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Voice user interfaces (VUIs) or voice assistants (VAs) such as Google Home or Google Assistant (Google), Cortana (Mircosoft), Siri (Apple) or Alexa (Amazon) are highly available in the consumer sector and present a smart home trend. Still, the acceptance seems to be culture-dependent, while the syntax of communication poses a challenge. So, there are some basic questions: 'Why do people buy VAs?' 'What do they use them for?' 'What could be improved in the future?'. We explore the opinion of a German technology-based user group to identify the challenges and opportunities of VAs. We focus on the interaction behaviour, frequency of use, concerns, and opinions of this target group as they show a higher variety of interaction as well as privacy concerns in representative population studies. Our preliminary findings confirm previous results (missing accuracy of commands and serious concerns about privacy issues) and show that technology-based users from Germany are intensive users, although with particular concerns about data collection. Probably, there is a correlation between privacy concerns and speech intelligibility as queries relating to VAs are problematic due to repetitions and refinement.
Content may be subject to copyright.
Exploring Voice Assistant Risks and Potential
with Technology-based Users
Andreas M. Klein1 a, Andreas Hinderks1 b, Maria Rauschenberger2 c and J¨
org Thomaschewski3 d
1Department of Computer Languages and Systems, University of Seville, Seville, Spain
2Social Computing Systems, Max Planck Institute for Software Systems, Saarbr¨
ucken, Germany
3Faculty of Technology, University of Applied Sciences Emden/Leer, Emden, Germany
Keywords: Voice User Interface, VUI, Conversational User Interface, CUI, Smart Personal Assistant, SPA, Voice
Assistant, VA, Frequency of Use, Context of Use, Privacy.
Abstract: Voice user interfaces (VUIs) or voice assistants (VAs) such as Google Home or Google Assistant (Google),
Cortana (Mircosoft), Siri (Apple) or Alexa (Amazon) are highly available in the consumer sector and present
a smart home trend. Still, the acceptance seems to be culture-dependent, while the syntax of communication
poses a challenge. So, there are some basic questions: ‘Why do people buy VAs?’ ‘What do they use them
for?’ ‘What could be improved in the future?’. We explore the opinion of a German technology-based user
group to identify the challenges and opportunities of VAs. We focus on the interaction behaviour, frequency of
use, concerns, and opinions of this target group as they show a higher variety of interaction as well as privacy
concerns in representative population studies. Our preliminary findings confirm previous results (missing
accuracy of commands and serious concerns about privacy issues) and show that technology-based users from
Germany are intensive users, although with particular concerns about data collection. Probably, there is a
correlation between privacy concerns and speech intelligibility as queries relating to VAs are problematic due
to repetitions and refinement.
1 INTRODUCTION
Analysts predict a growing use for digital voice as-
sistants and devices with voice control in the next
few years (Tuzovic and Paluch, 2018). Current mar-
ket analyses expect a worldwide increase from almost
2 billion dollars in 2020 to almost 7 billion dollars
in 2025 for voice- and speech-recognition software
(Tractica, 2020). This technology will and has al-
ready started: it has developed into a leading-edge
technology with a wide range of applications in both
corporate and consumer sectors. The example ar-
eas are healthcare, automotive industry, authentica-
tion and identification, voice commerce and customer
service, and smart home (Tractica, 2020).
When talking about digital voice assistants or
smart personal assistants, we consider the so-called
general-purpose assistants”, that belong to the
adaptive voice (vision) assistants” (Knote et al.,
ahttps://orcid.org/0000-0003-3161-1202
bhttps://orcid.org/0000-0003-3456-9273
chttps://orcid.org/0000-0001-5722-576X
dhttps://orcid.org/0000-0001-6364-5808
2019). Well-known examples are Google Assistant
(Google), Siri (Apple), Alexa (Amazon), Bixby
(Samsung), and Cortana (Microsoft). We refer to
these systems and devices with integrated voice
user interfaces (VUIs) as voice assistant (VA) in the
following. On one hand, VAs are highly available
in the consumer sector, as they are recently being
integrated into smart devices (also, internet of Things,
IoTs), tablets and personal computers. On the other
hand, there is a high degree of scepticism about their
use, especially in Germany (Tas et al., 2019).
The quality of a product or application including
VAs can be determined by measuring usability and
user experience (UX) which are designed with the
well-known Human-Centered Design framework
(HCD) (ISO/TC 159/SC 4 Ergonomics of human-
system interaction, 2010). HCD is a standard to
develop and evaluated, for example, products with
a graphical user interfaces (GUI). But there are
currently no equal focus in frameworks to develop
devices with VUIs. The UX of GUI is distinguished
from VUI as voice and hearing abilities are different
from the visual ability.
Klein, A., Hinderks, A., Rauschenberger, M. and Thomaschewski, J.
Exploring Voice Assistant Risks and Potential with Technology-based Users.
DOI: 10.5220/0010150101470154
In Proceedings of the 16th International Conference on Web Information Systems and Technologies (WEBIST 2020), pages 147-154
ISBN: 978-989-758-478-7
Copyright c
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
147
In order to meet the users’ requirements for VA
applications in the future, the amount of personal data
required must increase, which at the same time leads
to higher concerns of the users regarding the protec-
tion of their data and privacy (Tas et al., 2019). In
terms of adoption, Germany is far behind countries
such as Italy, Spain, and the United Kingdom, and it
is also behind countries such as the USA, India, and
China in global rankings in this particular area (Tas
et al., 2019).
Therefore, we aim to explore how VAs are used
in Germany by a so-called technology-based (affine)
target group, which refers to people having a prefer-
ence for technology. We expect to find higher poten-
tial for improvement and the essential concerns in this
target group to overcome barriers that might keep po-
tential users from using VAs in Germany. The Ger-
man study of the BVDW (BVDW e.V., 2017) shows
that VA user experience correlates with age, as three
out of four users (16 to 24 years old) have already ex-
perience with VAs. This age group also has the most
diverse usage patterns and, at the same time, the high-
est concerns in the use of VAs. Hence, we explore the
context of use for VAs for this target group, which,
in this case, refers to students of technical courses in
Germany.
This article is structured as follows: Section 2
presents recent studies that focus on different aspects
of the contemporary use of VAs. The following Sec-
tion 3 explains the development and structure of our
questionnaire while Section 4 describes the research
method. In Section 5 we cover our results and discuss
our findings. We finish with conclusion and future
work in Section 6.
2 BACKGROUND & RELATED
WORK
We briefly introduce VUI and VA terms and their re-
quirements regarding usability and UX. Furthermore,
we present several studies that explore VA user be-
haviour. The following VA characteristics regarding
our technology-based target group is of particular
interest to explore the controversy of high availability
of VUI vs. use: frequency of use, several user groups,
the context of use, and concerns of users. Since voice
interfaces and speech dialogue systems are recent,
there are various definitions. A concise and often
quoted definition is: A Voice User Interface (VUI)
is what a person interacts with when communicating
with spoken language application. (Cohen et al.,
2004). When interacting with information technology
systems, VUIs enable the user to work without classic
input/output devices such as the keyboard and the
mouse combined with screens, i.e., graphical user
interfaces (GUIs). The term ‘VUI’ mainly describes
an interface as one component of an entire system to
communicate via, e.g., voice commands. Sometimes
VUI is used to describe the overall system of a
speech application that consists of different function
modules such as automatic speech recognition or
natural language processing. The overall systems of
a voice application or a VA are called a service or
device (Tas et al., 2019). VAs offer various integrated
functions (e.g., web search, online shopping), and its
additional features are called ’skills’ or ’actions’ that
can be included. These ’skills’ can serve different
purposes, (e.g., entertainment, smart home), and are
often provided by third parties. Besides, there are
end-user environments that allow the use of preferred
online web services through VAs (Ripa et al., 2019).
UX (ISO/TC 159/SC 4 Ergonomics of human-
system interaction, 2010) as a holistic concept, in-
cluding all types of reactions, before, during, and af-
ter the use of a product. Measuring the UX of prod-
ucts applying GUI is possible using tools like the User
Experience Questionnaire (UEQ) (Laugwitz et al.,
2008), meCUE (Minge, Michael and Riedel, Laura,
2013) or UEQ+ (Schrepp and Thomaschewski, 2019)
questionnaires, but these are not specific to products
with VUI.
The UX of devices with VUI is not sufficiently
considered as these evaluation tools do not measure
the user’s expectations of VAs yet, i.e., compre-
hensibility, response behaviour, or response quality.
VAs should capture the context without a particular
formulation to fulfill the users’s intentions. UX for
voice interaction can be derived regarding the user,
the system, and the context (Klein et al., 2020c).
Existing questionnaires need to be extended or a
new questionnaire should be created to evaluate VAs,
which should lead to improvements in VAs. For ex-
ample, a new and flexible method is the modular
framework UEQ+ based on various scales to con-
struct a product-specific questionnaire for which three
VUI scales have been developed but not validated yet
(Klein et al., 2020b).
Others (BVDW e.V., 2017; Biermann et al., 2019;
Tas et al., 2019), however, focus on exploring current
users, use cases, and systems to understand VAs
interaction, and finding design patterns. For example,
the usability and UX of VUIs were described as
usable from a social media-based interest group, but
they also identified challenges. Users had difficulties
giving long commands, or commands have to be
given multiple times to accomplish the task, or there
would be problems with the integration with other
WEBIST 2020 - 16th International Conference on Web Information Systems and Technologies
148
systems (Pyae and Joelsson, 2018). A population-
representative online survey among 1040 US citizens
(aged 18 years) shows the usage behaviour con-
cerning different device groups (smartphone, smart
speaker, car) and results on the quality and wishes of
VA consumers (Kinsella and Mutchler, 2018). They
are not exploring privacy concerns. A long-term
exploration of smart speaker assistants (SSAs) in the
US over 110 days focused on how SSAs fit in the
household’s daily life and the long-term interaction
(Bentley et al., 2018). They found out that users
explore commands but not new use cases over time.
An online consumer survey conducted in Novem-
ber 2018 in Germany investigated the development of
the use of popular VAs (Tas et al., 2019). VA usage
behaviour is representative of the population based
on the quota sample of 18–54 years of age. Among
other things, aspects such as the intensity of use, us-
age patterns, and consumer protection are taken into
account. The results confirm the enormous potential
of this technology, as 85% of consumers already have
a VA. However, only 26% of Germans use at least
one device, probably due to the lack of conversational
skills and privacy concerns and monitoring. The study
revealed that VAs pass on information derived from
the continuously buffered data.
Another population-representative online survey
of 1006 Germans aged between 18–69 years old from
January 2019 investigated, e.g., the extent of VA use
and considered different user groups (SPLENDID
RESEARCH GmbH, 2019) but it did not focus on
technology-based users. The survey shows that 60%
of Germans have used at least one known VAs, 30%
of them intensively, 32% occasionally, and 38% less
frequently. Nevertheless, 61% of the respondents did
not see any sensible use, and 35% mentioned data
protection concerns.
The October 2017 online survey of 1038 partic-
ipants, representing the German population (aged
16 years), studied usage trends, concerns, and
application areas of VAs (BVDW e.V., 2017). For the
group of the surveyed German onliner people, 56%
had already used a VA and 80% found at least one
area of application, while 80% also expressed a usage
concern. In various survey categories, a subgroup
comparison is used to identify certain characteristics
in a specific user group. For example, women (52%)
use VAs less often than men (62%). Particularly
affine are those aged 16–24 years, among whom 75%
have already had VA user experience. This group
also shows significant concerns with 90%.
Since the technology-based user group showed a
more diverse usage pattern and the most notable pri-
vacy concerns, we are exploring this target group by
focusing on the challenging aspects of VA applica-
tions. Additionally, we want to know if challenges
such as the comprehension of commands has changed
since the latest evaluation of UX in 2018. Therefore,
we explore the opinions of both users and non-users
about VAs in connection with the current context of
use and use frequency. We also intend to discover the
risks and opportunities for such systems in the future.
3 QUESTIONNAIRE STRUCTURE
There are various types of questionnaires: for ex-
ample, the Subjective Assessment of Speech System
Interfaces (SASSI) (Hone, 2014) mainly to measure
VUI parameters or the User Experience Question-
naire (UEQ) (Laugwitz et al., 2008) to measure
Usability and UX. The UEQ is already designed
in over 30 languages including Spanish (Rauschen-
berger et al., 2013). The modular UEQ+ (Schrepp and
Thomaschewski, 2019) offers the advantage of focus-
ing on a specific research question but currently lacks
scales for VUIs. Either questionnaires do not have
VUI parameters included or are mainly developed for
one purpose (without focusing on UX) and cannot be
easily adapted to new research purposes. Adaptions
such as new VUI parameters beeing turned into,
for example, the UEQ, are costly in terms of time
and personnel. Hence, we designed a questionnaire
(Klein et al., 2020a) for our research questions, which
contains both qualitative and quantitative elements
to explore VUIs and their parameters as well as
usability and UX. Its essential aspects are questions
about availability and usage, frequency of use, the
context of use and the potential to improve VAs.
The structure of our questionnaire is as follows:
Page 1 contains the introduction to the topic of the
study regarding an anonymous survey. The socio-
demographic (age, gender) data is followed by two
questions about availability and which VAs are used.
Here, multiple entries of popular VAs (Siri, Alexa,
Cortana, Google Assistant) are possible as well as
a free text field for other devices. This is followed
by question 5 (“Give reasons why you own certain
VAs but do not use them.”) which can only be an-
swered with free text. Question 6 (“How often do you
use VAs in total?”) has six possible answers (daily,
approximately daily, several times a week, approxi-
mately weekly, several times a month, approximately
monthly or less often), and “never” with a hint to
jump to question 9 directly, and finally a free text an-
swer field to give reasons for occasional use. Ques-
tions 7—11 are structured tabular as follows: several
answer options, which are answered with a seven-
Exploring Voice Assistant Risks and Potential with Technology-based Users
149
point Likert-scale (e.g., from 1 [highly relevant] to 7
[completely irrelevant]) and ”No statement possible”.
The participants had after each question the possibil-
ity to enter further explanations in a free text field.
Question 7 (“Why do you use VAs?”) contain a total
of eight predefined fields with answers such as “For
more convenience”, “For more security” or “Because
I like to try out new techniques”. Question 8 (“In
what environment do you use VAs?”) provides two
context areas (at home and on the road), each con-
taining the possibilities “home control”, “media se-
lection”, “communication” and “web search”. Ques-
tion 9 (“In your opinion, what are the reasons for not
using VAs?”) offers various response options in the
areas of “understanding and responding to requests”,
data security, price and quality of the devices or the
preference for classic input devices. Question 10 asks
for improvement, e.g., in the areas of comprehensibil-
ity, quality of the answers of the VAs, as well as data
protection and privacy. Finally, Question 11 includes
the general feeling of “discomfort” when talking to
machines.
The questionnaire was evaluated in two pre-tests
with five participants each. After the first pre-test,
small changes in the wording and the procedure also
allowed the non-user to answer questions about im-
provements in VAs in order to derive possible rea-
sons for non-use. The second run confirmed the fi-
nal version of the four-page questionnaire with 11
question areas and the corresponding answer options.
After the pre-test, we conducted a preliminary study
that delivered useful and reliable results by compar-
ing our findings with the previous literature concern-
ing our target group. The paper–pencil form was cho-
sen to get a direct return from the participants. The
questionnaire is available in the original German lan-
guage and English translation (Klein et al., 2020a)
(https://doi.org/10.13140/RG.2.2.21473.12646).
4 METHODOLOGY
At the age of 16–24 years, Germans, who are per-
ceived as strongly technology-based people with great
VA user experience, show the most diverse usage pat-
tern and display the most significant concerns about
VAs (BVDW e.V., 2017). We aim to discover how a
German technology-based target group currently uses
VAs by surveying technical-degree students to ex-
plore the possibilities and current pitfalls that could
deter potential users from applying VAs. We focus on
the following research questions:
Table 1: Overview of the participants.
Group Number of
participants
%
Total 115 100.0
VA availability 101 87.8
Users VA 52 51.5
Non-users VA 49 49.5
No VA availability 14 12.2
Figure 1: Comparison of the availability of VAs for 115
participants to the use of VAs by 101 participants.
RQ1. How frequently are VAs used in this target group?
RQ2. In which context does the target group use VAs?
RQ3. What are their concerns regarding data protection
and privacy when using VAs?
RQ4. What improvements do they propose for VAs?
4.1 Procedure
We collected our data from different seminars of
three technical courses of studies (electrical engi-
neering, computer science, media technology) with
the paper–pencil questionnaire between March and
April 2019 at the University of Applied Science Em-
den/Leer. The participants were informed by one of
the authors about the purpose of the voluntary study.
Following a brief introduction, the questionnaire was
distributed among the students and collected after ap-
prox. 12-minutes of processing time.
4.2 Participants
Filling out the Likert-scales analogue has the risk that
the participants overlooked the scales, but they also
have the opportunity to fill out the same instantly.
Hence, missing data is due to not-readable or not-
filled-out Likert-scales. Participants were excluded
from the survey in the case of more than two miss-
ing response options (n=12). Hence, we analysed
115 participants and split our participants groups by
their response on the availability and actual use of
digital voice assistants (Question 6: How often do
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you use Voice Assistants in total?”). Here multiple
answers are possible (see Table 1): 12,2% (n=14)
stated that they did not have any VA, whereas 87,8%
(n=101) had such systems (see Figure 1). The study,
therefore, evaluates the results of 115 participants 91
males (79%), 22 females (19%), and two with no gen-
der indication) with the average age of 23 years (SD
3 years).
5 RESULTS & DISCUSSION
The statistical analysis was carried out using Mi-
crosoft Excel for Mac. We accept our imbalanced dis-
tribution of gender (19% female vs. 79% male, 2% no
answer) as this was similar to the comparative study
(BVDW e.V., 2017) and something that made sense
in retrospect. On the one hand, females are under-
represented in technical courses in Germany (Statista,
2020); on the other hand, woman currently seem to
use VAs less frequently (BVDW e.V., 2017). Other
comparative studies show similar gender distributions
with 77% or 72% male participants (Pyae and Joels-
son, 2018; Sciuto et al., 2018). As VAs are a relatively
young field of research, future research is necessary to
give a comprehensive assessment of the topic (e.g., on
VAs and gender acceptance), but this is not the main
scope of this paper.
In the first part of the study, the participants (n=
115) indicated the availability of VAs and the ones
they use. As a result, Figure 1 shows that 87,8%
(n=101) have access to at least one VA, among
which 51.5% (n=52) currently use one or more de-
vices and 48.5% (n=49) did not use any. The Google
Assistant is used most often with 28,7% (n=29), fol-
lowed by Amazon’s Alexa with 15.8% (n=16) and
Apple’s Siri with 12.9% (n=13). We are in line
with previous surveys where, for example, 56% of
the users chose the Google Assistant in 29% of cases
(BVDW e.V., 2017) or 60% of respondents have al-
ready used a VA (SPLENDID RESEARCH GmbH,
2019). According to Kinsella & Mutchler (Kinsella
and Mutchler, 2018) survey, 36.5% of the US popula-
tion say they are not interested in using such devices.
In the comparison of users/non-users, the technology-
based target group of our study, with 51.5% users,
has a significantly larger user share compared to the
WIK (Tas et al., 2019) study with 26% users. But the
BVDW study showed that the younger the users, the
more VAs are used (BVDW e.V., 2017). In summary,
we see the choice of a technology-based target group
for our study as confirmed. Overall, our small data re-
sults are in line with current studies, as we compared
above.
Figure 2: Frequency of use (n=49).
Figure 3: Comparison of intensive users.
5.1 How Frequently Are VAs Used in
This Target Group?
Figure 2 shows the frequency of use, from which
two user groups can be derived. The intensive users
(n=30, 61.2%) have a usage time of several times a
day to several times a week while the occasional users
(n=19, 38.8%) have approximately weekly to ap-
proximately monthly usage time. The graph is based
on n=49 participants since two answers in free text
form a) “sometimes” and b) “while driving” and one
respondent did not provide any pertinent usage time
information. In the SR (SPLENDID RESEARCH
GmbH, 2019) survey, a similar subdivision was made
to make a statement on the frequency of use and to
define meaningful user groups. This results in 30%
intensive users (daily and several times a week), 32%
occasional users (weekly, several times a month and
monthly), and 38% rare users. The WIK study (Tas
et al., 2019) shows 31% with a “rather frequent” use.
The large share of 61,2% of intensive users in our
study confirms the expectations of a high frequency
of use by the selected target group (see Figure 3).
5.2 In Which Context Does the Target
Group Use VAs?
The participants have evaluated four typical VA use
cases, each “at home” and “on the road” as well as
the dictation and voice mail function in general. Fig-
ure 4 shows that media selection in the domestic en-
vironment is the preferred application of this target
Exploring Voice Assistant Risks and Potential with Technology-based Users
151
group. Due to the small sample size (n=52) and
the wide spread of answers, the confidence intervals
are not small enough to make a reliable statement for
the entire target group. Since this was a preliminary
study, we need to gather more data to make further
valid statements in future.
The American long-term study (Bentley et al.,
2018) shows that in daily VA use, 40% of the requests
are for music procurement, 17% for information, and
9% for automation. The VACAR survey (Kinsella and
Mutchler, 2018) indicates that innovative applications
such as smart home control were used daily by 5.6%
and monthly by 11.9% of respondents. As a result,
Figure 4 shows that, except for media selection and
voice transmission, the target group accepts that the
usage environments and use cases have not been stud-
ied enough.
5.3 What Are Their Concerns
Regarding Data Protection and
Privacy When using VAs?
User data misuse and the possibility of monitoring
can be seen as the main concerns when using VAs in
our target group. For example, 36.5% (n=19) of the
users are concerned that the data could be misused,
while 40.4% (n=21) suspect that the devices could
be used for monitoring. These concerns relating to
data protection are also shown in a very similar form
by comparative studies. For example, the BVDW sur-
vey (BVDW e.V., 2017) has 33% users who fear data
misuse, and 33% who fear monitoring or interception
by others.
As a result, our target group, despite more in-
tensive use, express more significant concerns about
monitoring and data misuse. The quality of the accu-
rate command execution of VAs depends currently on
the ability to understand the context, e.g., User:“Siri,
Figure 4: Context of the use for VAs (y-axis scale from
“never” [-3.0] to “often” [3.0]).
how many inhabitants does Hamburg have?” Siri:“In
2019, the population of Hamburg was 1,899,160.”
User:“And in Sevilla?” Siri:“I found this online about
And in Seville’.”. The more information available to
the VA system, the more accurately it can react. At
the same time, this means that more data is collected
and transmitted, which increases the user’s concerns
about data protection and privacy (Tas et al., 2019).
Additionally, an American study shows that the
participants preferred the input of data using VA
from non-private information over private informa-
tion (Easwara Moorthy and Vu, 2014). As private in-
formation is unwillingly submitted to VAs in public
places in the presence of other people, it is perceived
as unacceptable (Easwara Moorthy and Vu, 2014).
5.4 What Improvements Do They
Propose for VAs?
We have collected answers about the overall opinion
independent from the brand about risks and oppor-
tunities. We have provided four categories: speech
intelligibility, response quality, additional forms of
interaction, and the protection of privacy. Figure 5
shows the results of the seven-point Likert-scale in
the numeric range between 3 (not applicable) and
+3 (applicable). We are comparing the means be-
tween 3 and +3 for the different questions in the
following. Owing to the small sample size and the
wide spread of answers, the confidence intervals are
not small enough to make a reliable statement for the
entire target group.
Privacy and protection of users (n=52, mean =
2.0) and non-users (n=49, mean =2.6) shows the
highest scoring for improvements. Then we can iden-
tify similar scores regarding speech comprehensibil-
ity. These are in detail for the user’s speech recogni-
tion (1.6), recognize fast speech (1.4) and recognize
unclear speech (1.5), as well as for the non-user’s
speech recognition (1.8), recognize fast speech (1.8),
recognize unclear speech (1.9). We also find high val-
ues in can distinguish users,improve learning ability,
and better integration.
Our results are in line with the existing literature
that the technology-based target group expresses neg-
ative thoughts towards the data protection and speech
intelligibility (Biermann et al., 2019). Biermann et
al. have identified three clusters for positive and
negative features regarding the most frequently used
genereal-purpose” VAs. The positive features are
specific function,interaction and positive emotions,
as well as negative features like speech recognition
& dialogue,trust and security, and system and
functionality. That technology-based users express
WEBIST 2020 - 16th International Conference on Web Information Systems and Technologies
152
Figure 5: Comparison between users and non-users regarding VA improvement proposals (y-axis scale from “not applicable”
[-3.0] to “applicable” [3.0]).
high concerns about privacy issues is explainable
considering the regularly appearing security news
of DDoS-Attacks with Internet of Things (IoTs)
(Schirrmacher, 2016; Labs, 2017; Scherschel, 2017).
Already in 2014, US Americans expressed privacy
concerns when using Voice-Activated Personal As-
sistants (VAPA) in public (Easwara Moorthy and Vu,
2014). There is probably a correlation between the
privacy concerns and speech intelligibility because
queries relating to VAs are problematic in repetitions
and refinement (Porcheron et al., 2017).
In summary, we can identify as the result of our
study a broad potential for improvement. Non-users
could become users if privacy and speech comprehen-
sibility are enhanced as a priority.
6 CONCLUSION AND FUTURE
WORK
Overall, VAs are equally present in technology-based
groups with deep concerns about privacy and express
opportunities for improvement in speech intelligibil-
ity. In this survey, we have investigated the availabil-
ity and actual use of the so-called “general-purpose
VAs. As expected, our results show in our target
group a high proportion of intensive users compared
to other studies. But, at the same time, there are con-
siderable concerns about monitoring and data misuse.
VAs are mainly used for media selection and voice
transmission; they can revolutionize the interaction
between humans and technology in the long run if
engineers take the user’s reservations into account.
Our preliminary exploration shows concerns from the
technology-based users and could be repeated every
year to understand the user needs and evolution. Fu-
ture work includes the collection of more data from
different user groups to validate our results and to un-
derstand the potential user groups, e.g., consumer vs.
professional use. Therefore, we will explore power or
routine users with a structured interview. We plan to
apply new scales for the modular questionnaire UEQ+
by focusing on the measurement of UX of VAs. We
additionally plan more qualitative evaluations with in-
terviews and observations.
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WEBIST 2020 - 16th International Conference on Web Information Systems and Technologies
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... There are several survey and review papers, e.g. on how fundamental metaphors and guidelines for designing voice assistants might empower and constrain visually impaired users [5], on the risk and potential of voice assistants [14], and concerns from the users' perspective [6]. Voice assistants have a high potential to support blind users, but entail usability and accessibility issues by complex commands, receiving appropriate nonvisual feedback, and correcting errors during interaction [2]. ...
... With consumer devices, another survey from 2021 identified Amazon Echo as most Alexa disseminated (78 %), followed by Apple HomePod (Siri) and Google Home (Google Assistant) with 12 % each [29]. Klein et al. [14] presented in 2020 a questionnaire with 115 (sighted) participants, whereof 87,8% have access to at least one voice assistant, and 51.5% use one device. Google Assistant is most frequently used, followed by Amazons Alexa and Apples Siri and 30,61% use a voice assistant several times a day or week, and 19,38% have a weekly or monthly usage times. ...
... However, interestingly, such privacy aspects are less pronounced among cell phone users than in the domestic smart home context [18]. A 2018 study identified several reasons impeding the use of voice assistants: Security concerns, gathering a lot of data, and autonomy and transparency when accessing information [10], where data privacy [6] and possible data misuse and monitoring [14] is not only most important, but also well-founded [16,17]. With offline data processing as far as possible, such problems can be systemically avoided. ...
Preprint
Full-text available
People with special needs like blind and visually impaired (BVI) people can particularly benefit from using voice assistants providing spoken information input and output in everyday life. However, it is crucial to understand their needs and to include these in the development of accessible and useful assistance systems. By conducting an online survey with 145 BVI people, this paper revealed that common voice assistants like Apple's Siri or Amazon's Alexa are used by a majority of BVI people and are also considered helpful. In particular, features in the context of audio entertainment, internet access and everyday life practical things like weather queries, time-related information (e.g. setting an alarm clock), checking calendar entries and taking notes are particularly often used and appreciated. The participants also indicated that the integration of smart home devices, the optimization of existing functionalities and voice input are important, but also potentially negative aspects such as data privacy and data security are relevant. Therefore, it seems particularly interesting to implement an online data processing as far as possible. Our results contribute to this development by providing an overview of empirically collected requirements for functions and implementation-related aspects.
... o Devices or systems including VUIs (Hoy 2018) • Google Assistant (Google), Alexa (Amazon), Siri ( • Privacy has to be enhanced. (Klein, A. M., Hinderks, A., Rauschenberger, M., & Thomaschewski, J., 2020a) • Further improvement proposals (Klein, A. M., Hinderks, A., Rauschenberger, M., & Thomaschewski, J., 2020a) • Challenges when using VUIs: (Tas et al. 2019, Rauschenberger 2021 o Correct command execution o Speech intelligibility o Context-sensitivity ...
... o Devices or systems including VUIs (Hoy 2018) • Google Assistant (Google), Alexa (Amazon), Siri ( • Privacy has to be enhanced. (Klein, A. M., Hinderks, A., Rauschenberger, M., & Thomaschewski, J., 2020a) • Further improvement proposals (Klein, A. M., Hinderks, A., Rauschenberger, M., & Thomaschewski, J., 2020a) • Challenges when using VUIs: (Tas et al. 2019, Rauschenberger 2021 o Correct command execution o Speech intelligibility o Context-sensitivity ...
... In Germany, most users rated voice assistants lower than in the UK, Italy and Spain. Reviews from these users also reflect the level of development of a country's voice assistants [7]. In contrast, the usage rate of Chinese language assistants is relatively high, and users' evaluation of voice assistants is also relatively high. ...
Article
Full-text available
Voice assistants have gradually occupied an important position in the products of many electronics companies. Artificial Intelligence voice assistants are able to interpret human speech and respond. Users can ask their assistant questions and manage other essential tasks such as email calendars through verbal commands. This paper analyzes the artificial intelligence voice assistant through the method of comparative analysis. The author studies the development situation of intelligent voice assistants, and compares the differences between Chinese and foreign voice assistants, and finally discusses the relationship between voice intelligent assistants and people’s lives. The author found that users in different countries have different functional preferences for using voice assistants, but they can help people’s work and life to a great extent. In other words, voice assistants play an important role in contemporary society. Therefore, people need to better understand the relationship between humans and machine
... According to a PwC report (McCaffrey et al., 2018), safety concerns, privacy risks and financial risks are a few common apprehensions consumers mention in explaining their resistance to using VAs for transactional service interactions. Some users have also noted speech comprehensibility as a feature requiring improvement (Klein et al., 2020). Using decision avoidance theory, Malodia et al. (2022) suggested that consumer inertia and procrastination behaviour resulted in limiting the use of VAs for non-transactional activities. ...
Article
Purpose This study aims to examine customers’ willingness to engage in service interactions enabled by artificial intelligence (AI) controlled voice assistants (VA). Drawing on the tenets of dual-factor theory, this study measures the impact of both enablers and inhibitors – mediated by trust in Alexa – on customers’ intentions to transact through VAs. Design/methodology/approach Data from a survey of 290 users of VAs from Japan was collected through “Macromill”. The authors used a covariance-based path analysis technique for data analysis after establishing the validity and reliability of the measures. Findings The results of this study demonstrate that convenience and status-seeking act as enablers and positively influence trust in VAs, whereas risk barrier acts as an inhibitor and negatively influence trust in VAs. In turn, trust in VAs positively influences the intention to use VAs for transactional service interactions. This association is positively moderated by technology comfort. Originality/value This study applies dual-factor theory to the context of VAs – a context that scholars have, to date, examined solely from a technology adoption perspective. For the first time, the authors adopt a dual-factor approach to identify a new set of antecedents for customers’ intentions to use VAs for transactional service interactions.
... • Example: Privacy has to be enhanced. (Klein, A. M., Hinderks, A., Rauschenberger, M., & Thomaschewski, J., 2020a) • Example: Privacy comparing Germany and Spain (Klein, A. M., Rauschenberger, M., Thomaschewski, J., and (Hassenzahl & Tractinsky 2006). ...
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Virtuellen digitalen Assistenten (VAs) wird weltweit ein enormes Wachstum bei Unternehmensanwendungen vorausgesagt. Eine Form von VAs sind Sprachassistenten, welche in vielen Geräten und Systemen integriert sind (z.B. Smartphones). VA-Qualitätskriterien, wie korrekte Befehlsausführung, Sprachverständlichkeit und der Datenschutz, sind gleichzeitig Herausforderungen bei der Nutzung. Daher ist die Messung der Qualität des Benutzererlebnisses für die Bewertung und Verbesserung von VAs von großem Interesse. Dieser Beitrag zeigt, wie Sprachinteraktion in Kombination mit dem neuen Fragebogenkonzept UEQ+ (https://ueqplus.ueq-research.org) gemessen werden kann.
... However, the users' requirements must also be considered in addition to the technical implementations. In this regard, several survey and review papers on the use and experience with voice assistants [34], on how fundamental metaphors and guidelines for designing voice assistants might empower and constrain visually impaired users [7], on the risk and potential of voice assistants [22], and concerns from the users' perspective [8] were conducted. ...
Conference Paper
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Assistive technologies help blind and visually impaired people to manage their daily lives independently. However, they usually have to work with voice user interfaces to use smartphones and tablets. Tasks like managing the calendar, taking notes, or setting an alarm clock require reliable voice recognition, which entails online access for remote computing. However, apart from data privacy and security issues, an online connection is not available in any situation. In this regard, our paper develops and evaluates an offline voice user interface with offline speech processing. The voice assistant was tested with seven blind and visually impaired people. It was found that the assistant was very well received (i.e., pragmatic, hedonic, and general quality) and that no functional limitations could be perceived due to the offline data processing. Based on these findings, the scope of functionality and the level of detail of the evaluation can be extended further to adapt this technology for this specific user group and promote its dissemination.
Chapter
People with special needs like blind and visually impaired (BVI) people can particularly benefit from using voice assistants providing spoken information input and output in everyday life. However, it is crucial to understand their needs and include them in developing accessible and useful assistance systems. By conducting an online survey with 146 BVI people, this paper revealed that common voice assistants like Apple’s Siri or Amazon’s Alexa are used by a majority of BVI people and are also considered helpful. In particular, features in audio entertainment, internet access, and everyday life practical things like weather queries, time-related information (e.g., setting an alarm clock), checking calendar entries, and taking notes are particularly often used and appreciated. The participants also indicated that the integration of smart home devices, the optimization of existing functionalities, and voice input are important. Still, also potentially negative aspects such as data privacy and data security are relevant. Therefore, it seems particularly interesting to implement offline data processing as far as possible. Our results contribute to this development by providing an overview of empirically collected requirements for functions and implementation-related aspects.
Book
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Inducing a thirst for research among the budding engineering graduates could be the top priority for institutions and teachers who care for the fantastic career of their students. It’s not necessary that the research should be so intense at the beginning of their study. But need for research should be elaborated to these students. Firstly, they should be exposed to the futuristic trends in technology, to the need for the modern society with respect to technology, to the user-friendly operations in modern technology and very importantly they should be taught how to collect validated and authentic information from the available resources from the web. Secondly, they should be explained in organising and classifying the gathered information with suitable headings and subheadings. Thirdly, they need to know about citing the works from where they have collected the resources. Finally, they should also be cautioned about plagiarism. This stable procedure of preparing a literary survey at the initial stage will create positive vibes and will stimulate them to take the lead to the next level of research in the following years. One such attempt is what you will witness in this book which has followed the above said procedures. A common theme of VOICE ASSISTANTS was chosen and procedures were methodically infused into the minds of the graduates. Skeleton of the paper was shared with the students and the information was gathered, paraphrased in their own language and was inserted to make their papers complete. This maiden attempt has given confidence to the students and has created a real thirst for fundamental research This marks the success of the attempt made by the teachers who not only teach but also motivate and inspire their students. This book is dedicated to all such wonderful teachers who attempt their best to instill a hunger for research among their students in all feasible manners.
Data
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Protocol for Exploring Voice Assistant Risks and Potential With Technology-based Users Version 2.0 / 2020
Conference Paper
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The UEQ+ is a modular framework for the construction of UX questionnaires. The researcher can pick those scales that fit his or her research question from a list of 16 available UX scales. Currently, no UEQ+ scales are available to allow measuring the quality of voice interactions. Given that this type of interaction is increasingly essential for the usage of digital products, this is a severe limitation of the possible products and usage scenarios that can be evaluated using the UEQ+. We describe in this paper the construction of three specific scales to measure the UX of voice interactions. Besides, we discuss how these new scales can be combined with existing UEQ+ scales in evaluation projects. CCS CONCEPTS • Human-centred computing • Human computer interaction • HCI design and evaluation methods
Conference Paper
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Voice User Interfaces have become part of our daily life by being integrated in smartphones, computers, smart home devices and many other consumer products. However, despite their potential, voice assistants like Alexa, Google Assistant or Siri are not that widely used. Why is that? What are their pain points? How can the interaction and dialog flow between the user and the voice user interface (UI) be improved? In a research and development project at designaffairs, insights from user research were used to develop an interaction and dialog concept for a voice UI. The concept draft was refined with user feedback to develop a software prototype. This early voice UI prototype was then evaluated in a user test which demonstrated its potential in better satisfing the user’s wishes for a natural dialog flow. Overall, the iterative user centered approach of the project revealed crucial pain points of the human-machine interaction and further opportunity areas to meet the user’s need.
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This report is only available in German. For an English version, please refer to the TPRC paper: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3426809 Be it Amazon's Alexa, Apple's Siri, Google's Assistant, Microsoft's Cortana or Samsung's Bixby: Voice assistants are becoming more and more popular. According to the survey of 3,184 consumers in Germany, which was conducted for this discussion paper, 26% already used at least one such voice assistant at the end of 2018. The adoption rate is thus similar to that of smartphones about five years after the introduction of the first wave of devices. However, the two technologies differ critically as regards the investment and effort a consumer has to take in order to use them. With around 85% of German consumers already owning or using at least one device with a pre-installed voice assistant, they do not have to spend additional money or even to install an additional app to start using a voice assistant. The majority of current smartphones and laptops feature pre-installed voice assistants. So called smart speakers have found their way into the homes of around 11% of the respondents. Given the penetration of devices with pre-installed voice assistants, a rapid increase in the adoption rate is possible. In light of our survey, however, such an increase is unlikely in the near future. The vast majority of non-users have no intention of using one of the five most popular voice assistants within the next year. Current usage patterns also do not point to a great influence of the assistants. Consumers use voice assistants rarely and only for about 2 to 3 functions. The most frequently used one is to request simple information from the Internet, such as the weather, sports results or the nearest petrol station. Alexa, Amazon's voice assistant, is used much more frequently on average than the other systems. The average Alexa user also relies on approximately one function more than the users of Bixby, Cortana, Google Assistant and Siri. Music streaming is particularly popular with Alexa users, be it via Amazon's own service or a third-party provider. Multi-homing, i.e. the parallel use of several such assistants, is the exception: 78% of users rely on one voice assistant. Among the 22% of those who multi-home, there are users who choose different services for different tasks (about 7% of the surveyed users) as well as those who actually perform at least one identical function over several services (about 15% of the surveyed users). The voice assistants integrated in other devices such as cars, refrigerators or washing machines, which are often provided by third-party providers, hardly play a role in Germany. The usage patterns observed, especially the lack of multi-homing and the close integration of popular voice assistants into larger digital ecosystems, underline the importance of the continuous monitoring of digital technologies. This is particularly true if they represent a direct customer interface and thus a possible new gatekeeper, as is the case with voice assistants. The huge number of pre-installed systems suggests that broad adoption of voice assistants could be substantially faster than with existing digital technologies as soon as the systems have the necessary appeal for consumers. Competent authorities should therefore develop forward-looking policies with regard to this upcoming categories of services.
Article
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Existing user experience questionnaires have a fixed number of scales. Each of these scales measures a distinct aspect of user experience. These questionnaires can be used with little effort and provide a number of useful support materials that make the application of such a questionnaire quite easy. However, in practical evaluation scenarios it can happen that none of the existing questionnaires contains all scales necessary to answer the research question. It is of course possible to combine several UX questionnaires in such cases, but due to the variations of item formats this is also not an optimal solution. In this paper, we describe the development and first validation studies of a modular framework that allows the creation of user experience questionnaires that fit perfectly to a given research question. The framework contains several scales that measure different UX aspects. These scales can be combined to cover the relevant research questions.
Conference Paper
Full-text available
The digital age has yielded systems that increasingly reduce the complexity of our everyday lives. As such, smart personal assistants such as Amazon's Alexa or Apple's Siri combine the comfort of intuitive natural language interaction with the utility of personalized and situation-dependent information and service provision. However, research on SPAs is becoming increasingly complex and opaque. To reduce complexity, this paper introduces a classification system for SPAs. Based on a systematic literature review, a cluster analysis reveals five SPA archetypes:
Chapter
Voice Assistants, and particularly the latest gadgets called smart speakers, allow end users to interact with applications by means of voice commands. As usual, end users are able to install applications (also called skills) that are available in repositories and fulfill multiple purposes. In this work we present an end-user environment to define skills for voice assistants based on the extraction of Web content and their organization into different voice navigation patterns. We describe the approach, the end-user development environment, and finally we present some case studies based on Alexa and Amazon Echo.
Conference Paper
Recently, commercial Voice User Interfaces (VUIs) have been introduced to the market (e.g. Amazon Echo and Google Home). Although they have drawn much attention from users, little is known about their usability, user experiences, and usefulness. In this study, we conducted a web-based survey to investigate usability, user experiences, and usefulness of the Google Home smart speaker. A total of 114 users, who are active in a social-media based interest group, participated in the study. The findings show that the Google Home is usable and user-friendly for users, and shows the potential for international users. Based on the users' feedback, we identified the challenges encountered by the participants. The findings from this study can be insightful for researchers and developers to take into account for future research in VUI.
Conference Paper
In-home, place-based, conversational agents have exploded in popularity over the past three years. In particular, Amazon's conversational agent, Alexa, now dominates the market and is in millions of homes. This paper presents two complementary studies investigating the experience of households living with a conversational agent over an extended period of time. First, we gathered the history logs of 75 Alexa participants and quantitatively analyzed over 278,000 commands. Second, we performed seven in-home, contextual interviews of Alexa owners focusing on how their household interacts with Alexa. Our findings give the first glimpse of how households integrate Alexa into their lives. We found interesting behaviors around purchasing and acclimating to Alexa, in the number and physical placement of devices, and in daily use patterns. Participants also uniformly described interactions between children and Alexa. We conclude with suggestions for future improvement for intelligent conversational agents.