Who Wants to Use an Augmented Reality Shopping Assistant

Application?

Daniel Mora

, Robert Zimmermann

, Douglas Cirqueira

3 a

, Marija Bezbradica

3 b

Markus Helfert

4 c

, Andreas Auinger

2 d

and Dirk Werth

Artiﬁcial Intelligence Lab, AWS Institute for Digitized Products and Processes, Saarbr

ucken, Germany

Digital Business Management, University of Applied Sciences Upper Austria, Styer, Austria

School of Computing, Dublin City University, Dublin, Ireland

Innovation Value Institute, Maynooth University, Maynooth, Ireland

Keywords:

Digital Shopping Assistant, Recommender Systems, Explainable Artiﬁcial Intelligence, Retail Sales, Digital

Retail, Brick-and-Mortar.

Abstract:

Brick-and-mortar retailers need to stay competitive to the convenience provided by online channels. Tech-

nologies, such as personalized shopping assistants on smartphones can empower customers in-store towards a

similar experience as in an online scenario. For instance, an augmented reality shopping assistance application

with explainable recommendations (XARSAA) can mimic the behavior of recommender systems in personal-

izing offers to consumers in physical shops. However, before deploying such technologies, it is essential that

retailers get to know the demographics of their customer base. Existing literature rarely addresses the inﬂuence

of customers demographics towards XARSAA technologies. Therefore, we follow a design science approach,

and develop an instantiation of a XARSAA artifact, which is artiﬁcially evaluated through a controlled online

user experiment with 315 participants. Results illustrate multiple demographics which inﬂuence customers

attitude towards an augmented reality shopping assistant application in brick-and-mortar stores. Additionally,

we provide insights into the design of such technology to guide researchers in its implementation.

1 INTRODUCTION

Brick-and-mortar businesses are currently struggling.

For example, emblematic companies such as J. Crew,

GNC, and Brook Brothers, went bankrupt, leading

some researchers to describe the situation as the ”re-

tail apocalypse”. Still, physical stores certainly pro-

vide value to customers on their shopping journey,

taking a crucial role in the product information search

point (Pimenidis et al., 2019); To counter the ”retail

apocalypse,” the sector and literature propose tradi-

tional retailers to transition into omnichannel retail.

This retail model leverages technologies (e.g., rec-

ommender systems, explainable artiﬁcial intelligence,

augmented reality, and smart devices) in order to cre-

ate digital services around the customer experience

https://orcid.org/0000-0002-1283-0453

https://orcid.org/0000-0001-9366-5113

https://orcid.org/0000-0001-6546-6408

https://orcid.org/0000-0002-2672-0896

(Lemon and Verhoef, 2016).

One manifestation of this is a personalized digi-

tal assistant that boosts the customer journey (Parise

et al., 2016) as it allows retailers to suggest tailored

options that can positively stimulate the customer. For

instance, digital shopping assistants, levering explain-

able recommendations, are well regarded to enhance

sales and proﬁt online (Cirqueira et al., 2019a), as

they provide customers with personalized offers and

reasons, which clarify and improve their decision-

making toward purchases (Zimmermann et al., 2019),

leading to higher satisfaction and retention (Gao et al.,

2019).

However, research exploring the impact of

explainable recommendations in brick-and-mortar

stores it is still scarce, which is why retail managers

and practitioners lack guidance on how to implement

such technology into their customers’ customer jour-

ney in the most effective way.

An essential requirement for such technology

would be to identify a typical user proﬁle of cus-

Mora, D., Zimmermann, R., Cirqueira, D., Bezbradica, M., Helfert, M., Auinger, A. and Werth, D.

Who Wants to Use an Augmented Reality Shopping Assistant Application?.

DOI: 10.5220/0010214503090318

In Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2020), pages 309-318

ISBN: 978-989-758-480-0

309

tomers who want to use explainable recommendations

in brick-and-mortar stores (Peker et al., 2017). Addi-

tionally, retailers usually assess their customer groups

based on demographics (Antony et al., 2018). Such

analysis is essential as it inﬂuences the types of prod-

ucts, offers, and bundles which a retailer can plan

(Wetzlinger et al., 2017). Furthermore, when im-

plementing novel technologies in-store, it is funda-

mental to assess the potential acceptance of different

customer proﬁles for such technologies (Ren et al.,

2018).

Hence, this study investigates how customers’

demographics inﬂuence the perception of an aug-

mented reality shopping assistance application with

explainable recommendations (XARSAA). Conse-

quently, we address the following research question:

I: Which customer demographic inﬂuences the cus-

tomer perception of an XARSAA? II: Which demo-

graphics does the target audience of an XARSAA

have?

We tackle the research questions following a de-

sign science approach, mainly focused on the stages

of problem and objectives identiﬁcation, development

and artiﬁcial evaluation. We instantiate an XARSAA

application and assess the feasibility of such an in-

stantiation in increasing perceived usefulness, infor-

mativeness, irritation, purchase intention, and trust in

the technology in-store, based on the model of (Haus-

man and Siekpe, 2009), and (Hoffman et al., 2018).

The instantiation is evaluated following an artiﬁcial

evaluation approach, through a controlled online user

experiment with 315 participants.

The paper is structured as follows: Section 2

presents related work. Section 3 illustrates the re-

search methodology and details on the study develop-

ment. Section 4 provides the study results. Section 5

discusses results and main ﬁndings, followed by sec-

tion 6 which focus on the current limitations of the

study, as well as future work. Closing, the section 7

concludes the paper.

2 RELATED WORK

We review the state-of-the-art trends in the retail do-

main, as well as previous approaches in deploying

recommender systems in physical stores as the un-

derline mechanism to provide personalized shopping

assistance. This allows us to identify limitations in

the current corpus and draw motivation for our study,

as the investigation of the feasibility of an XARSAA

prototype in-store, also integrating Explainable Arti-

ﬁcial Intelligence (XAI) methods.

2.1 Digital Retail

The concept of the digitization of retail has become

quiet the trend starting with e-commerce and internet-

based companies, such as Amazon.com, Otto.com,

and many others. It expanded into different channels

and customer touchpoints such as smartphones and

social media.

Consequently, retailers rapidly began to target

customers across different channels, giving birth to

what is now known as the multi-channel retail ap-

proach. In turn, this is slowly morphing into om-

nichannel retail, which integrates all channels and

touchpoints into a single seamless customer expe-

rience. In parallel, companies have started adopt-

ing customer experience at the center of the business

model, and digital technologies are deployed to en-

hance that experience (Parise et al., 2016), (Rigby,

2011).

Accordingly, McKinsey’s Consulting afﬁrms that

with omnichannel retailing, retailers can do person-

alized advertising and promotion via devices, such as

the increasingly ubiquitous devices (MacKenzie et al.,

2013); smartphones as the archetype of this devices

(Pimenidis et al., 2019), which can also be enhanced

by technologies that are prognostic to revolutionary

the retail sector, such as, Augmented Reality (von

Briel, 2018).

In (Parise et al., 2016), the authors acknowledge

the problem of meeting customers’ expectations in

brick-and-mortar and evaluate how digital technolo-

gies can aid in the improvement of customer expe-

rience and in the transition to omnichannel retail.

The authors consider in-store touchpoints essential,

and identify digital shopping assistance as one of the

key solutions to battle brick-and-mortar challenges to

meet customers’ expectations. The digital shopping

assistant provides a more holistic experience for the

customers in the physical stores, boosting utilitarian

value through efﬁciency in information search and

product comparison, as well as hedonic value creating

a more immersive experience leveraging technologies

such as augmented reality, thus, stimulating the cus-

tomer perception of fun, pleasure, and enjoyability

(Juaneda-Ayensa et al., 2016) – crucial factors inﬂu-

encing customers’ shopping experience.

2.2 Explainable Recommendations

Recommender engines have helped online commerce

in the past decades, providing customers with a more

personalized experience, which has led to a high-

impact on retails sales and customer retention (Am-

atriain and Basilico, 2015), (MacKenzie et al., 2013)

WUDESHI-DR 2020 - Special Session on User Decision Support and Human Interaction in Digital Retail

310

(Cirqueira et al., 2019b). The recommender systems

work as a type of information ﬁltering that lever-

age machine learning techniques, to determine users’

preferences to generate a ranked list of products rel-

evant for the users, based on their past behavior and

similarities to other customers, as well as patters in

items information (Mora et al., 2020). These engines

allow enterprises to better understand how they can

target customers or potential buyers, understanding

customer experience throughout the customer journey

(Lemon and Verhoef, 2016). Recommender systems

provides utilitarian value for the users as it boosts ef-

ﬁciency on information searches, and product com-

parison (Pimenidis et al., 2019) – key stages on the

path-to-purchase (Shankar et al., 2011), with the po-

tential to enhance the digital sales conversion.

While AI empowers recommender systems, re-

searchers have also considered the value of Explain-

able AI supporting in such application. Explainable

AI research aims to enable understanding of AI pre-

dictions, while keeping good learning performance

(Adadi and Berrada, 2018). Explainable AI has the

potential to support decision-making of AI users, and

enhance their experience and trust while dealing with

automated partners (Cirqueira et al., 2020). In the

context of recommender systems, explainable rec-

ommendations aim to enhance shopping experience,

through high quality and intuitive recommendations,

which are easy to consume (Wang et al., 2018). In-

deed, it has been shown that such explanations in-

crease purchase intention (Chen et al., 2019). In

(Zhang and Chen, 2018), the authors classify expla-

nations in the context of recommender systems within

ﬁve types: 1) User or Item-Based; 2) Feature-Level;

3) Textual; 4) Visual; and 5) Social.

User or Item-Based explanations regard similar

users and products to recommend items to a user.

Feature-level refers to important features of a prod-

uct, which a customer usually considers to make pur-

chases. Textual explanations are presented as natural

language sentences for a user to read. Visual expla-

nations highlight features on the image of a product

which are important for recommendations. Social ex-

planations are connected to friends and social media

activities to illustrate how a particular product is per-

ceived to a user. Those have been explored in the sce-

narios of restaurants (He et al., 2015), E-commerce

shopping (Cheng et al., 2019), movies recommenda-

tion (Huang et al., 2019).

However, it is lacking the assessment of explain-

able recommendations with augmented reality for re-

tailers’ assessment of such technologies when dealing

with different customer proﬁles and demographics in-

store.

3 RESEARCH METHODOLOGY

This research follows a Design Science Research

methodology (Peffers et al., 2007). That methodol-

ogy is suitable when taking and information systems

perspective for a study, which considers the require-

ments of users for the development of a system fulﬁll-

ing such requirements with an organization (Gregor,

2006; Creedon, 2016). Furthermore, it provides clear

steps for identifying the problems within an organiza-

tion, and for assuring rigor and relevance of a research

outcome by analyzing the state of the art and practice,

and to assure a problem is relevant for practitioners

and industry. In addition, the methodology guides the

development of an artefact to solve the problem, and

the interaction with practitioners to guarantee it is ful-

ﬁlling the research requirements.

In our study, we focus on investigating the impact

of an application on customers attitude moderated by

demographics, which might affect their willingness

for shopping in-store. Given those aspects and con-

nection to our research goals, we adopt this method-

ology to guide the development of this study, focused

on the stages of problem and objectives identiﬁcation,

development, and artiﬁcial evaluation step.

We started by investigating the problem, based on

the literature review described in section 2 and discus-

sions with practitioners within the PERFORM Train-

ing Network (Perform, 2020), which is a Horizon

2020 project and consortium composed of retailers

and universities. We perceived the problem as the lack

of understanding how customer demographics inﬂu-

ence customers acceptance of an XARSAA in-store.

This is a barrier for retailers aiming to invest in in-

novative technologies in their physical shops. The

research objective was then settled as to develop an

XARSAA tool as an artifact, and assess the perfor-

mance of its instantiation, moderated by demograph-

ics inﬂuencing the users attitude towards such a tool

in-store.

Therefore, from the literature review and discus-

sions with practitioners, the requirements for devel-

oping the XARSAA are to develop an XARSAA:

R1) within a mobile user-interface; R2) based on past

shopping data of customers; R3) enabling explainable

recommendations for shopping in-store; R4) to eval-

uate the developed XARSAA artefact through its in-

stantiation regarding the attitude of users moderated

by their demographics.

Who Wants to Use an Augmented Reality Shopping Assistant Application?

311

Figure 1: Smartphone-based artifact illustration leveraring AR recommendations enhanced by XAI for the participants.

3.1 Mobile-based Augmented Reality

Shopping Assistance Application

For development of the artifact and its instantia-

tion, the proposed XARSAA, which customers can

use throughout their shopping journey in brick-and-

mortar stores, is developed as an application running

on an android-based smartphone device. In our sce-

nario, while the app is deployed by the retailer, the

device is owned by the customer. Thus, the device

has access to personal information, which is needed to

provide tailored recommendations (e.g., social media,

historical purchase data). We conceptualize the arti-

fact instantiation to provide augmented content, an-

chored around the product of interest, and it displays

recommendations, offers, and comparison of items on

the smartphone as shown in Figure 1, besides to a buy

option.

In this stage of the prototype development, the

XARSAA leverages the smartphones camera to ﬁrst

detect the customers’ object of interest (product-

item), then the application can monitor the user’s

cameras ﬁeld of view to determine which product is

being examined by users at each point in time, as well

to track the item in the physical space under the cam-

era ﬁeld of view overtime. The involved object recog-

nition can be realized using SDKs such as Vuforia

(Microsoft, 2019). Furthermore, the application dis-

plays multiple digital buttons (UI) anchored around

the product to trigger and display relevant content us-

ing augmented reality.

3.2 Survey

Because of the early stage of our artifact, artiﬁcial

artifact evaluation was conducted by using an online

survey as this is the ﬁrst evaluation and an online sur-

vey provides a fast and efﬁcient way to get a sufﬁ-

cient number of participants. In the survey, partici-

pants were introduced to the concept of an XARSAA

instantiation with the help of pictures and videos.

To measure participants attitude towards an

XARSAA we adopted a questionnaire design pro-

posed by Hausman and Siekpe (Hausman and Siekpe,

2009). Consequently, we measured the participants

attitude towards the XARSAA with the constructs

“Usefulness” (4 items), “Entertainment” (3 items),

“Informativeness” (3 items), “Irritation” (3 items)

and “Purchase Intention” (4 items). Additionally,

we measured trust towards an XARSAA adopting a

scale from Hoffman et al. (Hoffman et al., 2018)

(6 items). Items were measured using a 5-point

Likert-type scale ranging from “Completely Dis-

agree” to “Completely Agree” and the sequence of

questions was randomly shufﬂed to avoid order bias.

Complementing, we asked participants open ques-

tions to get their general sentiment (“Yes, I agree” /

”No, I disagree”) about the presented XARSAA (5

items) as well as questions about their demographics

(“Age”,”Gender”,”Income”,”Shopping Type”, “Edu-

cation”). The questionnaire was conducted using

the software Surveygizmo (SurveyGizmo, 2020). An

overview of all questions asked can be found in Table

3.3 Participants

We recruited participants using a crowd-sourcing

provider called Clickworker (Clickworker, 2020a) as

this provider ensures a high level of qualiﬁcation of

their crowd workers by requiring the use of real per-

sonal data, testing of writing and language skills and

a constant evaluation of their workers results (Click-

worker, 2020b).

In total, we recruited 315 participants from the

DACH region (Germany, Austria and Switzerland).

To enrich the quality of our sample, we excluded par-

ticipants who took less than seven minutes to com-

plete our survey, used the same IP multiple times to

answer the survey, or entered only one word or ran-

dom letters in the open questions as the credibility of

these participants is questionable. The resulting sam-

WUDESHI-DR 2020 - Special Session on User Decision Support and Human Interaction in Digital Retail

312

Table 1: Statistical Tests.

Construct n Age Gender Income Shopping Type Education

Usefulness 251 0.370

0.005

0.031

0.604

0.694

Entertainment 251 0.432

0.057

0.112

0.087

0.969

Information 251 0.338

0.173

0.007

0.040

0.851

Irritation 251 0.357

0.674

0.016

0.848

0.418

PI 251 0.160

0.002

0.082

0.006

0.505

Trust 251 0.631

0.002

0.445

0.192

0.528

Q1 240 <0.001

0.142

0.724

0.746

0.147

Q2 231 0.106

0.411

0.793

0.327

0.137

Q3 230 0.349

0.073

0.370

0.990

0.887

Q4 213 0.901

0.037

0.710

0.029

0.719

Q5 229 0.325

0.080

0.430

0.361

0.814

Note: R (Regression), T (T-Test), M (Mann-Whitney-U-Test), C (Chi Squared),

K (Kruskal-Wallis-Test), PI (Purchase Intention)

Table 2: Bonferroni adjusted post-hoc tests and effect sizes.

D-C Group Comparison n1/n2 M1/ M2 T/Z/Chi Sig. d

Age - Q1 Yes-No 109/131 34.03/39.92 3.929 <0,001 0.509

Gender - Usefulness Female-Male 131/118 3.18/3.55 -2.788 0.005 0.357

Gender - PI Female-Male 131/118 2.92/3.31 -3.056 0.002 0.393

Gender - Trust Female-Male 131/118 3.0/3.26 -3.066 0.002 0.395

Income - Information <1000e- 1000e-1999e 63/84 3.41/3.90 -3.235 0.018* 0.554

Income - Irritation 1000e-1999e- 2000e-2999e 84/53 2.20/2.79 -3.194 0.021* 0.567

Shopping Type - PI NAF-VF 25/42 2.6/3.42 -3.412 0.006* 0.917

Shopping Type - Q4 Yes-No 96/117 2.90/2.61 -2.189 0.029 0.303

Note: D - C (Demographic - Construct), M (Mean), Q (Question; see Appendix), d (Cohens’s d),

NAF (Not at All Frequently), VF (Very Frequently), PI (Purchase Intention),

* (Bonferroni Adjusted)

ple included 251 participants between the age of 18

to 69 (mean age = 37.43, SD = 12.06) of which 131

(52.2%) were female, 118 (47%) were male, and 2

(0.8%) were divers. Looking at the participant’s ed-

ucation, 16 (6.4%) had “Some secondary education

(high school)”, 69 (27.5%) “Completed secondary

education (graduated high school)”, 97 (38.6%) had

“Some undergraduate education (college or univer-

sity)”, and 69 (27.5%) “Completed postgraduate edu-

cation (masters or doctorate)”. From the participants,

63 (25.1%) had a monthly net income of less than

1000 e, 84 (33.5%) earned between 1000 eand 2000

e, 53 (21.1%) earned between 2001 eand 3000 e,

29 (11.6%) earned between 3001 eand 4000 e, 15

(6%) earned between 4001 eand 5000 eand 7 (2.8%)

earned more than 5000 e. Participants shopping fre-

quency in the last 30 days was as follows, 25 (10%)

”Not at all frequently”, 63 (25.1%) “Slightly Fre-

quently”, 114 (45.4%) “Moderately Frequently”, 42

(16.7%) “Very Frequently”, and 7 (2.8%) “Extremely

Frequently”.

3.4 Statistics

To analyze for signiﬁcant impacts of participants de-

mographics on their attitude towards an XARSAA

we performed the following statistical tests: We used

a linear regression to check for a correlation be-

tween “Age” and attitudes, a Mann-Whitney-U-Test

to check for single group differences in “Gender”, and

a Kruskall-Wallis-Test for multiple group differences

in “Income”, “Shopping Type”, and “Education”.

When statistically signiﬁcant differences were identi-

ﬁed, we complemented the Kruskall-Wallis-Test with

Bonferroni adjusted post-hoc tests to pinpoint the sig-

niﬁcant group differences. Despite having used Lik-

ert scales, we calculated this analyzes using the mean

values of each construct as we regard the psycholog-

ical difference of the items on the used Likert scales

as equal and in such cases, Likert scales can be re-

garded as a continuous scales and their resulting data

as interval data.

Additionally, we analyzed the inﬂuence of partic-

ipant’s demographics on their shown sentiment when

Who Wants to Use an Augmented Reality Shopping Assistant Application?

313

answering the open questions using a T-Test (“Age”),

Chi-Squared (“Gender”), and Mann-Whitney-U-Test

(“Income”, “Shopping Type”, “Education”). We ex-

cluded participants who gave no answer or answered

“I don’t know”. When analyzing “Gender” we ex-

cluded participants who answered “Diverse” as their

small sample size (n = 2) does not allow for a robust

statistical analyzes. We tested the effect size of all dis-

covered differences using Cohen’s d’ (Cohen, 1992).

The software SPSS (v. 26) (IBM, 2020) was used to

analyze the survey data.

4 RESULTS

Looking at the results, participants “Age”, “Gender”,

“Income”, and “Shopping Type” had a signiﬁcant in-

ﬂuence on participants attitude towards an XARSAA

and participants sentiment when answering the open

questions (see Table 1).

In detail (see Table 2), a lower “Age” has a sig-

niﬁcantly positive inﬂuence on participant’s desire for

additional feature, showing a medium effect size (Yes:

M = 34.03/ No: M = 39.92/ d = 0.509). “Gender”,

has a signiﬁcant impact on perceived usefulness (Fe-

male: M = 3.18/ Male: M = 3.55/ d = 0.357), “Pur-

chase Intention” (Female: M = 2.92/ Male: M = 3.31/

d = 0.393), and “Trust” (Female: M = 3.00/ Male:

M = 3.26/ d = 0.395) of an XARSAA, all showing

small effect sizes and Male participants being more

effected. “Income” has a signiﬁcant inﬂuence on per-

ceived “Information” (<1000e: M = 2.20/ 1000e-

1999e: M = 3.90/ d = 0.554), with lower income

participants perceiving the XARSAA less informa-

tive than higher income participants, and “Irritation”

(1000e-1999e: M = 3.41/ 2000e-2999e: M = 2.97/

d = 0.567), with lower income participants perceiving

the XARSAA more irritating than higher income par-

ticipants, both showing medium effect sizes. “Shop-

ping Type” has a signiﬁcant inﬂuence on perceived

“Purchase Intention”, with more frequent shoppers

having a higher purchase intention than less frequent

shoppers, showing a large effect size (Not at All Fre-

quently: M = 2.60/ Very Frequently: = 3.42/ d =

0.917), and the possibility of an XARSAA to motivate

people to go brick-and-mortar shopping, with more

frequent shoppers being more motivated to go shop-

ping than less frequent shoppers, showing a small ef-

fect size (Yes: M = 2.90/ No: M = 2.61/ d = 0.303). In

contrast, participants ”Education” did not have a sig-

niﬁcant impact on participant’s attitude or sentiment

towards an XARSAA.

5 DISCUSSION

Our study could detect multiple demographic inﬂu-

ences on participant’s attitude and participant’s senti-

ment towards an XARSAA.

First, the age of a participant has a signiﬁcant

inﬂuence on the desire for additional features, with

a younger participants requesting more features and

older participants requesting no additional features.

This demonstrates that when designing an XARSAA

it should be taking into consideration to which age

group the XARSAA should be targeted. Younger

users prefer an application with a wide variety of fea-

tures while older users might prefer a more stream-

lined and less complex experience.

Second, the gender of a participant has a signif-

icant inﬂuence on the perceived “Usefulness”, “Pur-

chase Intention”, and “Trust” of an XARSAA, with

women having a lower score then men in all these

constructs. This indicates that an XARSAA should

preferably be targeted to male target group. However,

as the observed effect sizes only show a small effect

this difference should not be overestimated.

Third, people with lower income showed a sig-

niﬁcantly lower value in perceived “Information” and

a signiﬁcantly higher value in perceived “Irritation”

compared to people with higher income. This could

indicate that differing income groups have differing

requirements regarding the type and amount of pre-

sented information of an XARSAA. For example,

people with lower income might value a feature to

compare prices much higher compared to people with

higher income and in turn, people with higher income

could prefer information about the origin of a prod-

uct. Indeed, price comparison has been perceived

to empower consumers with low income previously

(Hamilton, 2009). Although the observed differences

show a medium effect size, it has to be noted that in

each of the two constructs only a single group com-

parison showed a signiﬁcant difference, which dimin-

ishes the overall strength of the observed effect.

Forth, ”Shopping Type” has a signiﬁcant inﬂuence

on users perceived “Purchase Intention” when using

an XARSAA. In fact, “Very Frequent” shoppers have

a much higher “Purchase Intention” when using an

XARSAA than “Not at all Frequently” shoppers. This

difference shows a high effect size further emphasiz-

ing that an XARSAA should be targeted to frequent

shoppers to further increase their purchase intention

instead of less frequent shoppers who might beneﬁt

less from using an XARSAA. Furthermore, although

having a small effect, more frequent shoppers showed

to be more motivated by the use of an XARSAA to

shop in-store, then less frequent shoppers.

WUDESHI-DR 2020 - Special Session on User Decision Support and Human Interaction in Digital Retail

314

Fifth, participants education does not have an im-

pact on participant’s attitude or participant’s senti-

ment towards an XARSAA, which is why it should

not be taken into consideration when designing an

XARSAA for a speciﬁc target group.

Summarizing, we argue that the optimal user

of an XARSAA would be male, as they perceive

an XARSAA as more useful and informative and

trust explainable recommendations more. The user

should not belong to the elderly group to still make

use of a wide variety of features, which would be

used to speciﬁcally tailor the information provided

by the XARSAA to people with lower and higher in-

come using in app personalization. Additionally, the

XARSAA should be targeted to frequent shoppers as

these would be motivated even more to shop in brick-

and-mortar stores by using an XARSAA and when

using an XARSAA would also have an increased pur-

chase intention.

6 LIMITATIONS AND FUTURE

WORK

In its current form, our study has some limitations

that are mainly connected with the stage of research,

as well as the design of the artifact and instantia-

tion. First, we restricted our assessment to an arti-

ﬁcial evaluation approach, following the design sci-

ence research framework. Therefore, the study does

not cover a whole design science project and steps,

but it is particularly focused on the stages of identify-

ing the problem, development of an artefact to solve

the problem, and its artiﬁcial evaluation.

Thus, in the current form, the instantiation was

not assessed in a real retail environment and as such

misses important characteristics of real retail situa-

tions as for example an assessment of participants

status of ﬂow (Hausman and Siekpe, 2009). Addi-

tionally, our study did not test customers privacy con-

cerns, which need to be further evaluated as usually

customers express higher privacy concerns in person-

alized services than in non-personal ones (Wetzlinger

et al., 2017).

Despite the mentioned issues, recommender sys-

tems and the shopping assistant artifact instantiation

provide clear beneﬁts to enhance user’s experience on

the path-to-purchase, as the system support customers

and provide rich information for decision-making

through the customer shopping journey, which has the

potential to boost brick-and-mortar sales. However,

as our study was focused on the inﬂuence of demo-

graphics on the the attitude towards an XARSAA, we

did not measure how an XARSAA competes to a reg-

ular shopping scenario or an augmented reality shop-

ping scenario without explainable AI features. Under-

standing the customer and working on their holistic

experience are some of the major obstacles that re-

tailers need to overcome. Some even go as far as to

call them the most important constraints for the future

of retail (Lemon and Verhoef, 2016).

Thus, we spotted different opportunities to com-

plement the study. In addition, the design science

methodology will be covered fully with iterations in-

cluding practitioners, and the design practice and nat-

uralistic evaluation of the instantiation. For instance,

further investigation should be conducted to evalu-

ate the artifact instantiation in comparison to other

shopping scenarios. Moreover, it is aimed to pro-

vide retailers and developers with the design princi-

ples and practices for such an application, which fos-

ters customer positive attitude towards shopping in-

store. Additionally, the impact of an XARSAA on

brick-and-mortar sales should be evaluated in a real-

case scenario in order to include ﬂow assessment and

measure the impact of privacy concerns.

7 CONCLUSION

Looking at our ﬁrst research question we conclude,

that a customer’s perception of an XARSAA is in-

ﬂuenced by the demographics “Age”, “Gender”, “In-

come” and “Shopping Type”. Regarding our second

research question, we argue that the optimal user of

an XARSAA would be a younger male who likes

to shop at least frequently. Users with low income

should receive different information then users with

high income. The level of education is not relevant for

designing an XARSAA. These results have various

managerial implications. By understanding the im-

pact of demographics on customers attitude towards

an XARSAA, retailers can decide if such a tool is an

appropriate tool to be used to engage with their main

customer target group and as a consequence if it ﬁts

to the company in general.

As such, brick-and-mortar retailers need to under-

stand that not all customers are alike. Gathering more

information about individual customers than just their

past purchases in the store may allow for more precise

subsequent analyses and predictions. For example,

adults could be differentiated from children in order

to see whether and how their shopping habits differ.

Additionally, with XARSAA Brick-and-mortar

stores have the opportunity to integrate innovative so-

lutions, not by mimicking the e-commerce but by an-

alyzing in-store customer desires and adapting tech-

nologies that have been proven to be key success

Who Wants to Use an Augmented Reality Shopping Assistant Application?

315

factors in modern retail, like recommender systems

(MacKenzie et al., 2013) and technologies that have

the potential to enrich customer experience like AR

(Papagiannidis et al., 2017).

To conclude, retailers needs to focus on solving

the customers’ problem, aiming to create a holistic

experience along the shopping journey. As creating

a shopping assistant which creates a more immersive

experience, and by leveraging machine learning tech-

niques can provide a more personalize in-store experi-

ence to the customers. These shopping assistant, such

as an XARSAA, can enhance the digital transforma-

tion of brick-and-mortar stores and thus, help the shift

in the physical environments in order to blur the per-

ception of channels for customers toward omnichan-

nel retail.

Therefore, as the retail sector moves forward, and

most retailers face challenges to keep up the competi-

tion, this study can help traditional brick-and-mortar

stores managers to create strategies, as well as it gives

insights for the practitioners who are working on the

transition toward the omnichannel model, in order to

strengthen their market position and become more re-

silient to online competition.

ACKNOWLEDGEMENT

This research is a part of the

European Training Network

project PERFORM that has

received funding from the Eu-

ropean Union’s Horizon 2020

research and innovation program under the Marie

Skodowska-Curie grant agreement No 765395. This

research reﬂects only the authors’ view, the European

Commission is not responsible for any use that may

be made of the information it contains.

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APPENDIX

In the following section, all questions of the used

questionnaire in this study are shown in Table 3.

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Table 3: Questions to the participants.

Constructs Questions

Attitutes towards XARSAA

Usefulness

Use1 This scenario can improve my shopping performance in-store

Use2 This scenario can increase my shopping productivity in-store

Use3 This scenario can increase my shopping effectiveness in-store

Use4 This scenario seems useful in brick-and-mortar

Enjoyment

Enj1 The shown scenario is enjoyable

Enj2 The shown scenario is pleasing

Enj3 This scenario is entertaining

Informarion

Inf1 The shown scenario offers a good source of product information

Inf2 This scenario supplies relevant information

Inf3 This scenario is informative concerning the shown products

Irritation

Irr1 The shown scenario is annoying

Irr2 The shown scenario is frustrating

Irr3 This scenario is irritating

Purchase Intention

PI1 I would deﬁnitely buy products in this scenario

PI2 I would intend to purchase products in this scenario in the near future

PI3 If it would exist today, it is likely that I would purchase products in this scenario

in the near future

PI4 I would expect to purchase products in this scenario in the near future if it

would exist today

Sentiment of participants

Sentiment

Q1 Looking at the presented application, are there features you are missing?

Q2 Do you see any issues or room for improvement when using this app?

If yes, could you give examples?

Q3 Would this application help to make your shopping trip more secure during

COVID-19? If yes, why and if no, why not?

Q4 Would this application motivate you to shop in-store?

If yes, why and if no, why not?

Q5 Did you ﬁnd the explanations given by the application helpful?

If yes, why and if no, why not?

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