Robots in Tourism – Preliminary Results from a Global Study

Robots in Tourism – Preliminary
Results from a Global Study
Stanislav Ivanova and Craig Websterb
aVarna University of Management, Bulgaria
bBall State University, USA

31 January 2019 ENTER2019 – Research Track Page 2
Source: Ugljesa Stankov

69th AIEST Conference
Key facts:
• Dates: 25th-29th August 2019
• Organiser: Varna University of Management, Varna, Bulgaria
• Venue: Rosslyn Hotel Dimyat 4* (http://dimyat.rosslyn-hotels.com/)
• More information at: https://www.aiest.org/conference/conference-2019-
varna/
4

https://nexttourismgeneration.eu

Robots in Tourism –
Preliminary Results from a Global Study
The presentation combines the results of two ENTER 2019 papers:
• Ivanov, S., & Webster, C. (2019). Perceived appropriateness and
intention to use service robots in tourism. In Pesonen, J. & Neidhardt,
J. (Eds.) Information and Communication Technologies in Tourism
2019, Proceedings of the International Conference in Nicosia, Cyprus,
30.01-01.02.2019, pp. 237-248.
• Ivanov, S., & Webster, C. (2019). What should robots do? A
comparative analysis of industry professionals, educators and tourists.
In Pesonen, J. & Neidhardt, J. (Eds.) Information and Communication
Technologies in Tourism 2019, Proceedings of the International
Conference in Nicosia, Cyprus, 30.01-01.02.2019, pp. 249-262.

Robots have arrived …

Source: http://vignette4.wikia.nocookie.net/starwars/images/c/c8/Counterparts.jpg https://en.wikipedia.org/wiki/BB-8

Source: http://vignette2.wikia.nocookie.net/avp/images/4/49/Terminator.jpg

Source: http://www.kurzweilai.net/images/robot-thinking-one.png

• Manufacturing
http://www.businesskorea.co.kr/sites/default/files/field/image/Industrial-Robots-Hyundai-Heavy.jpg

• Warehousing, supply and logistics
https://abm-website-assets.s3.amazonaws.com/inddist.com/s3fs-public/styles/hero/public/featured_image/2017/01/Amazon-Robots.jpg?itok=fhqW-lPW

• Agriculture • Autonomous vehicles
http://sparc-robotics.eu/wp-content/uploads/2014/05/polni-robot-amazone-bonirob-44e031.jpg https://media.wired.com/photos/5a6fbe7d68851b1549e7d280/master/w_799,c_limit/
Waymo-Minivan.jpg

• Guards
http://cdn.hiconsumption.com/wp-
content/uploads/2014/11/K5-Security-
Guard-Robot-3.jpg
https://media.wired.com/photos/5c48c3ab9fa6e032a790af23/master/w_799
,c_limit/amazon-scout.jpg
• Parcel delivery• Households
http://www.roboticstrends.com/images/photos/xiaomi-mi-robot-vacuum.jpg

• Gardens • Swimming pools
https://allpools.net/wp-content/uploads/2017/08/2-Various-Types-of-Pool-Cleaners.jpg
https://hgcdn82.azureedge.net/qs_h=720&crop=1&w=720&ver=00000000T000000&
hcsh=292EF7F8BB3804096DCBD1044248BDCA0DBFCF8E/_$$_/media/damroot/husq
varna/garden%20lawnmowers%20and%20ride-
on%20mowers/robotic%20lawnmowers/action/h350-02xx/h350-0236.ashx

• Medicine • Warfare
http://mediad.publicbroadcasting.net/p/wual/files/styles/x_lar
ge/public/201712/xi.jpg
https://media.defense.gov/2010/Feb/01/2000398487/1088/820/0/090609-F-0000M-777.JPG

• Education • Entertainment
Source: https://3e7e6c34hre53ebyea38hmr6-wpengine.netdna-ssl.com/wp-
content/uploads/2017/09/nao.png
https://robots.nu/img/uploads/2018/08/10/Vector%2C%20ador
able%20robot%20from%20Anki.jpg

• Be smart-looking and
attentive PhD students!
Source: Ulrike Gretzel

• Information provision in service industries

• Sex robots
https://surgefs.imgix.net/quality=v:80/K25JiHV0QUKIXCo9usln?auto=format&ixlib=imgixjs-3.3.2&w=1000 https://www.vanguardngr.com/wp-content/uploads/2018/02/Sex-robot-2.png
18+

What about tourism and hospitality?
23

• Hotels
http://www.h-n-h.jp/assets/images/facility_img_01.jpg
http://www.mirror.co.uk/news/uk-news/futuristic-
hotel-thats-like-robotic-6449905
https://www.hartrobotics.com/wp
-content/uploads/2016/01/relay-
specifications-1.jpg

• Hotels

• Restaurants
https://naulo.com.np/static/base/images/ginger_robot_waiting_at_kitchen.jpg https://amazing.zone/fotosblog/max/drone_q_entrega_pizzas.jpg

• Restaurants
https://cdn.winsightmedia.com/platform/files/public/2018-09/background/spyce-restaurant_1536600954.jpg

• Restaurants
https://www.youtube.com/watch?v=wvDLWS-_eZ8

• Meetings and events / Telepresence
https://www.bizbash.com/mobile-telepresence-new-systems-allow-users-participate-
meetings-events-afar/gallery/123291
• Meetings and events / Bars
https://www.bizbash.com/bartender-makr-shakr-bartender-social-sharing-alcohol-
consumption-monitor/gallery/123237

Introduction

Introduction
• The International Organization for Standardization defines a robot as an
“actuated mechanism programmable in two or more axes with a degree
of autonomy, moving within its environment, to perform intended tasks”.
• While robots have been widely used in many different industries, both in
terms of manufacturing/production as well as service, the
implementation of robots into tourism-related industries has been rather
recent, presumably because of the high level of customer service needed
in the industry. With the evolution of technologies that can now react to
the demands of industry, they are now increasingly being used by
tourism-related industries.

• These two papers delve into perceptions of the public into the use of
robots in tourism-related industries. Specifically, they aim to:
a) Evaluate individuals’ perceptions about the use of robots for
implementing specific tasks and activities in travel, tourism and
hospitality;
b) Identify how the perceived appropriateness of robots for implementing
these activities influences the intentions of people to use them in a
tourism setting;
c) Assess the role of respondents’ occupation on their perceptions about
the use of robots in a tourism setting

Literature review

Literature review
• In just a few years, the academic literature has expanded with regards
to exploring the attitudes towards robots.
• Within travel, tourism and hospitality, research on robotics is very
scarce and it tends to be driven by engineering and the design of
robots.
• From a social sciences perspective, there are a few academic
publications that deal directly with issues linked to the adoption of
robots by travel, tourism and hospitality companies and their
acceptance by consumers.

Literature review

Literature review
• Source: 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).

Literature review

Literature review
• Forthcoming:
Ivanov, S. & Webster, C. (Eds.) (2019).
Robots, Artificial Intelligence and Service
Automation in Travel, Tourism And
Hospitality. Emerald.

Methodology

Methodology
• Global study on robots in tourism
• Ethics approval by Ball State University, USA
• Draft questionnaire checked by 6 experts in the field – Ulrike Gretzel,
Katerina Berezina, Iis Tussyadiah, Jamie Murphy, Dimitrios Buhalis and
Cihan Cobanoglu
• Online questionnaire available in 12 languages
• 7-point Likert scale used for level of agreement statement
• 30 minutes necessary to complete the questionnaire – several control
questions included

Methodology
• Data collection stated in March 2018 and still goes on
• The two conference papers present the preliminary results based on
the data collected by 7th September 2018
• Sample size: 1003 respondents
• Methods for data analysis – exploratory factor analysis, regression
analysis, Mann-Whitney U-test, Kruskal-Wallis Chi-square test
• Grouping of respondents: tourism industry professionals, tourism
educators, robotics industry professionals and researchers, rest of
respondents

Methodology
• Sample characteristics
Characteristic Number of respondents Percent
Total 1003 100.0
Gender Male 439 43.8
Female 564 56.2
Age 18-30 466 46.5
31-40 257 25.6
41-50 152 15.2
51-60 79 7.9
61-70 42 4.2
71+ 7 0.7

Methodology
Country of living USA 281 28.1
Bulgaria 280 28.0
UK 43 4.3
Turkey 29 2.9
Portugal 23 2.3
Russian Federation 19 1.9
India 15 1.5
Spain 15 1.5
France 14 1.4
Brazil 13 1.3
Other (77 countries and territories) 268 26.8

Methodology
Education High school or less 125 12.5
2 year/Associate degree 66 6.6
Bachelor 283 28.2
Master 313 31.2
Doctorate 216 21.5
Occupation Travel, tourism and hospitality industry 115 11.5
Tourism/hospitality education 159 15.8
Robotics (education, research, industry) 22 22.2
Other 701 70.0
Not specified 6 0.5

Results

Results
The activities with highest acceptance by respondents include:
• Provision of information about departures / arrivals (m=5.95), about
seat/berth availability (m=5.88), and about ticket prices/fees (m=5.88) at
airports and other transport stations
• Garbage collection (m=5.86)
• Cleaning the common areas of the hotel / theme park / airport / restaurant
etc. (m=5.79)
• Provision of information about the trip/flight/route (m=5.77) and about the
vehicle (m=5.76), and Luggage carrying in hotels / airports etc. (m=5.72).

Results
The most inappropriate areas for robot application are considered:
• Babysitting (m=2.39)
• Hairdressing (m=2.98)
• Dancing with guests (m=3.24)
• Massages (m=3.62)
• Cooking food (m=3.66)
https://www.nextnature.net/app/uploads/2016/09/Screen-Shot-2016-09-29-at-14.14.31-1024x528-1.png

Results
• Perceived appropriateness of robot application in various activities
Activity Mean values M-W U-test:
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Travel, tourism and hospitality industries in general
Welcoming / greeting a tourist/guest/passenger 4.10 4.12 5.50 4.41 4.35 786.5** 11.353**
Accompanying the guest when leaving the hotel/restaurant/theme park 4.07 3.97 4.77 4.17 4.14 1330 3.613
Providing information about facilities of the hotel/restaurant/theme
park/airport/bus station/train station, etc.
5.76 5.62 5.82 5.60 5.63 6378 3.490
Providing information about the destination 5.66 5.46 5.68 5.64 5.61 1493.5 3.775
Booking tourist services (e.g. flight tickets, hotel accommodation, transfers, rent-a-
car, travel insurance, etc.)
5.00 5.13 5.59 5.34 5.27 1025.5 3.515
Concierge services (e.g. ordering tickets for shows, taxis) 5.05 5.04 5.55 5.21 5.17 1357.5 3.324

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Issuing travel documents (e.g. voucher, tickets) 5.39 5.57 5.41 5.45 5.46 8813 0.189
Issuing payment documents (e.g. invoice, receipt) 5.30 5.73 5.27 5.55 5.55 1534.5 2.650
Processing cash payments 5.11 5.43 5.09 5.35 5.33 1588 1.270
Processing credit card and debit card payments 5.25 5.62 5.36 5.52 5.50 8173.5 1.984
Luggage carrying in hotels / airports etc. 5.63 5.79 5.59 5.73 5.72 1638.5 0.931
Luggage storage in hotels / airports etc. 5.55 5.91 5.55 5.59 5.63 1631.5 6.342
Garbage collection 5.77 5.89 5.73 5.87 5.86 1694.5 0.038
Cleaning the common areas of the hotel/theme park/airport/restaurant 5.71 5.95 5.64 5.78 5.79 1602 2.204

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Providing gardening services 4.94 5.26 5.50 5.09 5.11 1014 5.061
Providing repair and maintenance in a facility 4.92 5.18 4.77 5.01 5.02 1474 2.291
Serve as guards / security 3.90 4.11 4.82 3.68 3.80 5219.5* 12.133**
Helping tourists/guests/passengers in case of emergency 4.00 3.89 4.45 3.90 3.92 1443 1.817
Hotel > Reception
Check-in 4.80 4.52 5.48 4.98 4.89 1167.5* 9.258*
Guiding to the room 4.56 4.39 5.68 5.00 4.87 1060.5** 19.108***
Check-out 4.88 4.88 5.36 5.21 5.12 1456 5.898

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Hotel > Housekeeping
Cleaning the room 5.23 5.29 5.55 5.20 5.23 6719.5 1.423
Taking customer orders for laundry 5.42 5.58 5.45 5.52 5.52 8178 1.410
Laundry service 5.61 5.85 5.64 5.58 5.63 50091.5 3.193
Ironing service 5.25 5.46 5.23 5.19 5.24 50080 2.383
Delivering ready laundry 5.74 5.67 5.55 5.65 5.66 1195 0.206
Taking customer orders for new towels, linen, etc. 5.57 5.62 5.55 5.67 5.65 7360 0.291
Delivering new towels, linen, etc. 5.63 5.58 5.68 5.58 5.59 1638 0.230

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Hotel > Food and beverages / Restaurants
Taking orders for room service 5.33 5.41 5.73 5.43 5.42 1200.5 0.517
Delivering food and drinks in room service 4.87 5.03 5.45 5.22 5.16 1110 2.536
Guiding guests to tables in the restaurant 4.42 4.55 5.27 4.90 4.80 974 9.104*
Providing information about the menu 5.12 5.04 5.59 5.15 5.14 1415.5 2.019
Taking orders in the restaurant 4.85 4.79 5.59 4.99 4.96 1299.5 3.990
Cooking food 3.76 3.59 4.59 3.62 3.66 1242.5* 5.196
Serving food in the restaurant 4.25 4.21 4.82 4.54 4.46 1407 5.015
Making drinks (coffee, tea, cocktails) in the restaurant/bar 4.48 4.46 5.18 4.51 4.51 1350.5 2.722
Serving drinks in the restaurant/bar 4.14 4.28 4.95 4.49 4.43 984.5 4.771

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Hotel > Additional services
Massages 3.83 3.37 4.64 3.62 3.62 1101.5** 8.948*
Playing games with the guests 4.60 4.10 4.95 4.35 4.35 1271.5 6.034
Entertaining the guests 4.31 4.27 5.05 4.31 4.32 1269 3.137
Dancing with guests 3.37 2.95 4.41 3.24 3.24 971*** 12.334**
Babysitting 2.81 2.24 3.73 2.31 2.39 903.5*** 19.393***
Provision of very short 1-2 hour workshops to guests (e.g. on gardening, cooking,
painting, astronomy, etc.)
3.92 3.90 4.91 4.00 3.99 1207** 5.405
Hairdressing 3.03 2.93 4.09 2.95 2.98 1119.5** 8.272*

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Travel agency / Tourist information centre
Provision of information about the offers (in the office of the agency / TIC) 5.25 4.96 5.55 5.27 5.22 1365.5 5.139
Robot tour guide in the destination (outside the office of the agency / TIC) 4.42 4.24 5.00 4.62 4.54 1335 5.679
Theme park
Automation of rides 4.96 5.15 5.14 4.64 4.77 44872.5** 13.354**
Robotic control of the rides 4.74 4.98 4.62 4.41 4.54 44110*** 13.999**
Serve as robot-entertainers / show participants 4.32 4.29 5.29 4.49 4.46 1171.5** 5.798

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Events
Providing information about the event 5.56 5.37 5.59 5.59 5.55 1554.5 3.949
Guiding the participants to their seats 4.81 4.72 5.41 5.10 5.01 1362.5 6.959
Serving food during the event 4.44 4.29 5.05 4.76 4.66 1324.5 9.873*
Serving drinks during the event 4.51 4.42 4.95 4.80 4.71 1451 6.137
Serve as robot-entertainers / show participants 4.33 4.27 4.95 4.49 4.45 1375.5 3.275
Museums and galleries
Providing information about the exhibits 5.50 5.54 5.67 5.61 5.59 1129 1.520
Robot tour guide in the museum / gallery 4.89 4.92 5.43 5.12 5.07 1062 2.270

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Rent-a-car
Self-driving cars 5.03 4.67 5.00 4.51 4.61 33090** 8.318*
Cleaning the vehicles 5.74 5.59 5.55 5.71 5.69 1140 1.014
Robotic car key delivery 5.40 5.37 5.32 5.35 5.36 1220.5 0.475
The car is opened and started by access code received with the booking (no
physical key)
5.38 5.39 5.64 5.21 5.27 6412.5 3.900
The car is electric 5.76 5.71 5.45 5.72 5.72 1161 0.550
The car goes to a gas station / charging station automatically when fuel tank /
battery is below a specific limit
5.04 5.06 5.14 4.79 4.87 6717.5 5.069

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Airplanes/Buses/Trains
Self-driving planes 3.98 3.78 4.95 3.50 3.64 4603.5*** 14.750**
Self-driving buses 4.39 4.07 5.18 3.93 4.04 5002.5** 11.331**
Self-driving trains 4.68 4.53 5.41 4.32 4.42 5241.5** 9.356*
Self-driving vessels (e.g. ships, cruise ships) 4.30 4.05 5.14 3.84 3.96 4868** 12.629**
Check-in (e.g. at airports) 5.37 5.19 5.86 5.44 5.41 1373.5 2.912
Guiding the passenger to the seat 4.95 4.75 5.64 5.15 5.08 1258 7.696
Providing information about the vehicle 5.73 5.65 5.76 5.79 5.76 50969.5 0.842
Providing information about the trip/flight/route 5.80 5.72 5.82 5.77 5.77 1613.5 1.856
Providing information about the safety and security procedures and regulations on
5.63 5.65 5.64 5.74 5.71 38481.5 0.693

Results
min vs max
K-W χ2-test: all 4
groupsTI TE R O T
Airplanes/Buses/Trains
Serving food on board 4.58 4.52 5.27 4.83 4.77 1345 5.032
Serving drinks on board 4.72 4.45 5.23 4.81 4.76 1331.5 5.018
Cleaning the vehicle / vessel / aircraft 5.44 5.68 5.57 5.62 5.61 8480 0.446
Airports and other transportation stations
Provision of information about departures/arrivals 5.93 5.86 5.82 5.97 5.95 7176 2.231
Provision of information about seat/berth availability 5.85 5.87 5.82 5.89 5.88 7498.5 0.674
Provision of information about ticket prices / fees 5.89 5.86 5.77 5.89 5.88 1146 1.568
Provision of information about special legal regulations, visa formalities, etc. 5.50 5.60 5.36 5.54 5.54 1569 0.363

Results
Activity Mean values
TI TE R O
Number of maximum mean values across the 4 groups 6 16 53 7
Number of minimum mean values across the 4 groups 16 35 16 15
Number of minimum and maximum mean values across the 4 groups 22 51 69 22
https://robots.nu/img/uploads/2018/09/03/Pepper%2
0robot,%20humanoid%20robot.jpg

Results
• Robot researchers and industry professionals are most optimistic toward the
use of robots in travel, tourism and hospitality
• Tourism and hospitality educators are most skeptical towards robots
• Nevertheless, the occupation of respondents only partially related to their
perceptions about the appropriateness of robot application in tourism

Results
• Exploratory factor analysis
Factors Number of items Cronbach’s Alpha Eigenvalue Variance explained
Factor 1: Information provision 15 0.962 34.263 43.926%
Factor 2: Housekeeping 14 0.949 4.854 6.223%
Factor 3: Food, beverages and guidance 11 0.962 3.867 4.958%
Factor 4: Robot autonomy 9 0.915 3.267 4.188%
Factor 5: Personal services 5 0.834 2.270 2.910%
Factor 6: Entertainment 5 0.896 1.758 2.254%
Factor 7: Bookings, payments and documentation 5 0.890 1.350 1.731%
Factor 8: First and last impression 2 0.814 1.151 1.475%
Total variance extracted 67.666%
Use intentions 4 0.932 3.320 83.006%

Results
• Regression analysis – Model 1
Dependent variable: Use intention
Independent variables
Model 1
Unstandardized Coefficients Standardized Coefficients t Collinearity Statistics
B Std. Error Beta Tolerance VIF
(Constant) 0.003 0.023 0.135
F1: Information provision 0.344 0.022 0.341 15.281*** 1.000 1.000
F2: Housekeeping 0.206 0.023 0.204 9.139*** 1.000 1.000
F3: Food, beverages and guidance 0.319 0.023 0.312 14.005*** 1.000 1.000
F4: Robot autonomy 0.303 0.023 0.298 13.357*** 1.000 1.000
F5: Personal services 0.230 0.023 0.227 10.192*** 1.000 1.000
F6: Entertainment 0.255 0.023 0.253 11.332*** 1.000 1.000
F7: Bookings, payments and
documentation
0.271 0.023 0.268 12.015*** 1.000 1.000
F8: First and last impression 0.192 0.023 0.189 8.494*** 1.000 1.000
Model summary characteristics
R 0.757 df 8
R2 0.573 N 867
Adjusted R2 0.569 F 144.336***
Standard error of the estimate 0.66541 Note: Levels of significance: *** p<0.001

Results
• Regression analysis – Model 2
Dependent variable: Use intention
Independent variables
Model 2
Unstandardized Coefficients Standardized Coefficients t Collinearity Statistics
B Std. Error Beta Tolerance VIF
(Constant) -1.280 0.127 -10.086***
F1: Information provision 0.234 0.021 0.232 10.906*** 0.880 1.136
F2: Housekeeping 0.142 0.021 0.140 6.820*** 0.936 1.069
F3: Food, beverages and guidance 0.220 0.022 0.215 10.063*** 0.868 1.151
F4: Robot autonomy 0.190 0.022 0.187 8.540*** 0.829 1.206
F5: Personal services 0.152 0.021 0.150 7.175*** 0.909 1.100
F6: Entertainment 0.185 0.021 0.183 8.917*** 0.939 1.065
F7: Bookings, payments and documentation 0.191 0.021 0.189 9.098*** 0.925 1.081
F8: First and last impression 0.102 0.021 0.100 4.816*** 0.912 1.096
Personal attitude towards robots in tourism 0.235 0.016 0.388 14.729*** 0.571 1.750
Gender 0.008 0.043 0.004 0.177 0.897 1.114
Age 0.003 0.002 0.034 1.526 0.797 1.255
Education 0.005 0.018 0.007 0.305 0.811 1.233
Tourism experience 0.005 0.008 0.012 0.562 0.907 1.103
Model summary characteristics
R 0.813 df 13
R2 0.661 N 867
Adjusted R2 0.656 F 128.001***
Standard error of the estimate 0.59506 Note: Levels of significance: *** p<0.001

Results
• Results of both models clearly show that the perceived appropriateness of
robot use is positively and significantly related to the intention to use
robots. Therefore, higher perceived appropriateness of robot use for
implementing various activities in a tourism setting is associated to higher
intentions to use the robots.
• On the other hand, the personal attitude toward service robots in tourism
is significantly and positively associated to robot use intentions, i.e.
respondents with more positive attitudes were more inclined to use robots.
• None of the demographic variables and the tourism experience seem to be
connected to robot use intentions.

Discussion

Discussion
• Respondents accept robots to be used for information processing activities
which computers and self-service kiosks already do and people are used to
them (e.g. information provision, or processing bookings, documents and
payments) or 3D (dirty, dull and dangerous) housekeeping tasks. These
same activities do not require much human involvement in their delivery by
a robot.
• At the same time, activities that require more complex human-robot
interaction and greater human involvement (like entertainment) or the
person is required to temporarily subordinate his/her body to a robot (e.g.
massages, babysitting or hairdressing) were not considered as acceptable.

Discussion
• The preliminary findings from the global study are similar to the
results reported by Ivanov, Webster and Garenko (2018) for young
Russian adults, and Ivanov, Webster and Seyyedi (2018) for Iranian
tourists

Conclusion

Conclusion
• The findings indicate the activities/tasks that customers perceive as
most appropriate for robotisation. These are the activities that would
face the least resistance from customers if /when travel, tourism and
hospitality companies decide to introduce robotic technologies in
their service operations.
• Moreover, the activities within the groups Information provision,
Housekeeping activities, Processing bookings, payments and
documents, and Food, beverages and guidance seem to be easiest to
automate because they would step on currently existing technologies.

Conclusion
• Not every activity than can be automated should be automated because
robotised service may not be accepted by customers.
• The low acceptance of robotisation for some activities (e.g. cooking food
in restaurants) would mean that robotised tourist companies may need to
have a very targeted marketing campaign to familiarise their potential
customers with the robotic technology and address customers’ concerns.
• Robot manufacturers need to focus robots that perform the activities that
tourists perceive as appropriate for robotisation as they would face least
resistance by tourists.

When travel, tourism and hospitality
companies adopt robots for activities
perceived as most appropriate for
robotisation, they will not need to fire
the robots later!
82

Conclusion
• Limitations:
• Although respondents are from 87 countries and territories, half of respondents
come from the home countries of the two co-authors. As these two papers are part
of an ongoing research project, data collection continued after the time of writing
of the two conference papers (September 2018) through different channels in order
to achieve a much larger and more diverse sample (especially in terms of country of
residence and education of respondents).
• The use intentions depend on many other factors besides the attitudes and
perceived appropriateness. The goal of the papers was not to provide explanation
about the factors that influence robot use intentions, but to assess the relationship
between perceived appropriateness and intentions to use robots. Therefore,
findings need to be viewed in this context only.

Conclusion
• Future research:
• Future research may use SEM and shed light to different aspects of people’s
attitudes towards robots – acceptance of robots (based on TAM, UTAUT2 or
other models), evaluation of people’s intentions to use service robots and
the factors that influence it, analysis of actual human-robot interaction.
• Research could be expanded into tourism supply and elaborate on managers’
readiness to use robots as compliments or substitutes of human employees,
the impact of robots on companies’ bottom line, their competitiveness,
operations, servicescape, human resource management and other aspects of
their activities.

Conclusion
• Future research:
• Research can also focus on the
national/cultural differences in people’s
attitudes towards robots in tourism.

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Thank You!
May the Robotic Force be with You!
Contact: stanislav.ivanov@vumk.eu, cwebster3@bsu.edu

Robots in Tourism – Preliminary Results from a Global Study

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