Exploring Interaction Mechanisms and Perceived Realism in Different
Virtual Reality Shopping Setups
en Grande, Javier Albusac, Santiago S
anchez-Sobrino, David Vallejo, Jos
e J. Castro-Schez
and Carlos Gonz
Department of Technologies and Information Systems, School of Computer Science, University of Castilla-La Mancha,
Paseo de la Universidad 4, 13071 Ciudad Real, Spain
Virtual Reality, E-Commerce, Human-Computer-Interaction, User Activity Monitoring.
Within the e-commerce field, disruptive technologies such as Virtual Reality (VR) are beginning to be used
more frequently to explore new forms of human-computer interaction in the field and enhance the shopping
experience for users. Key to this are the increasingly accurate hands-free interaction mechanisms that the user
can employ to interact with virtual products and the environment. This study presents an experiment with a
set of participants that will address: (1) users’ evaluation of a set of pre-formalised interaction mechanisms,
(2) preference for a large-scale or small-scale shopping environment and how the degree of usability while
navigating the large-scale one, and (3) the usefulness of monitoring user activity to infer user preferences.
The results provided show that i) interaction mechanisms made with users’ hands are fluid and natural, ii)
high usability in small and large shopping spaces and the second ones being preferred by the users and iii) the
recorded interactions can be employed for user profiling that improves future shopping experience.
Virtual Reality (VR) is emerging as one of the key
technologies in retail innovation and in enhancing the
shopping experience for users in the coming years
(Grewal et al., 2017). In fact, studies suggest that VR,
along with Augmented Reality (AR) and other tech-
nologies, are part of the competitive strategy of many
retailers (Kim et al., 2023). Since the COVID-19 pan-
demic, the use of e-commerce services has increased
dramatically, not only among younger people but also
among individuals of all ages, including older adults
who were initially more reluctant.
One way to enhance this competitiveness is by
improving the shopping experience, making it more
realistic and closer to that of a physical store. Gen-
erally, the shopping experience can be described as
what arises from the interactions between a consumer
and a product in a specific shopping situation or envi-
ronment over a certain period of time. This definition
is directly applicable to a shopping experience in a
virtual space. In recent years, thanks to advances in
hardware for VR devices, it has become easier to fos-
ter greater immersion in a virtual shopping environ-
ment with the use of VR headsets (Xi and Hamari,
2021), which provide a much more comprehensive
field of view (FOV).
The mechanisms of interaction between the user
and virtual elements play a crucial role in the shop-
ping experience and have a direct influence on the
sense of realism. However, until recently, interaction
mechanisms in these virtual environments were quite
limited, focusing on classic input devices like the
mouse and keyboard, or head movements in devices
using a smartphone (Speicher et al., 2017). These
methods, in a way, represented a somewhat unnatural
and restricted way of exploring displayed products.
Recent advances in hand, body, and even eye
tracking in the latest VR headsets, such as the Oculus
Meta Quest 2, 3, and PRO models, open new avenues
for research and design of more complex interaction
mechanisms. Previous studies have shown that the
shopping experience in an immersive virtual shopping
environment provides greater immersion and more
natural interactions compared to desktop-based solu-
tions (Schnack et al., 2019).
Thus, we aim to achieve several objectives in this
study. The first is to investigate the sensations, fluid-
ity, and comfort that the interaction mechanisms pro-
vided by Meta’s SDK and others defined by us, such
as the shopping cart, produce in users in immersive
shopping environments. In terms of how these prod-
ucts should be displayed in a virtual shopping envi-
ronment, we aim to determine the user’s preference
for a large, store-like environment, or a smaller, sim-
Grande, R., Albusac, J., Sánchez-Sobrino, S., Vallejo, D., Castro-Schez, J. and González, C.
Exploring Interaction Mechanisms and Perceived Realism in Different Virtual Reality Shopping Setups.
DOI: 10.5220/0012626000003690
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 2, pages 505-512
ISBN: 978-989-758-692-7; ISSN: 2184-4992
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
pler one with minimal distractions. This will allow us
to ascertain how motivated they are to interact in each
environment, as well as the usability and navigabil-
ity of the environment. Lastly, monitoring user activ-
ity on current e-commerce websites is very useful in
understanding their tastes and preferences. Similarly,
we want to investigate the utility of such monitoring
in a VR Shopping environment so that the generated
data can be used by AI algorithms.
To achieve the proposed objectives, we have de-
signed and conducted an experiment with a group
of volunteers. Following the study, participants an-
swered a 26-question survey. The results of this sur-
vey have helped us determine the preferred layout for
virtual shopping environments, their feelings about
the presented interaction mechanisms, their prefer-
ence compared to other forms of shopping, and their
tastes based on the products they interacted with.
The remainder of this paper is structured as fol-
lows. Section 2 introduces previous work related to
interaction mechanisms used in virtual shopping envi-
ronments and experiments conducted in this context.
Then, Section 3 outlines the methodology followed
for conducting the experiment, as well as the re-
sources used and other considerations. Subsequently,
Section 4 presents the results obtained from the exper-
imentation, and Section 5 discusses the conclusions of
our work.
In their investigation into gesture-based controls
within VR shopping environments (Wu et al., 2019)
conducted a pair of studies that led to the develop-
ment of a novel approach for formulating dependable
user-defined gestures. The research included an ex-
perimental setup where 32 participants engaged with
a series of VR shopping tasks, ranging from object se-
lection to color changes and size adjustments—using
an HTC Vive headset. Nevertheless, the study’s de-
scription lacks specific details on the recording and
subsequent analysis of the gesture data.
(Peukert et al., 2019) explored the impact of im-
mersive experiences in VR shopping settings on the
likelihood of users adopting the technology for fu-
ture use. To this end, the researchers designed an ex-
perimental study utilizing two distinct environments:
one with a high level of immersion facilitated by the
HTC Vive, and a less immersive version presented on
a standard desktop display. In the more immersive
setting, the study monitored and captured data on the
movements of the participants’ hands and head, in-
cluding their interactions with products such as grab-
bing, dropping, or transferring items between hands.
Eye-tracking data was also gathered. The findings of
Peukert et al. suggest that the immersive quality of
the VR environment could shape a user’s decision to
reuse the system via two distinct avenues: one being
hedonic, driven by enjoyment, and the other utilitar-
ian, driven by practicality.
(Speicher et al., 2017) explored a VR shop-
ping platform to analyze the effects of user interface
modalities on shopping efficiency, user preferences,
and behaviours. Initial investigations involved a sur-
vey to capture the highs and lows of online shop-
ping, leading to the creation of VR prototypes for
both desktop and smartphones, focusing exclusively
on voice and head-pointing controls. This work cul-
minated in the proposal of design principles for VR
shopping ecosystems. Building upon this, they fur-
ther innovated with a new VR shopping prototype,
the Apartment” metaphor (Speicher et al., 2018), to
probe into product selection and manipulation strate-
gies, alongside different shopping cart designs. Their
study revealed that immersion and a seamless user ex-
perience are paramount for users, prompting recom-
mendations to mitigate motion sickness and advising
on product assortments that are most amenable to VR
shopping interfaces.
(Ricci et al., 2023) compared immersive virtual re-
ality (IVR) and desktop virtual reality (DVR) in the
context of virtual fashion stores to assess their influ-
ence on the shopping experience. A within-subject
experiment with 60 participants was conducted to ex-
plore the use of an HTC Vive head-mounted display
against a desktop setup. The results revealed that
IVR provides a more engaging, pleasurable, and ef-
ficient shopping experience, with participants show-
ing a higher intention to use IVR setups in the future
due to enhanced hedonic and utilitarian values. The
authors underscored the significant potential of IVR
in enriching the shopping experience in the fashion
In this section, we have presented works related to
VR Shopping and experiments conducted with proto-
types in this context. However, none focused on ex-
ploring the importance of realism in purchase inten-
tion, complex interaction mechanisms with products
and environment, or the utility of monitoring user ac-
tivity in the shopping environment.
In this section, we will describe both the resources
used and the methodology followed to execute the
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
experiment. The experiment carried out focused on
the following aspects: 1) usability and preference of
a large and/or small shopping environment, 2) eval-
uation of the proposed interaction mechanisms, in-
cluding interaction with the shopping cart, and 3)
the utility of monitoring user activity for profiling
and purchase intention. Due to space limitations,
the conceptualisation and mathematical formalisation
in 3D space of the interaction mechanisms used in
the experiment (grabbing, translating, rotating, scal-
ing, teleporting, adding to shopping cart and look-
ing with the eyes) as well as the elements with
which to carry out these interactions can be found in
the following GitHub repository: https://github.com/
3.1 Setup and Resources
The application, run on the Meta Quest Pro HMD,
was developed using the Unity graphics engine, ver-
sion 2022 LTS, to ensure compatibility with the lat-
est update of the Meta XR All-in-one SDK. The de-
veloped application consists of 3 scenes, each corre-
sponding to the steps of the experiment. The equip-
ment used for development and execution of the ex-
periment was an MSI Vector GP66, featuring an i7-
12700H, 32GB RAM, and an Nvidia GeForce RTX
3060 6GB VRAM GPU. During the experiment, the
laptop was connected to the Meta Quest Pro using
a 5-meter Oculus Link cable, in order to provide
the smoothest experience possible and minimize any
performance degradation during the experiment. It
should be noted that in all samples taken, the frame
rate of the application was maintained at 60 FPS.
A 2 m × 2 m space was defined to allow the user
some freedom of movement, especially in the small
environment, as the large environment required the
use of teleportation to move around due to the lack
of physical space.
3.2 Participants
For the experiment, we involved 10 participants (8
male, 2 female) from the university campus, with ages
ranging between 22 and 28 years (8 participants) and
58 years (2 participants). The experimental session
was conducted the same day, with each participant
taking a mean (M) = 28.50 minutes to complete the
experiment and answer the subsequent questionnaire.
The questionnaire also included questions about age,
gender, and experience with VR, as well as online
shopping and related aspects (see Fig. 1). According
to the mean and standard deviation results shown in
Fig. 1, most volunteers had little experience with VR
and the HMD used for the experiment (Meta Quest
3.3 Scenes Developed for the
For the execution of the experiment, three scenes were
developed in Unity to cover the previously described
objectives. Each of these was executed successively,
and each participant was notified beforehand of what
each scene entailed, what they would encounter, and
what they were expected to do.
3.3.1 Second Scene: Small Virtual Shopping
The second scene consists of a single counter that dis-
plays the ve scanned products, along with a label
that provides basic product information: name, short
description, and price. Fig. 2a shows the aforemen-
tioned scene. In this part of the experiment, partic-
ipants were expected to interact freely with the dis-
played products for 2 to 3 minutes. Here, they could
move, rotate, and scale the objects. After the inter-
action time, they were to include in the shopping cart
the three products they preferred or would buy.
The purpose of this step is to explore the usability
of such environments and the sensations they evoke in
users within a simple, distraction-free setting. In ad-
dition to the questionnaire, interaction data with the
products and the shopping cart were monitored to de-
termine which products were added, along with eye-
tracking data to gather results on their focus of atten-
tion. In this way, we aim to deduce a correlation be-
tween the number or duration of interactions and the
frequency or length of time a product is viewed, with
the final decision to add it to the shopping cart.
Analyzing the recorded data, our intention is to
discern whether a simple environment fosters greater
user interaction with objects and whether this leads
to an increase in the participant’s purchase intention
for the interacted products. While possible layouts
for virtual shopping environments have been inves-
tigated, some using uncommon metaphors like an
apartment (Speicher et al., 2018), most prototypes re-
sort to the concept of a supermarket (Chandak et al.,
2022; van Herpen et al., 2016). There exists a gap
in the exploration of simplified shopping environ-
ments. Furthermore, the authors of (Ricci et al., 2023)
proposed using small-sized virtual shopping environ-
ments to help avoid cybersickness, so we will also in-
clude questions on this topic in the post-experiment
Exploring Interaction Mechanisms and Perceived Realism in Different Virtual Reality Shopping Setups
Figure 1: Box plot of responses to questions from pre-experiment questionnaire. All questions followed a Likert scale from 1
to 5. The first six questions ranged from 1- No experience to 5- Very frequently.
(a) Small environment with a single ex-
hibitor of products and the shopping cart.
(b) Screenshot of the large environment,
consisting of 24 products and 7 counters.
Figure 2: Screenshots of the environments developed for the experiment.
3.3.2 Third Scene: Large Virtual Shopping
The third and final phase of our experiment consisted
of a shopping experience in a large environment that
recreates a floor of a shopping center, as shown in Fig.
2b. In this last environment, our main objectives were
to address the usability and comfort of interactions in
a large setting, with a special focus on teleportation,
and to gather data to observe the effect of a large envi-
ronment on the time spent interacting with products.
Finally, through a post-experiment questionnaire and
the data obtained from interactions, we aim to dis-
cern whether monitoring user activity in an immersive
shopping environment provides useful data for infer-
ring their tastes and preferences.
All participants began the experiment in the same
position. This time, the experience was based on tasks
of searching for a product in a specific section. This
allowed us to collect data on navigation through the
environment and interaction in the sections.
Thus, the first task for each participant was to nav-
igate to the back of the environment, to get accus-
tomed to the teleportation interaction. From there,
participants were instructed to visit 3 product sections
and then a section of their choice, with the option to
revisit one of the first three. The user had to add to
the cart the product they would buy from the respec-
tive section at each point.
Each participant took between 3 and 4 minutes to
complete all the steps, having the freedom to inter-
act with the products once they reached the requested
section. Following this, they proceeded to complete
the questionnaire (see Section 4.4), which took them
an average of 10 minutes.
3.4 Data Collected
A series of Unity scripts were developed to save
a range of data into a .csv file during the exe-
cution of the second and third scenes. The gen-
erated .csv files contain data on interactions with
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
products, the shopping cart (products saved in each
scene and when), eye tracking, and teleportation.
Detailed information about the headers of the .csv
files, as well as the files generated during the
experiment, can be found in the README file
of the following repository, located in the “csv-
Files” directory of the repository: https://github.com/
Figure 3: Frequencies of each product as being selected in
top 5 more realistic products.
4.1 Products Selected as Most Realistic
Fig. 3 shows the frequency with which each prod-
uct was selected as one of the top 5 most realistic by
the 10 participants. Out of a total of 50 responses
(5 products selected by each participant), 50% were
products scanned with Luma AI. Therefore, on aver-
age, 2.5 scanned products were selected by each par-
ticipant. Notably, the most popular products, as ob-
served, were the octopus teddy, followed by the sport
shoes and the burner, which are the visually most at-
tractive objects and were chosen by more than 60%
of the participants in their top 5. Although we were
aware that these scans had lower visual quality due to
being mostly composed of materials that cause reflec-
tions, like metal (the application’s own best practices
advises against scanning such objects), we in-
cluded them to cover a wider range of materials.
4.2 Participants Behaviour in the Small
The top graph in Fig. 4 compiles the data from the
ten .csv files of eye tracking. The left graph displays
the total amount of time, in seconds, each object was
viewed (i.e., the rays emitted from the eyes collided
with the object’s collider), with an annotation on each
bar indicating the number of times it was added to the
shopping cart. Conversely, the right graph shows the
number of times each product was looked at (counted
by the number of collisions), with the same annota-
tions as the left graph.
Notably, the products most frequently added to the
cart were the sport shoes, the octopus teddy, and the
wood burner, followed by the trophy and the mush-
room. However, in terms of collision time, the burner
registered the least amount of time. This could be due
to the object’s characteristics and the use of capsule
colliders, resulting in a narrower collider compared
to, for example, the shoes, affecting the data record-
ing. This situation is similar for the trophy. As ob-
served, the burner was the third most viewed product,
which might support this assumption. Regarding the
spatial information gathered from the eyes (position
and rotation), it’s noteworthy that the standard devi-
ation (SD) on the Y-axis is 0.61 for both eyes, while
SD = 0.11 and 0.28 for the X and Z axes, respectively.
This indicates that designs with less vertical or high
counters might be more comfortable for users.
The bottom graph in Fig. 4 shows a barplot of
the total time that each object was interacted with by
hands (left Y axis) and the total number of interac-
tions (right Y axis). This graph shows a similar trend
to those of the top barplots in Fig. 4, as we can see that
the three objects with the most interactions were the
sport shoes, the octopus teddy, and the wood burner,
followed by the trophy and the mushroom. An in-
teraction was considered from the moment the user
interacted with the object (in this case, grabbing it for
inspection) until releasing it. These data seem to sup-
port the assumption about the low viewing time of the
burner, as it’s observed that users interacted with the
burner more than 20 times in total for over 200 sec-
onds. Analyzing the time to the first interaction, the
mean (M) = 9.86 and SD = 4.19, suggesting that users
take about 10 seconds to visually scan the scene be-
fore deciding to interact with a product. Additionally,
M = 9.0 and SD = 1.45 for the number of interactions
performed by each user, indicating that the users in-
teracted more times than necessary for the experiment
on average. Finally, each participant interacted with
the octopus for M = 33.45 seconds (s), with the sport
shoes for M = 26.36 s, with the trophy for M = 20.36
s, with the burner for M = 14.85 s, and with the mush-
room for M = 9.91 s.
Exploring Interaction Mechanisms and Perceived Realism in Different Virtual Reality Shopping Setups
Figure 4: At the top, barplots showing total time (in seconds) and number of times every item was watched. At the bottom,
barplot showing total duration of interaction and total number of interactions for each product.
Table 1: Statistics of metrics extracted from teleportation
Mean (M)
Deviation (SD)
First teleportation 14.76 seconds 6.10 seconds
Number of
17.60 4.36
Attempts before
2.25 0.40
Time for
8.47 seconds 2.16 seconds
3.74 meters 1.70 meters
4.3 Participants Behaviour in the Large
In this section, we will address the analysis of the
recorded data with the same approach as in Section
4.2. First, we will present statistics of some met-
rics related to teleportation within the virtual environ-
ment, which was the main novelty compared to the
previous environment. Table 1 shows the results ob-
tained for the metrics we will discuss. To success-
fully perform the first teleportation, participants re-
quired an average of M = 14.76 seconds, with an SD
= 6.01. This means they needed about 15 seconds on
average until they correctly formed the gesture with
their hand, pointed to a valid location to teleport, and
joined their thumb and index finger. Furthermore,
the time to successfully complete a teleportation took
users an average of M = 8.47 seconds, SD = 2.16. Al-
though there may be false negatives in the data (for
example, the HMD’s hand tracking system detecting
the pointing gesture for teleportation when the user’s
intention is different) or the dimensions of the tele-
portation zones being a factor, we consider the aver-
age teleportation time to be moderately high. While
it’s true that the teleportation distance is slightly high
(M = 3.74 meters, SD = 1.70 meters), which may also
have influenced a longer teleportation time as it re-
quires more time to aim, it suggests that participants
opted to move a greater distance with each teleporta-
tion. Lastly, we observe that the number of teleporta-
tion attempts before successfully completing one has
an average of M = 2.25 attempts, SD = 0.4 attempts.
In terms of eye tracking data, Fig. 5 displays the
number of times products in a section were viewed. It
also includes the times information signs of the sec-
tions, which guided the user (approximately 15% of
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Figure 5: Number of times that each section drew the atten-
tion of participants.
the total), and the products added to the shopping cart
were viewed. As expected, the three most viewed
sections were those of the initial searches; however,
the fashion section stood out. Moreover, the technol-
ogy section was also one of the most popular dur-
ing the last step of free exploration of the environ-
ment, with 60% of users choosing a product from
this section. This environment also included many
computer-designed 3D models. Nevertheless, among
the scanned objects, both the sport shoes and the oc-
topus teddy were added to the shopping cart 4 times
each, and the trophy 2 times, maintaining moder-
ate interest among the participants. The rest of the
scanned objects, except for the trophy, were not in-
cluded in the environment.
Regarding product interactions, participants inter-
acted with 18 of the 24 products included, with the
least interaction in the food section. It’s notable that
the average number of interactions in this environ-
ment was M = 5.2, SD = 1.23, which is about half
of that in the small environment. On the other hand,
the average time spent interacting with products per
participant was M = 71.06 seconds, SD = 19.0. Com-
pared to the small environment, where the average
time was M = 117.16 seconds, SD = 16.02, there was
a 39% decrease in interaction time. These data sug-
gest that interaction was primarily limited to the first
product chosen by the user in each product search.
4.4 Questionnaire Answers
The post-experiment questionnaire completed by the
users can be consulted in the README of the repos-
itory referenced in Section 3.4. We used questions
from questionnaires present in the literature on User
Experience (UEQ), Immersion (SUS) and Motion
Sickness (MSAQ). Most of the questions were for-
mulated following a Likert scale from 1 to 5.
4.4.1 VR Shopping Usage
The participants significantly preferred inspecting
products as 3D models rather than 2D images (M =
4.0, SD = 0.63), positively influencing their purchase
intention (M = 3.9, SD = 0.83). Thanks to the inspec-
tion in VR using 3D models, participants indicated
that their purchase intention would change after ex-
amining an object in VR as opposed to just seeing it
in 2D images (M = 4.1, SD = 0.54), noting the ma-
jority of imperfections present in the sport shoes that
could be hidden with 2D images. Despite the data
collected, the majority preferred shopping in an en-
vironment similar to the large environment (M = 1.7,
SD = 0.46).Despite the data collected, the majority
preferred shopping in an environment similar to the
large environment (M = 1.7, SD = 0.46). Regarding
the usefulness of monitoring user activity to infer their
preferences and tastes, 8 out of 10 participants added
the product they wanted from the same section they
responded to in question Q24.
4.4.2 Immersion and Motion Sickness
Participants indicated very low levels of general
malaise (M = 1.6, SD = 0.66), fatigue (M = 1.9, SD =
0.94), and dizziness (M = 1.5, SD = 0.67), suggesting
that the color palette of the environments and other
graphic aspects are comfortable for users in not very
long shopping sessions. Regarding immersion, par-
ticipants felt quite immersed in the shopping environ-
ments (M = 4.1, SD = 0.54), and time passed quickly
during the experiment (M = 4.1, SD = 0.54). How-
ever, they were neutral in feeling like they were in a
physical store (M = 3.0, SD = 0.45).
4.4.3 User Experience and Usability
Participants found the environment moderately easy
to understand and use (M = 3.7, SD = 0.9), and
thought it would be so for most of the public (M =
3.6, SD = 0.92). Regarding the shopping cart, par-
ticipants equally valued adding products themselves
(M = 3.6, SD = 0.92) or through the user interface
button (M = 3.6, SD = 0.8), while opinions regard-
ing the use of gravity in the large environment varied
(M = 3.0, SD = 1.18). Additionally, users moderately
positively rated the ease of moving through the large
environment using teleportation (M = 3.7, SD = 0.64)
as well as the fluidity and naturalness of interactions
(M = 3.5, SD = 0.92), where the variety of responses
was slightly higher. Finally, participants preferred in-
teracting using hands freely (M = 3.5, SD = 0.81) over
the use of controllers (M = 2.6, SD = 0.49).
Exploring Interaction Mechanisms and Perceived Realism in Different Virtual Reality Shopping Setups
Based on the presented results, we can conclude that
realistic products appear to influence both purchase
intention and the time and frequency with which a
user interacts and inspects them. The data shown in
Section 4.2 indicates that the most viewed products
and those with which there was more extended inter-
action were the ones most added to the shopping cart.
We also identified aspects to improve in future shop-
ping environments, such as the use of colliders that
allow for more consistent data recording, as seen in
the case of the wood burner.
We also observed differences between a large and
a small shopping environment. Although participants
generally preferred the large environment, possibly
influenced by its novelty and the use of teleporta-
tion, we found that a small environment encourages
interaction. While only 10 seconds were needed in
the small environment to visually scan the scene and
start interacting, it took users in the large environ-
ment on average four times more seconds to begin
interacting due to needing to become accustomed to
teleportation. However, user preference for a large
environment indicates flexibility in configuring a vir-
tual shopping environment, focusing more on product
interaction or a thematic store that gamifies the shop-
ping experience and makes it more enjoyable for the
Additionally, participants positively rated the in-
teraction mechanisms included in the environment in
the post-experiment questionnaire responses, result-
ing in averages close to 4 on the presented Likert
scales. Also, the monitored data showed that about
85% of the time spent by each participant in the ex-
periment steps was used to interact with objects, indi-
cating that the interactions were pleasant and natural
for them to continue repeating. Moreover, environ-
ment navigability was perceived as simple, although
the data shown in Table 1 indicate somewhat elevated
times, as participants needed more time to aim at
a greater distance. Lastly, the utility of monitoring
user activity in these virtual environments has been
demonstrated, corroborating that the objects in which
the user shows the most interest are those added to the
cart, corresponding to the section answered in ques-
tion 24 of the questionnaire.
This work has been founded by the Span-
ish Ministry of Science and Innovation
MICIN/AEI/10.13039/501100000033, and the
European Union (NextGenerationEU/PRTR), under
the Research Project: Design and development of
a platform based on VR-Shopping and AI for the
digitalization and strengthening of local businesses
and economies, TED2021-131082B-I00.
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