Customer-Facing Social Robots in the Grocery Store: Experiences

from a Field Trial

Niklas Eriksson

, Kristoffer Kuvaja Adolfsson

, Christa Tigerstedt

and Minna Stenius

Arcada University of Applied Sciences, Jan-Magnus Janssonin aukio 1, Helsinki, Finland

Keywords: Robotics, Retail, Grocery Shopping, Customer-Facing Technology, Human-Computer Interaction, Field Trial.

Abstract: Customer-facing technology provided by retailers is becoming increasingly common in retail stores. In this

study the focus is on customer-facing social robots (i.e. embodied robots that interact with humans) in a

grocery store. Based on workshops, learning via making and a customer survey (n=39) during a field trial,

this study explores potential roles for robots in a grocery store, how well the robots can perform the roles

assigned to them, and customers’ perception of the robots. Seven main roles that social robots could take on

in a grocery store were identified: store guide, sales promoter, shopping assistant, entertainer, store chef,

product supervisor, and experience evaluator. The two robots that were field trialled performed their tasks

reasonably well. The results from the customer survey confirm previous research that customers perceive

social robots primarily positively. This study, however, also indicates that a notable share of the customers

may find social robots unpleasant in a store setting. Limitations and further research are also discussed.

1 INTRODUCTION

A key trend in the retail sector is the digitalization of

the retail store. Not only are customers bringing their

own technology, such as smartphones, to stores, but

retailers are increasingly providing their customers

with in-store technology. Consumer-facing

technology plays an important part in contemporary

in-store customer experiences (Berg et al., 2024;

Fagerstrøm et al., 2020). Technologies such as digital

displays, self-checkout solutions and self-service

kiosks are increasingly prevalent in many retail

stores. Also, robotics and artificial intelligence (AI)

are becoming steadily more common in a store

context (Go et al., 2019). The presence of social

robots (i.e. embodied robots that interact with

humans) in stores and shopping malls is a further

noticeable aspect of this phenomenon. Customers

tend to feel positive about social robots, but the long-

term benefits and impact of them are difficult to

estimate, especially from a service and business

perspective (Niemelä et al., 2019; Tigerstedt et al.,

2023). Nevertheless, individuals may also perceive

https://orcid.org/0000-0001-9295-7114

https://orcid.org/0009-0001-8234-627X

https://orcid.org/0000-0003-1340-0217

https://orcid.org/0000-0003-2712-759X

robots negatively, potentially leading to job loss,

dehumanization, privacy intrusions, and improper

functions that lead to poorer in-store experiences

(Fuste-Forne, 2022; Song & Kim, 2022). Therefore,

it is important to identify key functions that are

beneficial for the customer and to determine how

robots can effectively contribute (Lu et al., 2020;

Song & Kim, 2022).

In this study the focus is on customer-facing

social robots in a grocery store. “The robot in the

grocery store” -project took place in the spring of

2024 in Helsinki, Finland, with the aim to: (1) explore

potential roles of customer-facing social robots in

grocery stores, (2) conduct a field trial with two social

robots in a grocery store in central Helsinki for one

week (to find out how they interact with customers

from feasibility perspective, i.e. whether they can be

used successfully for the roles given to them), and (3)

investigate store customers' perceptions and

intentions when encountering social robots in a real-

life grocery store setting.

The article is structured as follows. First, a

literature review of previous studies with social

Eriksson, N., Adolfsson, K. K., Tigerstedt, C. and Stenius, M.

Customer-Facing Social Robots in the Grocery Store: Experiences from a Field Trial.

DOI: 10.5220/0013164300003929

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 27th International Conference on Enterprise Information Systems (ICEIS 2025) - Volume 2, pages 509-516

ISBN: 978-989-758-749-8; ISSN: 2184-4992

509

robots in retail is presented, whereafter the methods

are described. Then the results from the field trial are

presented and discussed. Finally, further research

ideas are proposed, followed by conclusions.

2 LITERATURE REVIEW

Niemelä et al. (2019) identified six quite different

tasks or roles for a shopping mall social robot:

information and guidance, entertainment and

attraction, advertising and sales, shopping

companion, shopping assistant, and robot for

children. In their study, De Gauquier et al. (2018)

detected the following themes or roles for retail

robots: welcoming, informing, assisting, entertaining,

and advertising. The study further suggests that more

than a third of social robot tasks are in informing (e.g.

presenting product info and guiding customers)

followed by advertising. The entertaining role, such

as making the visit pleasant and positive, is also quite

common (Hägglund et al., 2023; Iwamoto et al.,

2022). Using social robots to inform, guide, or

advertise can also mean that they can be used to lead

the customer to smarter, healthier, and ecologically

sustainable decisions (Warringa, 2021). Overall,

social robots can help with marketing of products

(Iwamoto et al., 2020), and they have also been used

in supermarkets to assist customers to shop for food

(Iwamura et al., 2011; Thompson et al. 2018).

Customer-facing retail social robots have been

researched in a field setting in different ways. Some

studies have compared “customer-human employee”

interaction with “customer-robot employee”

interaction (e.g. De Gauquier et al., 2023; Roosen et

al., 2022). De Gaugier et al. (2023) found that social

robots can draw more customer attention than human

employees, but the human employee creates more

customer visits to the store. Roosen et al. (2022)

found no difference in the perceived service quality

when interacting with social robots or humans with

customers who had positive attitudes towards social

robots. Others have explored other aspects of

customer experiences with social robots (e.g.

Edirisinghe et al., 2023; Ferber & Vaziri, 2024;

Golchinfar et al., 2022; Subero-Navarro et al., 2022),

and some have investigated customer adoption of

social robots (e.g. Niemelä et al., 2019; Meyer et al.,

2020). Niemelä et al. (2019) concluded that a

shopping mall social robot must be perceived as both

entertaining and useful to be adopted by mall

customers. Overall, they found that customers had

positive attitudes towards social robots. Similarly,

Ferber and Vaziri (2024) also found that customers of

a shopping mall perceived social robots positively,

and especially the likability and perceived

intelligence of a social robot received good ratings.

Thompson et al. (2024) also found that participants in

their study perceive social robots in a grocery store as

easy to learn to use, helpful, and enjoyable. On the

contrary, Söderlund (2022) suggested that social

robots in a service context can also be perceived as

creepy, similarly to Grazzini et al. (2023), who argued

that social robots can generate discomfort among

customers because of their machine-like appearance.

Thus, we need to gain a better understanding of how

social robots are perceived by customers in different

contexts, such as a supermarket.

Overall, these studies, by using both quantitative

and qualitative methods, have contributed to a better

understanding of how to design and utilize social

robots, and how they can potentially contribute in a

retail setting. See also Table 1. for a summary of some

recent field studies with customer-facing social

robots in retail. Yet, to our best knowledge, few field

studies with social robots have specifically focused

on the grocery store or supermarket context.

3 METHODS

3.1 Study Design Development

To plan and design the field trial, we arranged two

project workshops for the core project team

consisting of a technical expert in social robots, a

researcher in retail and digital commerce, a researcher

in service design and social robots, and the store

owner of the supermarket (where the trial took place).

In these workshops the different roles of robots were

discussed and evaluated. To illustrate some of the

roles, two robots were used to create different real-

life scenarios in a physical project room. These were

evaluated within the group. Previous literature and

similar robot studies were also discussed during the

sessions for inspiration. Both sessions were

documented by two participating researchers (e.g. as

written notes and video footage of the proposed

roles). The development process was also iterative,

which meant that in addition to the organized

workshops, more informal sessions were arranged by

the team to iterate and develop the ideas into

prototypes, i.e. learning via making (Oates, 2006).

Three of the generated ideas developed into real roles

for the robots, in the field trial. A description of the

roles, together with some experiences from designing

them, is presented in the results section.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

510

Table 1: Some recent field studies with customer-facing social robots (SR) in retail.

Stud

Main investi

ation Data collection Ke

results

De Gauquier et

al. (2023)

Comparison between frontline

human employee and a SR

employee to investigate

attention and conversion rates

of customer store visits.

Observations at a

chocolate store

The SR drew more attention, but the

human employee created more store visits

by customers.

De Gauquier et

al. (2021)

Placement (inside or outside

store) of an entertaining SR.

Observations at a

chocolate store

When the goal is to create awareness

towards the store then the SR should be

placed outside. But to induce customers to

enter the store the robot should be placed

inside.

Edirisinghe et

al. (2023)

Customer perception of SR

advising to wear mask due to

Covid-19 (if not wearing)

when enterin

a store.

Observations and

interviews at a

clothing and sporting

ment store

The customers perceived SR to be

friendly, polite, and fun. They would use

such a SR in the future.

Ferber & Vaziri

(2024)

Customer user experience and

satisfaction with SR. Impact

of Age.

Quantitative survey at

a shopping mall

Consumers evaluated their SR-interaction

experience positively. Especially

likeability and perceived intelligence

were rated well. Age had no impact.

Golchinfar et al.

(2022)

Customer experiences of SR

and its effect on customers’

intention to use SR.

Quantitative survey

and interviews at a

sho

mall.

Hedonic quality contributes to predict

customers’ intention to use SR.

Heikkilä et al.

(2019)

Characteristics of SR-

guidance of shopping mall

customers.

Interviews at a

shopping mall.

Nine implications for guiding with SR,

e.g. use gestures and short instructions for

effectivel

uidin

customers.

Meyer et al.

(2020)

Customer adoption of SR in a

retail setting

Interview and survey

in a retail setting

Five drivers and barriers for customer SR

adoption: Functional capabilities, Role

Congruency, Discouragement, Social

Presence and Ph

sical A

earance.

Niemelä et al.

(2019)

Customer adoption of a

shopping mall SR

Quantitative survey

and interviews at a

sho

mall.

Customers perceived SR positively, but

SR needs to be both entertaining and

useful to be ado

ted.

Roosen et al.

(2022)

Customer perception of

service quality satisfaction

between human-human and

human-SR interaction

Quantitative survey at

a shoe store

Customer perceived service quality of

SR-interaction and human interaction did

not differ for customers with relatively

ositive attitudes towards SR

Subero-Navarro

et al. (2022)

Customer intention to use SR

in retail stores

Quantitative survey at

a retail store

Pleasure is the main driver for intended

use of SR.

Thompson et al.

(2018)

Customer experience of SR Quantitative survey

and open-ended

question at grocery

store

Customers perceived SR easy to learn,

helpful and enjoyable, and participants

would use SR in the future.

3.2 Data Collection Through Customer

Survey

During our uncontrolled field trial, we collected

survey data from customers in the grocery store.

Uncontrolled field trials, resembling case studies

more than controlled experiments, are typical when

introducing new types of technology (Oates, 2006).

Two researchers conducted the survey by

interviewing customers who visited the store using a

predefined questionnaire consisting of an open-ended

question and sixteen closed-ended questions (not all

are analyzed for the purpose of this study). Customers

were informed about the study through signs in the

store. Participation was voluntary with no

compensation offered. In total, the two researchers

interviewed 39 respondents, 11 men and 28 women;

17 in the age bracket 18-35 years, 9 in the bracket 36-

55 years, and 13 were older than 55 years. Twelve

respondents had interacted with social robots before,

but only two in a grocery store.

In the survey, the respondents were asked an

open-ended question: “Can you suggest what role a

social robot could play in a grocery store?” In

addition, in line with the Technology Acceptance

Model (TAM) (Davis 1989), we asked them to

Customer-Facing Social Robots in the Grocery Store: Experiences from a Field Trial

511

evaluate the perceived ease of interacting with the

robots, the perceived usability of the robots, and their

intention to interact with social robots in grocery

stores in the future, if this was possible. Finally, we

also listed some positive and negative words

associated with social robots to the respondents:

"Sympathetic", "Easy to approach", "Unpleasant" and

"Machine-like" and asked them to evaluate their

associations with the robots using a Likert scale from

1 to 5, with 1 meaning strongly disagree and 5

strongly agree. Due to the busy store environment,

not all respondents had the time, or wanted to, answer

all survey questions.

3.3 Data Analysis

The material generated from the workshops was

analysed by all team members to summarize the

generated ideas and create an overview of potential

robot roles. A content-analysis of the open-ended

survey question was conducted by one project-team

member and verified separately by another team

member. Frequency distribution was used to analyse

the closed survey questions.

4 RESULTS

4.1 Identified Roles

Based on the material generated in the project team's

workshops, learning via making and a content

analysis of the open-ended question in the survey

with customers, we identified the following potential

roles for a social robot in a grocery store: store guide,

sales promoter, shopping assistant, entertainer, store

chef, product supervisor, and experience evaluator.

See Table 2 for a description of the seven roles.

Some of the respondents suggested several roles

for the robots while some could not suggest any roles.

In total, we received 39 proposals. Social robot as the

store guide was mentioned 12 times. Typical answers

were "Guides you in finding products" and "Good

help if you go to a new store and it is difficult to find

certain products". The role of a sales promoter was

mentioned 13 times. Typical suggestions were

"Introduce new products, hand out samples", "Show

the best deals of the day" and "Surprise offers". The

role as a shopping assistant was mentioned 9 times,

with typical expressions such as "That [the robot] can

even take your groceries to the car" and "That [the

robot] can show product information, for example

related to allergies". The role of entertainer generated

5 suggestions, such as "Light up your day when the

robot wishes you a good day" and "This [robot] could

cheer us customers up". The roles of store chef,

product supervisor and experience evaluator were

identified in the project team's workshops and were

not mentioned by customers.

4.2 Description and Feasibility of the

Three Developed Roles

For the field trial, three roles were developed with

two social robots, Alf and Amy: 1. Guide to the

week's offers, 2. Promoter of a secret offer and 3.

Tasting waiter. See Figure 1 and a description below.

Table 2: Roles of customer-facing social robots in grocery stores.

Role categories Description

Store guide

Product finder with interactive maps, motions, and voice for directions. Also "follow

me" functionality to guide the customer to, for example, products in the store.

Sales promoter

Promotion of new products by conducting tastings. Recommend and promote

products. Hand out coupons. Promote the store outside the store entrance.

Shopping assistant

Helping customers plan their meals, provide recipes, nutrition tips, sustainability-

related information, etc. Cashier or help with check-out. Help customers carry their

roducts within the store but also from the store.

Entertainer

Greet incoming and outgoing customers. Dancing, singing and telling stories to

entertain customers.

Store chef

Provide customers with customized meals and drinks according to their preferences.

For example, mixing a salad or a coffee for take-away.

Product supervisor

Monitor age-restricted products such as alcohol and assist with theft monitoring. For

example, provide friendly reminders to young customers of age restrictions.

Experience evaluator Collect responses from customers such as product feedback and store feedback.

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

512

1. Guide to the week’s offers 2. Promoter of a secret offe

3. Tastin

waite

Figure 1: The three robot roles in the field trial. Photos by K. Kuvaaja & C. Tigerstedt. Used with permission.

Robot Alf, from Sanbot Innovation's fairy series,

and robot Amy, originally from CSJBOT but now

Suzhou Pangolin Robot Corporation, are both

proprietary Android-based platforms from Asia. Alf

is an interactive service robot, and Amy is specifically

designed as a waiter robot. In this project, we used

their primary features, but we also adapted them to

the grocery store environment.

4.2.1 Guide to the Week’s Offers

Alf was placed at the entrance of the store. The robot

greeted customers by waving its arm, changing its

facial expression, and verbally introducing itself,

asking customers to interact with it to see the week’s

offers (such as a vegetarian choice). If customers

interacted, different offers were shown on the screen

for the customer to choose from. When an offer was

chosen, the robot expressed a star-struck facial

expression while presenting the price visually and

verbally. The customer was also provided with the

opportunity to see the location of the product in the

store via Alf’s screen map.

4.2.2 Promoter of a Secret Offer

Amy was waiting for customers at a designated point

in the store. The location was chosen based on that

the robot was easily accessible and visible to the

customers, there was enough space to interact, and the

distance to the secret offer (about ten meters) was

appropriate. Customers were asked to interact with

the robot to discover a secret offer. When the

customer activated the robot via the touchscreen

interface, the robot verbally asked them to follow.

The robot guided them to the secret product offer via

a predetermined route. Once at its destination, the

robot presented the offer both verbally and visually to

the customer before going back to its designated point

to wait for the next customer interaction.

4.2.3 Tasting Waiter

Amy’s second role during the week was to act as a

tasting waiter. Amy patrolled between four different

points on a long, open stretch in the middle of the

store with product samples on its tray, accompanied

by the store's music and the offer displayed on the

screen. The robot stopped when a customer got in its

way, and if so, the robot verbally prompted the

customer to taste a sample.

All three roles were mainly within the category of

sales promotion, but also features of a store guide and

an entertainer were included. Some technical

problems and malfunctioning requiring some

maintenance were encountered during the week, but

overall, the roles were performed well. However, the

roles can be further developed with additional

features and some deployed functions can be further

improved. Alf and Amy's basic design features also

prevented us from developing certain elements of

customer interaction, such as speech recognition.

Customer-Facing Social Robots in the Grocery Store: Experiences from a Field Trial

513

Table 3: Perceptions of social robots and future intentions.

Variables Stron

disa

ree Disa

ree In-

etween A

ree Stron

ree

Ease of Use

(

n=30

)

0% 3.3% 3.3% 13.3% 80.0%

Usefulness

(

n=29

)

20.7% 0.0% 17.2% 17.2% 44.8%

Intention to use (n=33) 6.5% 1.1% 8.7% 12.0% 71.7%

Table 4: Positive and negative associations with social robots.

Variables Strongly disagree Disagree In-

etween Agree Strongly agree

athetic

(

n=17

)

11.8% 0.0% 0.0% 0.0% 88.2%

Eas

to a

roach

(

n=18

)

0.0% 5.6% 0.0% 11.1% 83.4%

leasant

(

n=17

)

47.1% 11.8% 17.6% 5.9% 17.6%

Machine-like (n=18) 5.6% 0.0% 16.7% 38.9% 38.9%

4.3 Customer Perceptions of Social

Robots and Future Intentions

Of the respondents, 93.3% felt that the robots were

easy to use (see Table 3). Furthermore, 62% of the

respondents perceived the robots as useful. The

perceptions were, however, quite divided as 21% did

not find them useful at all. Most of the respondents

(72%) strongly agreed with the statement that they

intend to interact with social robots in grocery stores

in the future, whereas only 7.7% had no such

intentions.

More than 80% of the respondents found the

social robots sympathetic and easily approachable,

whereas l2% did not find them sympathetic at all.

More than half of the respondents did not consider the

robots unpleasant as opposed to some 18%, who

strongly did. Most respondents found the robots

machine-like. See Table 4.

5 DISCUSSION AND

CONCLUSIONS

The first aim of this study was to identify possible

roles for social robots in grocery stores. Based on the

findings and in line with prior research, we argue that

a range of different roles can be given to and met by

customer-facing social robots in a grocery store. In

this study, seven main roles were identified: Store

guide, Sales promoter, Shopping assistant, Product

supervisor, Entertainer, Store chef and Experience

evaluator. Similar roles were identified by, for

example, Niemelä et al. (2019) for social robots in a

shopping mall. Here, however, the focus was

specifically on the grocery store context, and thus the

identified roles (Table 2) contribute to the literature

on how social robots can be used in a supermarket to

support customer service and experience.

Furthermore, the identified roles constitute a

reasonable starting point for grocery retailers who are

contemplating using social robots and evaluating

different options.

The second aim of this study was to place two

social robots in a grocery store for one week to

establish, from a feasibility standpoint, how social

robots interact in specific roles with customers in a

grocery store. They were given three tasks: to

promote a secret offer; to guide to offers of the week,

and act as a Tasting waiter. While they were all

primarily sales promoters, their roles also included

features of a Store Guide and an Entertainer. These

three roles provide concrete examples of how social

robots can be deployed in a grocery store setting.

Amy and Alf performed the tasks well, although we

did experience some technical difficulties and

malfunctioning during the week. Also, the basic

design features of Amy and Alf delimited the

implementation to certain features. It would therefore

be advisable to deploy social robots that can take on

several of the roles described (see Table 2). The initial

investment costs and maintenance costs for retailers

to use robots in stores can also be reduced if the robot

can flexibly and efficiently perform multiple roles or

tasks, rather than just one or a few specific ones

(which is quite common with social robots today).

The third aim of the study was to shed light on

how customers perceive social robots in a grocery

store, and whether customers intend to interact with

or use them in the future. Most respondents were

quite positive about interacting with the robots, and

given the opportunity, they would interact with social

robots in a grocery store in the future. Amy and Alf

were also considered easy to interact with and mainly

useful. However, the perceptions of usability were

quite divided, further highlighting the need to assign

suitable roles for social robots, and identify and

improve the design and features that add value to

customers. Most of the respondents found the robots

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

514

to be sympathetic and easy to approach, in line with

previous research (e.g. Edirisinghe et al., 2023;

Niemelä et al., 2019; Tigerstedt et al., 2023;

Thompson et al., 2018). However, a rather

considerable share (18%) found them unpleasant,

which is something retailers must take very seriously.

Creating unpleasant experiences or discomfort for

some customers can be a risky solution for a retailer,

even if most customers perceive them positively, and

even if the robots could replace human employees for

certain roles in the store. Also, a majority agreed that

robots are machine-like, which can impact the

customer experience negatively. Grazzini et al.

(2023) suggest that social robots should be developed

to become more human-like by adding features that

convey "warmth" to their appearance. Similar

findings were suggested by Söderlund (2022), that

human-like attributes affect perceived service quality

of social robots positively.

5.1 Limitations and Future Research

This study was an exploratory study to better

understand the potential roles of customer-facing

social robots and customer perceptions of them in a

supermarket. The study is limited to one field trial in

one grocery store, and an obvious further limitation is

the small customer sample. In addition, the novelty of

social robots in grocery stores can naturally affect

customers' perception of them and their future

intended use. Additionally, those who choose to

voluntarily try out the robots and participate in an

interview about robots may be the more tech-savvy

customers, which can result in an overly positive

image of robots (Niemelä et al., 2019). Thus, further

research could implement similar field trials with a

larger sample of respondents, including also other

types of robot roles. Future research could also use

stricter experimental designs, such as controlled

before-and-after studies, to identify more specific

effects of social robots in grocery stores.

5.2 Final Conclusions

Quite little research has been conducted to understand

how social robots can be used in a supermarket

context. This study identified seven main roles that

social robots could take on in a grocery store when

facing customers. In addition, we field-trialled two

robots and three roles to provide concrete examples

of how social robots can be used in grocery stores,

and were able to demonstrate that our robots

interacted with customers in the intended manner and

performed their tasks reasonably well. Finally, the

results confirmed previous research that customers

perceive social robots primarily positively. This

study, however, indicates that a considerable share (in

this study 18%) of the customers may find social

robots unpleasant. To establish how large this group

in fact is, and how to tackle their negative

perceptions, is very important for grocery retailers to

understand.

REFERENCES

Berg, H., Nilsson, E., & Liljedal, K. T. (2024). Consumer-

facing technology in retailing: how technology shapes

customer experience in physical and digital stores. The

International Review of Retail, Distribution and

Consumer Research, 34(2), 123–127. https://doi.org/

10.1080/09593969.2024.2344152

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease

of Use, and User Acceptance of Information

Technology. MIS Quarterly, 13(3), 319-340.

https://doi.org/10.2307/249008

de Gauquier, L., Brengman, M., & Willems, K. (2020). The

Rise of Service Robots in Retailing: Literature Review

on Success Factors and Pitfalls. In Retail Futures (pp.

15–35). Emerald Publishing Limited. https://doi.org/

10.1108/978-1-83867-663-620201007

de Gauquier, L., Brengman, M., Willems, K., Cao, H. L., &

Vanderborght, B. (2021). In or out? A field

observational study on the placement of entertaining

robots in retailing. International Journal of Retail and

Distribution Management, 49(7), 846–874.

https://doi.org/10.1108/IJRDM-10-2020-0413

de Gauquier, L., Willems, K., Cao, H. L., Vanderborght, B.,

& Brengman, M. (2023). Together or alone: Should

service robots and frontline employees collaborate in

retail-customer interactions at the POS? Journal of

Retailing and Consumer Services, 70. https://doi.org/

10.1016/j.jretconser.2022.103176

Edirisinghe, S., Satake, S., & Kanda, T. (2023). Field Trial

of a Shopworker Robot with Friendly Guidance and

Appropriate Admonishments. ACM Transactions on

Human-Robot Interaction, 12(3). https://doi.org/

10.1145/3575805

Fagerstrøm, A., Eriksson, N. & Sigurdsson, V. (2020).

Investigating the impact of Internet of Things services

from a smartphone app on grocery shopping. Journal of

Retailing and Consumer Services, 52, 101927.

https://doi.org/10.1016/j.jretconser.2019.101927

Ferber, T., & Bonn-Rhein-Sieg, H. (2024). Investigating

User Experience of HRI in the Context of a Realistic

Retail Scenario-The Influence of Consumer Age on the

Evaluation of a Humanoid Service Robot. Journal of

Consumer Satisfaction, Dissatisfaction and

Complaining Behavior, 37(1).

Fuste-Forne, F. (2021). Robot chefs in gastronomy tourism:

What’s on the menu? Tourism Management

Customer-Facing Social Robots in the Grocery Store: Experiences from a Field Trial

515

Perspectives, 37(6), 100774. https://doi.org/10.1016/

j.tmp.2020.100774

Go, H., Kang, M. & Suh, S. C. (2021). Machine learning of

robots in tourism and hospitality: interactive

technology acceptance model (iTAM) – cutting edge.

Tourism Review, 75(4), 625–636. https://doi.org/

10.1108/TR-02-2019-0062

Golchinfar, D., Vaziri, D. D., Stevens, G., & Schreiber, D.

(2022). Let’s Go to the Mall: Investigating the Role of

User Experience in Customers’ Intention to Use Social

Robots in a Shopping Mall. Proceedings of the 2022

ACM Designing Interactive Systems Conference:

Digital Wellbeing, 377 – 386. https://doi.org/

10.1145/3532106.3533490

Grazzini, L., Viglia, G., & Nunan, D. (2023). Dashed

expectations in service experiences. Effects of robots

human-likeness on customers’ responses. European

Journal of Marketing, 57(4), 957–986. https://doi.org/

10.1108/EJM-03-2021-0220

Heikkilä, P., Lammi, H., Niemelä, M., Belhassein, K.,

Sarthou, G., Tammela, A., Clodic, A., & Alami, R.

(2019.). Should a robot guide like a human? A

qualitative four-phase study of a shopping mall robot.

Proceedings of the Eleventh International Conference

on Social Robotics. https://laas.hal.science/hal-

02285035

Meyer, P., Jonas, J. M., Roth, A., Meyer, P., Jonas, J. M.,

& Angela, R. (2020.). Exploring customers’ acceptance

of and resistance to service robots in stationary retail -

a mixed method approach. Proceedings of the 28th

European Conference on Information Systems (ECIS).

https://aisel.aisnet.org/ ecis2020_rp/9

Hägglund, S., Tigerstedt, C., Biström, D., Wingren, M.,

Andersson S., Kuvaja Adolfsson, K., Penttinen, J. &

Espinosa-Leal, L. (2023). Stakeholders Experiences of

and Expectations for Robot Accents in a Dental Care

Simulation: A Finland-Swedish Case Study.

Proceedings of Social Robots in Social Institutions,

125–134. https://doi.org/10.3233/FAIA220611

Iwamoto, T., Baba., Nakanishi, J., HK, Yoshikawa, Y. &

Ishiguro, H. (2022). Playful recommendations: Sales

promotion that robots stimulate pleasant feelings

instead of product explanation. IEEE Robotic and

automation letters, 7(4), 11815-11822.

Iwamura, Y., Shiomi, M., Kanda, T., Ishiguro, H., &

Hagita, N. (2011). Do elderly people prefer a

conversational humanoid as a shopping assistant

partner in supermarkets? Proceedings of the 6th

ACM/IEEE International Conference on Human-Robot

Interaction, 449–457. https://doi.org/10.1145/1957656.

1957816

Lu, V.N., Wirtz, J., Kunz, W.H., Paluch S, Gruber, T.,

Martins, A. & Patterson P.G. (2020). Impact of robots

on customers and service employees. Journal of Service

Theory and Practice, 30(3). http://doi.org/10.1108/

JSTP-04-2019-0088

Niemelä, M., Heikkilä, P., Lammi, H. & Oksman, V.

(2019). A Social Robot in a Shopping Mall: Studies on

Acceptance and Stakeholder Expectations. In O. Korn

(Eds.), Social Robots: Technological, Societal and

Ethical Aspects of Human-Robot Interaction, Human–

Computer Interaction Series. https://doi.org/

10.1007/978-3-030-17107-0_7

Oates, B. (2006). Researching Information Systems and

Computing. SAGE Publications Inc.

Roozen, I., Raedts, M., & Yanycheva, A. (2023). Are Retail

Customers Ready for Service Robot Assistants?

International Journal of Social Robotics, 15(1), 15–25.

https://doi.org/10.1007/s12369-022-00949-z

Song, C. S. & Kim, Y-K. (2022). The role of the human-

robot interaction in consumers’ acceptance of

humanoid retail service robots. Journal of Business

Research, 146, 489-503. https://doi.org/10.1016/

j.jbusres.2022.03.087

Subero-Navarro, Á., Pelegrín-Borondo, J., Reinares-Lara,

E., & Olarte-Pascual, C. (2022). Proposal for modeling

social robot acceptance by retail customers: CAN

model + technophobia. Journal of Retailing and

Consumer Services, 64. https://doi.org/10.1016/

j.jretconser.2021.102813

Söderlund, M. (2022). Service robots with (perceived)

theory of mind: An examination of humans’ reactions.

Journal of Retailing and Consumer Services, 67.

https://doi.org/10.1016/j.jretconser.2022.102999

Tigerstedt, C. & Fabricius, S. (2023). Humanoid robots in

service contexts – insights from sessions with end users.

Proceedings of the 16th annual International

Conference of Education, Research and Innovation,

1413-1420. https://doi.org/10.21125/iceri.2023.0456

Thompson, C., Khan, H., Dworakowski, D., Harrigan, K.,

& Nejat, G. (2018). An Autonomous Shopping

Assistance Robot for Grocery Stores. Proceedings of

IEEE/RSJ International Conference on Intelligent

Robots and Systems, Madrid, Spain.

Warringa, T. (2021). Using Social Robots to Persuade

Consumers to Sustainable Products [Doctoral

dissertation, Tilburg University].

ICEIS 2025 - 27th International Conference on Enterprise Information Systems

516