2011, p. 82) found that respondents who have actually used robots had less negative attitudes than those who did not have interactions with robots. Hence, the actual HRI can change the attitudes of customers toward robots. Customers’ attitudes toward robots are above all a general judgment about the degree of robots’ importance, role, and usefulness in TTH. In most cases, the assessment is not neutral and is subjective, based upon the consumer’s attitudes, experiences, and interpretation of reality. For example, the forecasts that robotics and AI will lead to increased unemployment in some sectors of the workforce in the coming decades do not exclude the positive and favorable customer attitudes toward them in respect to their advantages in delivering TTH services. Therefore, the different attitudes toward the growing automation in tourism, where experiences and interpersonal communication are of foremost importance, need to be the subject of a deep analysis and evaluation from different points of view.

Conclusion Research on customer attitudes toward robots in tourism is accelerating in ­conjunction with the growing application of smart machines in tourism and ­hospitality. At this stage, the specialized studies on the topic are few and with limited scope. The introduction of robots in TTH is still in its initial stage, while customer attitudes are still unclear, incomplete, and limited, mostly because of the lack of enough experience and customer experiences with robots in real situations in tourism and hospitality. As such, most research is speculative, measuring what tourists would expect in robotic services. In addition, tourists’ attitudes are complex, changing fast, and are sometimes hypothetical and may be internally inconsistent. This fact necessitates systematic and in-depth empirical research of customer attitudes toward robotization of TTH services.

88    Velina Kazandzhieva and Hristina Filipova Future research should focus on: ⦁⦁ acceptance and application/use of robots by the different types and genera-

tions of customers;

⦁⦁ customers’ inclination to trust and feel safe when serviced by robots in TTH; ⦁⦁ activities and operations in TTH services to which customers show positive

attitude for robotization;

⦁⦁ the acceptable level of robotization of TTH services; ⦁⦁ negative attitudes, anxiety, and worries in the HRIs in TTH services, their

­triggers, and influencing factors;

⦁⦁ customer preferences toward the appearance of robots, their movements, voice,

and other characteristic features that are crucial for forming positive attitudes toward them; ⦁⦁ factors that have an impact on customer decisions to be served by robots. ⦁⦁ the willingness of users to trust, believe, and accept recommendations and tips from robots; ⦁⦁ the tendency for clients to entrust the organization and booking of their holidays to AI and robots.

Currently, consumers still value human presence in tourism and hospitality services because they have been socialized into a TTH environment in which is dominated by human labor and interactions. There are definitive benefits of the use of human labor, including cultural understandings and the ability to interpret sophisticated communications, especially across cultural divides. While the first generations of robots may not be able to have the sophisticated intercultural communications in the first stages, due to the need to develop more sophisticated response systems and the need to standardize and retain uncomplicated systems, this may go counter to what current customers expect in terms of service. If tourism providers fail to comply with the current consumer need to preserve the customers’ high expectations for human communication and interactions, there is a real risk of a backlash of robophobia (Hodges & Higgins, 2016). However, with proper research and the tweaking of technologies not only to perform tasks in a mechanical process, but also to interact with humans the way that customers are accustomed to communicate, it could work in ways to strongly facilitate the increasing use of RAISA into TTH globally.

References Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude–behavior relation: Reasoned and automatic processes. European Review of Social Psychology, 11(1), 1–33. Arthur, W. (2009). The nature of technology: What it is and how it evolves. New York, NY: The Free Press. Barnett, W., Foose, A., Gruber, T., Keeling, D., Keeling, K., & Nasr, L. (2014). Consumer perceptions of interactive service robots: A value-dominant logic perspective. In Proceedings RO-Man 2014, The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 25–29 August, Milbrae, CA (pp. 1134–1139).

Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality    89 Bartneck, C., Kulic, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robot, 1(1), 71–81. Blackwell, R., Miniard, P., & Engel, J., (2001). Consumer behavior (9th ed.). Ft. Worth, Tex.; London: Harcourt College Publishers. Boomer, I. (2016). ITB Berlin Convention: Robots as the new colleagues in the service sector. Retrieved from Chtourou, M., & Souiden, N. (2010). Rethinking the TAM model: Time to consider fun. Journal of Consumer Marketing, 27(4), 336–344. Chung, N., Tyan, I., & Han, H. (2017). Enhancing the smart tourism experience through geotag. Information Systems Frontiers, 19(4), 731–742. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. Duffy, B. (2003). Anthropomorphism and the social robot. Robotics and Autonomous Systems, 42(3–4), 177–190. Eagly, A. H., & Chaiken, S. (1998). Attitude structure and function. In D. T. Gilbert, S.  T.  Fiske, & G. Lindzey (Eds.), Handbook of social psychology (4th ed., Vol. 1, pp. 269–322). Boston, MA: McGraw-Hill. Edison, S., & Geissler, G. (2003). Measuring attitudes towards general technology: Antecedents, hypotheses and scale development. Journal of Targeting, Measurement and Analysis for Marketing, 12(2), 137–156. Fabrigar, L., MacDonald, T., & Wegener, D. (2005). The structure of attitudes. In  D.  Albarracín, B. Johnson, & M. Zanna (Eds). The handbook of attitudes (79–114). New York, NY: Routledge. Featherman, M., & Wells, J. (2010). The intangibility of e-services: Effects on perceived risk and acceptance. ACM SIGMIS Database, 41(2), 110–131. Giovanis, A., Binioris, S., & Polychronopoulos, G. (2012). An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece. EuroMed Journal of Business, 7(1), 24–53. Goetz, J., Kiesler, S., & Powers, A. (2003). Matching robot appearance and behavior to tastas to improve human-robot cooperation. In Proceedings RO-Man 2003, the 12th IEEE international workshop on robot and human interactive communication, 31 October–2 November, Milbrae, CA (pp. 55–60). Graf, B., & Barth, O. (2002). Entertainment robotics: Examples, key technologies and perspectives. In Proceedings of IEEE/RSJ international conference on intelligent robots and systems – workshop on “robots in exhibitions”. Retrieved from 12.6351&rep=rep1&type=pdf Haring, K., Tawil, D., Watanabe, K., Takahashi, T., & Velonaki, M. (2015). Changes in perception of a small humanoid robot. In 6th international conference on automation, robotics and applications (ICARA) (pp. 83–89). Hodges, L., & Higgins, L. (2016). Travelers expect robots on their holidays by 2020. Retrieved from IBM-Hilton Field-Test a Robot Concierge. (2016). Retrieved from http://www.speechtechmag. com/Articles/Editorial/FYI/IBM-and-Hilton-Field-Test-a-Robot-Concierge-111718. aspx IFR. (2018). Robot density rises globally. International Federation of Robotics, Press Releases, Frankfurt, February 2018. Retrieved from news/robot-density-rises-globally Ivanov, S., & Webster, C. (2017). Designing robot-friendly hospitality facilities. In  Proceedings of the scientific conference on “Tourism. Innovations. Strategies”, 13–14 October, Bourgas, Bulgaria (pp. 74–81).

90    Velina Kazandzhieva and Hristina Filipova Ivanov, S., & Webster, C. (2019a). What should robots do? A comparative analysis of industry professionals, educators and tourists. In J. Pesonen & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, 30 January–01 February, Nicosia, Cyprus (pp. 249–262). Ivanov, S., & Webster, C. (2019b). Perceived appropriateness and intention to use service robots in tourism. In J. Pesonen & J. Neidhardt (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2019, 30 January–01 February Nicosia, Cyprus (pp. 237–248). Ivanov, S., Webster, C., & Seyyedi, P. (2018). Consumers’ attitudes towards the introduction of robots in accommodation establishments. Tourism, 63(3), 302–317. Ivanov, S., Webster, G., & Garenko, A. (2018). Young Russian adults’ attitudes towards the potential use of robots in hotels. Technology in Society, 55, 24–32. Joosse, M., Waveren, S., Zaga, C., & Evers, V. (2017). Groups in conflict at the airport: How people think a robot should act. In CSCW’17 workshop on robots in groups and teams, 26 February, Portland, Oregon. Katz, J., & Halpern, D. (2014). Attitudes towards robots’ suitability for various jobs as affected robot appearance. Behaviour & Information Technology, 33(9), 941–953. Kim, J. S., Christodoulidou, N., & Brewer, P. (2012). Impact of individual differences and consumers’ readiness on likelihood of using selfservice technologies at hospitality settings. Journal of Hospitality & Tourism Research, 36(1), 85–114. Kim, M., & Noh, J. (2004). Prediction of travel abroad: A comparison of the theory of reasoned action and planned behavior. International Journal of Tourism Science, 4(1), 1–16. Kimura, T. (2017). Robotics and AI in the sociology of religion: A human in imago robo­ ticae. Social Compass, 64(1), 6–22. Kuo, C., Chen, L., & Tseng, C. (2017). Investigating an innovative service with hospitality robots. International Journal of Contemporary Hospitality Management, 29(5), 1305–1321. Li, D., Rau, P., & Li, Y. (2010). A cross-cultural study: Effect of robot appearance and task. International Journal of Social Robots, 2, 175–186. Lopez J., Perez, D., Zalama, E., & Bermejo, J. (2013). BellBot – A hotel assistant ­system using mobile robots. International Journal of Advanced Robotic Systems, 10(1), 40–51. Lukanova, G. (2017). Socio-economic dimensions of hotel service. University of Economics – Varna: University Publishing House “Science and Economics” (in Bulgarian). Melson, G., Kahn, P., Beck, A., & Garrett, E. (2005). Robots as dogs? Children’s interactions with the robotic dog AIBO and a live Australian shepherd. In Extended abstracts proceedings of the 2005 conference on human factors in computing systems, CHI 2005, 2–7 April, Portland, Oregon, USA (pp. 1649–1652). Miller, M. R., & Miller, R. (2017). Robots and robotics: Principles, systems, and industrial applications. New York, NY: McGraw-Hill Education. Moura, A., Gosling, M., Christino, J., & Macedo, S. (2017). Acceptance and use of technology by older adults for choosing a tourism destination: A study using UTAUT2. Revista Brasileira de Pesquisa em Turismo [Brazilian Journal of Tourism Research], 11(2), 239–269. Murison, M. (2016). Are robots the future of the travel industry? Retrieved from https:// Murphy, J., Hofacker, C., & Gretzel, U. (2017a). Robots in hospitality and tourism: A research agenda. ENTER 2017: 8 Research Notes. Retrieved from http://agrilife. org/ertr/files/2016/12/RN107.pdf Murphy, J., Hofacker, C., & Gretzel, U. (2017b). Dawning of the age of robots in hospitality and tourism: Challenges for teaching and research. European Journal of Tourism Research, 15, 104–111. Murphy, J., Gretzel, U., & Pesonen, J. (2019). Marketing robot services in hospitality and tourism: The role of anthropomorphism. Journal of Travel & Tourism Marketing, 1–12. Doi: 10.1080/10548408.2019.1571983

Customer Attitudes Toward Robots in Travel, Tourism, and Hospitality    91 Ng, P., Duffy, V., & Yucel, G. (2011). Impact of dynamic virtual and real robots on perceived safe waiting time and maximum reach of robot arms. International Journal of Production Research, 50(1), 161–176. Nomura, T., & Kanda, T. (2003). On proposing the concept of robot anxiety and considering measurement of it. In Proceedings RO-Man 2003, the 12th IEEE international workshop on robot and human interactive communication, 31 October–2 November, Milbrae, CA (pp. 373–378). Nomura, T., Kanda, T., Suzuki, T., & Kato, K. (2006). Measurement of anxiety toward robots. In Proceedings RO-Man 2006, the 15th IEEE international symposium on robot and human interactive communication, 6–8 September, Milbrae, CA (pp. 372–377). Nomura, T., Kanda, T., Suzuki, T., & Kato, K. (2008). Prediction of human behavior in human–robot interaction using psychological scales for anxiety and negative attitudes toward robots. IEEE Transactions on Robotics, 24(2), 442–451. Nomura, T., Suzuki, T., Kanda, T., Yamada, S., & Kato, K., (2011). Attitudes toward robots and factors influencing them. In K. Dautenhahn & J. Saunders (Eds.), New frontiers in human robot interaction (Vol. 2, 73–88). Amsterdam: John Benjamins Publishing Company. Oborn, E., Barrett, M., & Darzi, A. (2011). Robots and service innovation in health care. Journal of Health Services Research & Policy, 16(1), 46–50. Papathanassis, A. (2017). R-Tourism: Introducing the potential impact of robotics and service automation in tourism. “Ovidius” University Annals, Economic Sciences Series, 17(1), 211–216. Parasuraman A., & Colby, Ch. (2014). An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research, 18(1), 59–74. Parasuraman, A. (2000). Technology readiness index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320. Perloff, R. (2003). The dynamics of persuasion: Communication and attitudes in the 21st  ­century (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates. Retrieved from e-book-dinamic-persuasive.pdf Pierce, A. (2015). A hotel staffed by robots. Technology Today. Retrieved from http://www. technology Pradhan, M., Oh, J., & Lee, H. (2018). Understanding travelers’ behavior for sustainable smart tourism: A technology readiness perspective. Sustainability – Open Access Journal, 10(11), 4259. Rau, P. L., Li, Y., & Li, D. (2009). Effects of communication style and culture on ability to accept recommendations from robots. Computers in Human Behavior, 25(2), 587–595. Ryu, K., & Jang, S. (2006). Intention to experience local cuisine in a travel destination: The modified theory of reasoned action. Journal of Hospitality & Tourism Research, 30(4), 507–516. Singer, R. (2016). Robots and artificial intelligence in the hotel industry. Retrieved from archiv_2016/itb_hospitality_day_3/Robots_And_Artificial_Intelligence_In_The_ Hotel_Industry_Singer.pdf European Commission. (2012). Special Eurobarometer 382: Public attitudes towards robots. Retrieved from ebs/ebs_382_sum_en.pdf Thrun, S. (2004). Toward a framework for human–robot interaction. Human–Computer Interaction, 19(1), 9–24. Trakulmaykee, N., Trakulmaykee, Y., & Hnuchek, K. (2015). Two perceived dimensions of technology acceptance model in tourist guide context. International Journal of Trade, Economics and Finance, 6(5), 278–282.

92    Velina Kazandzhieva and Hristina Filipova Travelzoo. (2016). Travelers expect robots on their holidays by 2020. PR Newswire, March  8.  Retrieved from Tung, V., & Au, N. (2018). Exploring customer experiences with robotics in hospitality. International Journal of Contemporary Hospitality Management, 30(7), 2680–2697. Tung, V., & Law, R. (2017). The potential for tourism and hospitality experience research in human–robot interactions, International Journal of Contemporary Hospitality Management, 29(10), 2498–2513. Tussyadiah, I. P., & Park, S. (2018). Consumer evaluation of hotel service robots. In  B.  Stangl & J. Pesonen (Eds.), Information and communication technologies in tourism (pp. 308–320). Cham, Switzerland: Springer. Tussyadiah, I. P., Zach, F. K., & Wang, J. (2017). Attitudes toward autonomous on demand mobility system: The case of self-driving taxi. In R. Schegg & B. Strangl (Eds.), Proceedings of the international conference on information and communication technologies in tourism 2017, 24–26 January, Rome, Italy (pp. 755–766). Tutek, E., Gebbie, M., Chan, K., & Durand, S. (2015). Tourism megatrends. Horwath HTL. Retrieved from Varol, E., & Tarcan, E. (2009). An empirical study on the user acceptance of hotel information systems. Tourism, 57(2), 115–133. Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, ­intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71–102. Venkatesh, V., Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. Venkatesh, V., & Davis. F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. Wang, L., Rau, P. L., Evers, V., Robinson, B. K., & Hinds, P. (2010). When in Rome: The role of culture and context in adherence to robot recommendations. In Proceedings of the 5th ACM/IEEE international conference on human robot interaction, Osaka, Japan (pp. 359–366). Wang, Y., & Qualls, W. (2007). Towards a theoretical model of technology adoption in hospitality organizations. International Journal of Hospitality Management, 26(3), 560–573. Wang, Y., & Sparks, B. (2014). Technology-enabled services: Importance and role of technology readiness. Tourism Analysis: An Interdisciplinary Journal, 19(1), 19–33. Wirtz, J., Patterson, P., Kunz, W., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. World Economic Forum. (2017). Digital transformation initiative aviation, travel and tourism industry. Retrieved from ­ WEF_ATT_DigitalTransformation_White Paper.pdf Wu, I., Li, J., & Fu, C. Y. (2011). The adoption of mobile healthcare by hospital’s professionals: An integrative perspective. Decision Support Systems, 51(3), 587–596. Yamazaki, A., Yamazaki, K., Burdelski, M., Kuno, Y., & Fukushima, M. (2010). Coordination of verbal and non-verbal actions in human–robot interaction at ­museums and exhibitions. Journal of Pragmatics, 42(9), 2398–2414.

Chapter 5

Making Sense of Robots: Consumer Discourse on Robots in Tourism and Hospitality Service Settings Ulrike Gretzel and Jamie Murphy Introduction While industrial robots have been around for over half a century and are widely integrated into manufacturing processes, they have recently evolved to facilitate automation beyond factory floors (Carrozza, 2019). Wired Magazine (Wired. com, 2017) identified the year 2017 as a turning point for robotics, fueled by cost decreases in sensor technology and robotic joint actuators, increasingly small and energy-efficient computer chips, as well as artificial intelligence advances that increase mobility and enable sophisticated human-robot interaction (HRI). Progress in service robot development is expected to transform the tourism and hospitality industries in profound ways (Bowen & Morosan, 2018; Ivanov, 2019). This dawning of the age of service robots in tourism and hospitality raises important questions regarding robot uses and acceptance in a range of service settings (Murphy, Hofacker, & Gretzel, 2017). Understanding users’ interpretation of a technology is critical to ­understanding interactions with that technology (Tu, Wu, Hsieh, & Lin, 2011). Technology ­varies in interpretive flexibility and different users in different positions can view technology quite differently (Siino & Hinds, 2005). Robots are an especially complex technology and the introduction of robots in service settings has triggered sophisticated, dynamic, and diverse sensemaking processes, even before their adoption or implementation (Shick, Forlizzi, & Fussell, 2009). Existing literature of consumer reactions to robots mostly focuses on cognitive or behavioral responses. This focus is true for robots in general and specifically for service robots in tourism and hospitality settings (Rodriguez-Lizundia, Marcos, Zalama, Gómez-García-Bermejo, & Gordaliza, 2015). Despite a growing body of such research, Ivanov, Gretzel, Berezina, Sigala, and Webster (2019) find significant gaps in the literature on understanding human-robot relationships in tourism and hospitality service contexts.

Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality, 93–104 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78756-687-320191005

94    Ulrike Gretzel and Jamie Murphy Technology consumption research must go beyond simple reactions and include the meanings and myths consumers ascribe to technological artifacts in order to understand emerging technology paradoxes and specific consumer coping strategies (Mick & Fournier, 1998). Kozinets (2008) argues that consumer narratives surrounding technology emerge from an ideological field in which sensemaking occurs in relation to different ideological positions. Technology sensemaking research is largely absent from the tourism and hospitality literature, particularly from research on service robots. This chapter begins the identification of how tourism and hospitality consumers make sense of robots and how ideological positions color their sensemaking process.

Literature Review Engineering research to inform robot design dominates the extant research on ­service robots in general, and particularly in hospitality and tourism (Ivanov et al., 2019). A growing number of conceptual papers introduce the concept of robotic service (rService) and examine the implications of increasing service automation (Ivanov, Webster, & Berezina, 2017; Larivière et al., 2017; Murphy et al., 2017; van Doorn et al., 2017; Wirtz et al., 2018). This literature generally discusses the topic from a utopian perspective, assuming that advances in ­robotics will have positive impacts. Yet, some contributions explore the complexity of robotic tourism scenarios (Yeoman & Mars, 2012) or discuss ethical issues (e.g., van Wynsberghe, 2016), often in relation to job replacement (Huang & Rust, 2018). Discourse on the ideological positions that frame hospitality and tourism service robots, however, is missing. The extent to which ideologies color perceptions of service robots is especially absent from research that focuses on consumers. Instead, the emerging literature investigates responses to robots within technology acceptance and service quality frameworks.

Attitudes toward Service Robots Weiss, Bernhaupt, Lankes, and Tscheligi, (2009) suggest evaluating HRIs across four indicator categories – Usability; Social Acceptance; User Experience; and Societal Impact – the USUS evaluation framework. Attitudes toward using robots, conceptualized as a Social Acceptance measure, is the sum of all positive or negative feelings and attitudes toward the robot. Within this framework, Tung and Au (2018) explore consumer perceptions of robots and find varied reactions ranging from fear to trust and excitement to disappointment across different HRI experiences. Ivanov, Webster, and Garenko (2018) further dissect attitudes toward service robots in general and specifically in hospitality settings, finding that such attitudes are significant drivers of robot acceptance. They also confirm significant gender differences in attitudes toward robots. In addition, research by Ivanov, Webster, and Seyyedi (2018) reveal a direct link between perceived advantages of robots and attitudes toward hospitality service robots. Murphy, Gretzel, and Pesonen (2019) point out the significant role of robot anthropomorphism in relation to rService perceptions, influencing affective

Making Sense of Robots    95 reactions, acceptance, and ultimately loyalty. As such, the paper provides an overview of the quite extensive research that has investigated the impact of robot anthropomorphism on consumer attitudes. For instance, varying degrees of anthropomorphism in self-service technology can trigger significantly different reactions to service failures (Fan, Wu, & Mattila, 2016). Yu (2019) illustrates that differences in perceived likeability, intelligence, safety, animacy, and anthropomorphism (the so-called Godspeed dimensions) color the varied attitudes toward service robots apparent in comments to robot-related YouTube videos. Similarly, research links perceived human-likeness to intentions to adopt hospitality service robots (Tussyadiah & Park, 2018). Mende, Scott, van Doorn, Grewal, and Shanks (2019) demonstrate that service robots with human-like morphology can provoke discomfort in consumers and lead to compensatory consumption behaviors. Finally, even subtle manipulations of anthropomorphism in service robots, such as head tilt, can significantly alter attitudes toward service robots (Yu & Ngan, 2019). That measurement scales such as the Negative Attitudes toward Robots Scale and Robot Anxiety Scale have been ubiquitous for over a decade best exemplifies the focus on attitudes (Nomura, Kanda, Suzuki, & Kato, 2008). What is missing from the service robot literature is a deeper understanding of consumers’ sensemaking processes surrounding the anticipation of and exposure to service robots.

Technology Sensemaking Weick (1995) posits that technology is equivoque and always requires sensemaking; a lack of sensemaking can lead to resistance (Rodriguez-Lizundia, 2015). While technology sensemaking has a two decade-long tradition in organizational studies (Mesgari & Okoli, 2018), technology sensemaking has received little attention in consumer studies and is also largely absent from tourism and hospitality literature. Only Högberg and Olsson (2018) have recently discussed technological frames and sensemaking in the context of social media adoption in hotels. Mesgari and Okoli (2015) explain that technology sensemaking involves attributing meanings to experiences. In their review of technology sensemaking literature, they find three big streams of research: 1. Cognitive; 2. Social; and 3. Materiality. The cognitive stream focuses on “the cognitive structures and p ­ atterns based on which people make and ascribe meaning to technology” (­Mesgari & Okoli, 2015, p. 2). These cognitive structures include individuals’ scripts, technological frames, and mental models that guide their attitudes and behaviors toward technologies. In contrast, the social stream researches social factors that facilitate technology sensemaking, such as social norms, power structures, and roles and the influence of peers and powerful others. For example, workers in female- and male-dominated occupational positions develop different understandings about robots (Siino & Hinds, 2005). Finally, the materiality stream explores relationships between design elements of the technology and user perceptions. Bemgal (2018) proclaims that technological affordances create conditions that generate individual and social sensemaking processes, which are cognitive and

96    Ulrike Gretzel and Jamie Murphy embodied. Mesgari and Okoli (2018) further argue for an ecological stream of technology sensemaking research that sees affordance perceptions as arising from relationships between users and the technological artifacts to which specific user species adapt within technological niches. Technology sensemaking has been applied to robots, for instance, in the Siino and Hinds (2005) study mentioned above. Another example describes children’s sensemaking processes with a social robot in a learning context and the effect of their conceptualizations on interactions with the robot (Kory Westlund & Breazeal, 2016). Shick et al. (2009) report that subjects had a hard time making sense of a robot that was prone to making mistakes. Sung, Guo, Grinter, and Christensen (2007) indicate that anthropomorphizing a robot vacuum allowed users to form intimate relationships with the robot, which completely changed their interactions with the device and their perception of cleaning. Last, a recent paper posits that consumers make sense of intelligent technologies by conceptualizing them as either a servant, a partner/friend or a master (Schweitzer, Belk, Jordan, & Ortner, 2019). No technology sensemaking research, however, explores the ideological frames that users access to support the process.

Technology Ideology Building on calls to explore ideological discourses related to technology consumption (Thompson, 2004), Bajde, Nøjgaard, and Sommer (2019) propose that a consumer culture theory perspective is necessary for uncovering the cultural forces and dynamics in which technology consumption is entangled and to articulate the cultural processes (e.g., ideological, mythic, and ritualistic) through which technology meanings emerge. Existing studies often force consumers into simple categories of either resistant technophobes or exuberant technophiles and neglect to uncover the complexity and plurality of ideological frames that shape technology consumption (Kozinets, 2008). Kozinets’s (2008) technology ideology paper unravels how four technology ideologies – Techtopian, Green Luddite, Work Machine, and Techspressive ­ (Fig. 1) – influence consumer thought, speech, and action. Importantly, consumer discourse moves effortlessly within this dynamic ideological field. The Techtopian area is characterized by a central, optimistic notion of technology as progress, enabling empowerment and moral improvement, albeit being essentially amoral and rational. In contrast, the Green Luddite ideology sees technology as inherently inauthentic, dehumanizing, and threatening to natural and traditional ways of life. The Work Machine area amalgamates notions of technology as an economic engine that will drive national, corporate, and individual wealth. Although focused on efficiency and productivity gains, the Work Machine has undertones of enslavement, exploitation, and loss of control. Like the Techtopian thoughts, it is a rational way of thinking about technology. Last, the Techspressive ideology encapsulates understanding technology as leading to fulfillment and pleasure. Consumers play, have fun, and express themselves in new ways as technology manifests as a toy.

Making Sense of Robots    97

Fig. 1.  The Ideological Field of Technology. Source: Adapted from ­Kozinets (2008); reprinted with permission from the author. Although distinctive and different, the four ideologies also share traits. ­ imilar to the Green Luddite ideology, Techspressionism is highly emotional S but focuses on pleasure rather than fear. Similar to the Work Machine orientation, social goals are absent from the Techspressive sphere, while Techtopian and Green Luddite views are socially and collectively oriented. Technology-­ evaluation criteria, however, differ fundamentally between the Green Luddite and the Work Machine views; the latter focuses on economic performance while the Green Luddite ideology calls for standards like level of preservation and harmony. Kozinets (2008) illustrates with empirical data that consumers may not stick to one ideology when making sense of technology but rather switch fluidly between areas and make room for ambivalence, thus creating complex ­technology narratives. In the tourism field, two studies apply technology ideology to discuss conceptualizations and uses of educational and Internet technologies (Ismail, Hashim, Gemignani, & Murphy, 2012; Munar & Bødker, 2014). In relation to robots, Strait, Aguillon, Contreras, and Garcia (2017) observed that consumer narratives referenced fear of replacement and fear of a robot apocalypse. The first references a Green Luddite orientation, while the second theme conceptualizes

98    Ulrike Gretzel and Jamie Murphy robots as machines increasingly in control of essential resources. Building on the Kozinets (2008) framework, this chapter further explores ideological orientations in consumer narratives about tourism and hospitality service robots.

Methodology Sensemaking, often both an individual and a social process (Weick, 1995), requires a methodology that can collect social discourse data. This research used netnography (Kozinets, 2015) to capture online consumer discourses in an unmediated form, that is, without researcher interference. Researchers have used such online comments regarding robots because of the ability to explore opinions that are inclusive of the types of users they represent and not specific to particular robot designs (Strait et al., 2017). Yu (2019) also notes that online comments on platforms besides online review sites do not necessarily require actual experience with robots, thus facilitating research while the technology is still emerging. Existing analyses of online discourse surrounding robots (e.g., Strait et al., 2017; Tung & Au, 2018; Yu, 2019) have focused on classifying statements based on their sentiment and their relation to specific aspects of HRI. Netnography, a form of qualitative research, seeks to derive rich cultural understandings, and therefore focuses not only on explicit discourse, but also interprets implicit ­meanings (Kozinets, 2015). Immersion in the subject matter and the netnographic fieldwork is essential, as is deep reflection by the researchers. Central to netnography is the initial phase of identifying websites in which such rich immersion is possible. While netnography subsumes a variety of tools, such as online research websites, chat-based interviewing and participant observation, this research involved only observation. Specifically, it observed postings and comments on a hotel review site (related to a robot hotel in Japan, 148 reviews; and a hotel in California that uses a robot for room delivery, 281  reviews), ­YouTube (related to robots in hotels, 143 videos, and a specific robot brand that has been widely adopted as a concierge, seven videos), Reddit (based on searches for “robot hotel” and robot brands relevant to hospitality services), as well as searching Instagram for hashtags regarding relevant robot brands (about 4,800 posts). These platforms afford different kinds of commenting actions and support various levels of richness in comments. Importantly, they attract different user groups. The field phase lasted 2 years (March 2017–April 2019, with regular checks for new data) and included taking screen shots of relevant posts. Data analysis involved iterative interpretation of the data using Kozinets’s (2008) four different technology ideologies, while also identifying emerging themes through the analytic flexibility that thematic analysis provides (Clarke & Braun, 2017).

Findings The results of the research show both diversity and sophistication in the discourse surrounding tourism and hospitality robots. The discussions largely map onto the four areas of the ideological field described by Kozinets (2008). Table 1 provides

Making Sense of Robots    99 Table 1.  Exemplary Technology Ideology Comments. Technology Ideology


Example Quote

Work machine

Robot as efficient service provider

“This robot is a lean mean #delivery machine.” (Instagram post)


Robot as social change agent

“The robot isn’t taking anyone’s job. It is making someone’s job easier.” (Reddit comment)


Robot as playmate

“The kids had heaps of fun talking, playing and dancing with it.” (Instagram post)

Green Luddism

Robot as threat

“Botlr will bring you treats and toiletries when you need them … while secretly planning the destruction of the human race.” (Instagram post)

Justice and morality

Robot as being with rights

“I am dreadful of what sound it would make if you gave it a 1 star rating.