Never ever or no matter what: Investigating Adoption Intentions andMisconceptions about the Corona-Warn-App in Germany
Maximilian Häring*
University of Bonn

Eva Gerlitz*
Fraunhofer FKIE

Matthew Smith
University of Bonn, Fraunhofer FKIE
Sascha Fahl
CISPA, University of Hannover

Dominik Wermke
CISPA, University of HannoverYasemin Acar
Max Planck Institute for Security and Privacy

Abstractthese measures is the use of contact tracing apps. Early in
2020, the public media in Germany discussed two different
approaches. The centralized app was based on PEPP-PT [24],while the decentralized app, was based on DP-3T [44]. Bothapproaches come with advantages and disadvantages. The
public debate was driven by researchers who signed an open
letter (April 2020) backing the decentralized approach [7], as
well as privacy advocates (April 2020) [11]. A major argument
was that the general population would only be willing to adopt
the app in sufficient numbers if privacy was preserved [7]. The
German government had previously committed to the centralized app, which they abandoned during development due tothe public debate, starting a new development project basedon the decentralized approach at the end of April. The media extensively discussed this decision, and the government,
via direct appeals and public media, encouraged people toinstall the app. In this context, we were interested in finding out how much the general public understood about the
newly announced but, at the time of conducting the study,
yet to be released decentralized app. We were also interested
in the general public’s attitudes towards potential properties,
particularly those about the centralized and decentralized approaches’ advantages and disadvantages. To gain insights into
these issues, we conducted an online survey study from May
30 to June 11, 2020, with a quota sample of 744 participantsfrom Germany. The app was released on June 16 and became
one of the most installed European apps [43].
In this paper, we make the following contributions:

To help tackle the COVID-19 pandemic, the tech community
has put forward proximity detection apps to help warn people
who might have been exposed to the coronavirus. The privacy
implications of such apps have been discussed both in academic circles and the general population. The discussion inGermany focused on the trade-off between a centralized or
decentralized approach for data collection and processing andtheir implications. Specifically, privacy dominated the publicdebate about the proposed “Corona-Warn-App.” This paper
presents a study with a quota sample of the German population (n = 744) to assess what the population knew about
the soon-to-be-released app and their willingness to use it.
We also presented participants potential properties the app
could have and asked them how these would affect their usage
intention. Based on our findings, we discuss our participants’views on privacy and functionality, including their perceptionof selected centralized and decentralized features. We also examine a wide range of false beliefs and information that was
not communicated successfully. Especially technical details,
such as that the app would use Bluetooth, as opposed to location services, were unknown to many participants. Our results
give insights on the complicated relationship of trust in thegovernment and public communication on the population’s
willingness to adopt the app.

1

Christian Tiefenau*
University of Bonn

Introduction

Since the spread of COVID-19 in 2020, governments havebeen developing measures to fight its transmission. One of• We conducted the first study to assess participants’knowledge of and beliefs about the planned CoronaWarn-App (CWA) after the app features were publishedand broadly discussed in the media. This is in contrast
to other studies in Germany that focused on hypotheticalapps.
• We assess how accurately participants could identify theproperties of the planned German contact tracing app.

* These main authors contributed equally to this work.
This document is an extended version of a paper published at SOUPS 2021.

• The German public discourse was dominated by the discussion of a centralized versus decentralized application.

This document was last edited on July 09, 2021

1

We offer insights into the level of relevance of the app’s
capabilities linked to the centralized approach.

government changed course and pivoted to the decentralized
approach on April 26 [12]. Two days later, a press release
was published that contained (technical) information about
the app, such as that it would work with Bluetooth [27].

• We compare our work to contemporary work that assessed willingness to install various hypothetical tracing
apps in Germany [68].

2.22Related Work

Since the idea to use apps that would support the contact
tracing work of the health departments to contain COVID-19
became popular among governments worldwide, researchers
aimed at understanding user preferences to allow for broad
adoption. Studies were conducted in Australia [66], Europe[65] (including Belgium [70], France [45, 52], Germany [45,48,56,59,60,67,68], Italy [45], Ireland [63], Switzerland [69]and the UK [45,46,57,71,72]) and the USA [41,45,51,53,54,
58, 59, 61, 62, 64, 65, 68, 73]. As Utz et al. [68] and Kostka etal. [59] found similarities for Europe and America, we focus
on work conducted with those populations.
Most conducted studies were choice-based conjoint experiments, in which participants were asked to select which app ofseveral they would prefer or were given different app configurations for which they had to decide if they would install suchan app [48,68,71,73]. Some studies asked to imagine a corona
tracing app has already been released [45, 54, 57, 59, 61, 70]
We are aware of only a few studies that looked at the user acceptance and influencing factors on the acceptance for the appthat was already launched in the surveyed country [66, 69].

In this section, we discuss related work in three areas relevantto the context of this work. 1) The discussion about technicalaspects of tracing apps and their history/development in Germany, in particular between the centralized and decentralized
approach; 2) studies about the acceptance of contract tracing apps and influence of factors in released or hypotheticalcorona tracing apps. 3) Research on the existing knowledge
of users about upcoming or already released corona tracingapps.
We note that most of the literature was published after wedesigned and conducted our study, so we compare our results
retrospectively.

2.1

User acceptance of tracing-apps

Technical background/history

The idea of supporting contact tracing with mobile apps
emerged early in the pandemic. The first working app to fight
COVID-19 was released in March 2020 in Singapore [21],only two months after the first reported infections outside of
China [35]. The app had the disadvantage that it had to beconstantly the visible app on the smartphone to allow dataexchange. For such an app to effectively support health agencies in contact tracing, a large set of the population has to use
it, depending on the overall scenario (e.g., how quarantine ishandled) [55].
In Germany, two approaches to collect and process thedata were discussed. In the centralized version called “PanEuropean Privacy-Preserving Proximity Tracing” (PEPP-PT)[24] all collected encounters, namely contact-ID and timestamp of encounters with other app users would be uploaded
and stored on a central server. In the decentralized version“Decentralised Privacy-Preserving Proximity Tracing” (DP3T) [44], all encounters remain on the users’ smartphone.
If a user tests positive for COVID-19, they can upload alltheir cryptographic keys (from which the IDs can be derived)
to a server. Once a day, a list with keys of people who reported their positive COVID-19 tests is downloaded to allusers’ smartphones and compared to locally stored encounters within the last 14 days. The important difference is that
determining whether a user has been at risk of contracting
COVID-19 is calculated on their smartphone itself and theencounter data never leaves the phone. At the beginning of
the discussion, the German government wanted to follow thecentralized approach [25]. After two open letters in April
2020 suggesting the usage of DP-3T [7, 23], the German

Investigated Factors The studies explored factors that
could influence participants’ intention to install and use thecorona tracing app. Several authors investigated how personal
characteristics, such as demographics or one’s experience
with the pandemic, impacted the acceptance of corona tracing
apps [45, 46, 48, 52–54, 56, 59, 61, 68, 69, 73]. Amongst others,
people who were male [56, 61, 68], had higher trust in the
respondent’s government [45, 48, 52, 68, 69], health authorities [69] and others in general [56], had higher income [61,69]
or lived in urban areas [59, 61] were more likely to install a
tracing app. While some authors noted that younger participants were more inclined to use a tracing app [53, 56, 61], others found the opposite [54, 59]. Looking at pandemic-related
factors, health concerns during the pandemic, and personal experience with COVID-19 increased the willingness to install a
tracing app [48, 52, 53, 59, 68]. Additionally, better adherence
to COVID-19 regulations was a positive influence [69]. Fearand anxiety concerning changes in government rules [46]impacted participants negatively.
Apart from factors that might influence the acceptanceof contact tracing apps in general, many studies were conducted to find which app design choices would be considered positively or negatively by participants. The studies covered different attributes (e.g. what data will be collected) [48, 58, 61, 62, 73], the apps purpose [68] or what
institution will develop, host, distribute or own the app2[41, 48, 53, 57, 64, 65, 71]. Li et al. [62] found a preference
for the centralized, while Zhang et al. [73] had more participants who were willing to use a decentralized app. Horvath
et al. [57] found a centralized national health system to befavored. Participants rated health agencies more trustworthy
than their government as a whole concerning corona tracing
apps [57, 65]. Still, Simko et al. [65] found no entity thateveryone trusted. Anonymous data collection impacted participants positively in their decision to use an app [48] and it
was perceived negatively if the collected data can uniquely
identify individuals [68]. Independent of app design choices,
studies often found a subset of participants who did not likeany of the proposed apps [58, 62, 68].
Aside from app properties, researchers looked into effects
an app could have, and the influence this has on adoption[48,61,71] such as malfunctions of the app [58,68] or the perceived effectiveness in fighting against COVID-19 [59–61,70].
They found that participants’ perception of the (public) healthbenefits an app would offer and other people’s willingness
to use it explained the usage intention better than app design
choices and personal characteristics [61]. Performance expectancy and the benefit were also among the most criticalpredictors in other studies [59, 60, 70]. Malfunction in contact
tracing was found to be of negative influence [68] and participants valued false negatives worse than false positives [58].
The willingness to use contact tracing apps increased if its
usage is linked to priority testing [48, 71]
Further, numerous studies identified the primary reason
why users would or would not install tracing apps [45, 46,
56, 63, 66, 68, 69, 72]. In their studies, privacy concerns [45,46, 56, 66, 68, 69, 72], technical concerns or lack of technicalequipment [56, 66, 69], distrust in the government [66] orthe fear of surveillance at the end of the pandemic [45, 63]
and doubts about the effectiveness or benefit [56, 69] werebrought up as negative influences. The following topics werementioned as reasons for using a tracing app: willingness toprotect family and friends [45, 63], a sense of responsibility
for the community [45, 63, 72] and the hope that the app maystop the pandemic [45].

2.3

misunderstandings about the described app, although the description was still visible when they answered the questions.
For example, a third believed they would receive the names ofinfected people they had been in contact with. The number ofincorrect answers could not predict the participants’ usage intention. Williams et al. [72] conducted focus groups in the UK
to explore public attitudes to the proposed contact tracing app.
The authors found the most common misconception was thatthe app would make it possible to precisely identify COVID19 cases in their vicinity and amongst their contacts. In one
study that took place outside Europe and America, Thomas
et al. [66] surveyed 1500 Australians after the national tracing app was released and examined participants’ knowledge
about it. Around 70% knew the app would make it easier andfaster to inform people exposed to COVID-19 and warn users
who would not have been warned otherwise. However, 50%
did not reject the assumption that their personal information
would be used after the pandemic, and 57.4% believed theapp would warn if infected people were near them.

3

Methodology

This section describes instrument development and the conducted survey, our recruitment, and the data analysis process.

3.1

Survey Development

We followed the public discussion of the CWA. We were interested in the information that potential users have, mainly
as discussions focused on whether enough people would install it and why (not). Much of this discussion in Germany
revolved around the topic “centralized versus decentralized”
and the claim that this would heavily influence the willingness
to install. As there was little concrete related work on users’
perception, we were also interested in the broader topic ofacceptance and beliefs. We discussed factors and potential
influences with other researchers and iterated multiple times
over the survey.

Knowledge about corona tracing apps
Pre-Testing Before handing out the survey, we conducted
several test rounds with colleagues who were not involvedin the survey creation to identify comprehension problems.
Following that, we asked 19 computer science students tofill the survey and provide additional feedback about unclear
sections and inconsistencies. After this, we additionally sampled 50 participants on Clickworker [6]. Finally, we asked
five participants without a technical background to fill thesurvey while thinking aloud. Before starting the final study,
Qualtrics [29] additionally sampled 50 people. This pilot
study helped get an overview of the duration, evaluate therandomization and spot flaws in the survey logic.

The subsequent studies examined what participants knew
about corona tracing apps apart from factors and properties
influencing users’ installation or usage intention. Simko etal. [65] conducted surveys for seven months in the US andEurope, focusing on contact tracing and privacy and asking
for potential app properties. Within the participants’ answers,
they identified several false mental models, e.g., that proximity tracing is less secure than location tracking due to constantcommunication between devices. Zhang et al. [73] surveyed
2000 participants in the USA to measure the support for ninedifferent COVID-19 surveillance measures, including tracing
apps. While analyzing, they noticed participants had many3

3.2

Survey Content

Demographics In the end, we asked for demographic dataand how COVID-19 impacted their lives (Q16-29).

To inform the survey structure and questions, we looked at
the different available approaches to develop a contact tracing
app and followed the media discussion. The final survey consisted of the following described four parts and can be seenin Appendix B.

3.3

Recruitment

We used Qualtrics [29] to recruit a representative German
sample according to age, education, household income, andfederal state/region. Qualtrics provided representative numbers for age, education, and region, numbers for income weretaken from the Federal Statistical Office of Germany from2017 [17]. Due to the nature of online surveys, older participants were underrepresented, and we could not entirely fulfill
our quotas for a representative sample. The final distribution
after sanitizing the data together with our targeted quotas canbe seen in Table 1. The study was conducted from May 30 toJune 11; thus, shortly before the app was launched on June
16, 2020. To take part in the study, it was required that theparticipants owned a smartphone since that is a precondition
to use the app. 1025 participants took part in the study forwhich we paid Qualtrics C4000.

Media Sources and Knowledge In the first part, we asked
whether the participants had already heard of the planned app
and asked for their knowledge sources (e.g., public broadcasters, family members, social media, or official government
websites) (Q7). After this, we asked questions that assess theirknowledge of the properties of the app in general (Q8). We
also asked such questions for two scenarios: what happens if
other users are infected (Q9) or if the users themselves are
infected (Q10). In these three question blocks, we included
23 statements that were either correct (8 statements) or incorrect concerning the soon-to-be-released app (15 statements).
As incorrect statements, we used properties of another ’corona
app’ released in Germany [8]1 or were discussed in media at
the time of the survey. For example, we included the misconception that the app will share all phone numbers saved on
the user’s phone or share a movement profile with the government. Three statements were neither correct nor incorrect for
the released app. Details of all these statements can be foundin Table 6.

3.4

Data quality

During the study, Qualtrics excluded participants that 1) took
less than half the median of the time the participants neededin the final pilot study (243 seconds) for completing the survey, 2 or 2) failed the attention check question in the potential
properties question block.
To ensure our participants were paying attention, we included a straightforward attention check (Q14) and one comprehension check question (Q11). The comprehension checkquestion gave a short explanation of how the app will work
(specifically mentioning using Bluetooth for contact tracing).
It then asked what technology the app will utilize for contact
tracing. We excluded participants from our analysis who did
not choose “Bluetooth”.
When we designed this question, it seemed quite straightforward. To our surprise 262 participants failed this question. We
then discussed whether we had overlooked genuine reasons
why this question might be answered incorrectly. Potentially,
participants who read our description text did not believe it
and answered true to their previous or internal beliefs. It isalso possible that our description was too complex for someto understand and thus could mean that they misunderstoodother questions.
We also discussed the possibility of excluding participants
due to inconsistent or odd answers, e.g., a participant statingthat they are a civil servant but also stating that they lost theirjob3 or stating that the app used Bluetooth in one questionand stating otherwise in another. However, after an in-depth
discussion, we decided against this. We looked at the free text

Disposition to use In the second part of the survey, we
showed the participants a minimal description of the app,including the information that its primary purpose will be
to warn users who have been close to infected persons anduse Bluetooth to detect other app-users. Following that, theparticipants were asked whether they are planning to use theapp, using a question with five possible answers ranging from“1 - Definitely will use it” to “5 - Definitely will not use it”(Q12). We also asked to report their primary reason for theirchoice in a text field (Q13).
Potential Properties The third part presented 23 hypothetical statements, from now on called potential properties, about
the app (Q14). The participants were asked how these statements would affect their willingness to use the app if the app
would work this way (5 answer options, from “1 - Definitely
would use it” to “5 - Definitely would not use it”). In thissection, we added an attention check question. Six of those
properties can be attributed to a centralized approach, while
one would only be valid for the CWA app that is based on
the decentralized approach. Additionally, 12 properties werecorrect for the to-be-released app, while 11 were incorrect.
Details of all the presented statements can be seen in Table 7.

2 This is a standard procedure at Qualtrics; we do not know how many
participants were excluded.
3 In Germany this combination is incredibly rare

1 The app can be used to share fitness data with the RKI.

4

3.6

answers of participants who had such inconsistencies, but wefound them generally to be as plausible as those who did not
and did not find any other warning markers.

3.5

Ethics

Our study was reviewed and approved by our institution’s
Research Ethics Board. We also adhered to the German dataprotection laws and the GDPR in the EU. For all answers,
we provided an option for participants not wanting to giveany details (i.e., “I don’t want to state” or “I don’t know”).
Participants could drop out at any time. Participants had toconsent to take part.

Analysis

We analyzed the data in two different ways. Most of the results
concern a quantitative analysis of the answers. One free text
answer was analyzed qualitatively. Percentages are reported
rounded.

3.7

Limitations

We aimed for a representative sample of the German population. Unfortunately, some groups are over- while others
are underrepresented. Our sample lacks people of older age,
people with lower education, and those with high income.
Qualtrics, who acquired the sample for us, stated that this isvery common in online surveys. As with every survey study,
we have to take into consideration that the data is self-reported.
In this study, we additionally asked participants about their future behavior, which is even more prone to uncertainty. Many
possible properties of the app have consequences that are noteasy to estimate. We cannot assume that participants understood and thought of the consequences, especially considering
many participants did not understand how the app worked indetail.

Quantitative For our quantitative evaluation, besides reporting, we performed an ordered logit regression with model
selection, an ordered logit regression model containing all
potential properties, and hypothesis testing. For the app usage
intention, we decided to combine participants who answered“I don’t know” and those who answered “I am undecided”.
In the Media Sources and Knowledge-section (Q8-10) of
the survey, we asked participants whether they thought thepresented statements were correct for the CWA. False statements required no click from the participant to give the correct
answer. This may influence the measured correctness of theirbeliefs, besides the point that some statements may be easieror harder to know. We, therefore, only report true statementsthat were known as (positive) knowledge and false statementsthat were clicked as false beliefs.

4

Results

In this section, we present the results of the survey. We describe our participants, the accuracy of their knowledge about
the CWA, and what sources they consulted. Following that,
we describe the participants’ intention to use the app and how
demographic factors and beliefs about the app explain thisdecision. Last, we describe how different potential properties,
such as additional features, influence the willingness to install
the app.
To avoid confusing and overly complicated figures, weassigned short identifiers to each question, which can be seenin Table 6 and Table 7.

Coding process Participants were asked to indicate theirprimary reason for wanting to use or not use the CWA (Q13).
One researcher looked at the answers and coded them according to the participant’s misconceptions. All presented
quotes were discussed and agreed upon by two researchers.
All quotes were originally in German and translated into English by the authors.
Regressions For our exploratory regression model, we conduct a model selection approach by computing a set of candidate models based on different factor combinations, andselecting the final model based on a combination of the best
Akaike Information Criterion (AIC) [49] and Bayesian Information Criterion (BIC) [1]. Possible factor categories andcorresponding baselines are reported in Table 5.
Our final ordered logit regression (cf. Table 4) reports
change as log odds to highlight trends: a negative value directly correlates to a negative effect and vice versa for positive
values. In addition, we report a 95% confidence interval (C.I.)
and a p-value. For convenience, we highlight factors below
an arbitrary significance cut-off of 0.05 with an asterisk (∗).
In addition, to investigate potential effects of different app
features, we conducted an ordered logit regression (cf. Table 3) with all app features as factors.

4.1

Demographics

Table 1 presents the demographics of the final 744 participants
and Table 8 gives an overview of how COVID-19 impacted
them.
Since we conducted the study at an early stage of the pandemic, few participants had fallen ill with COVID-19 themselves or had somebody close to them fallen sick. 31.1% countthemselves as being a member of the high-risk group. This
may seem high but matches estimations in Germany [30].
Around half reported that the pandemic did not influence
their work situation (52.7%). 24.5% work from home, and13.6% reported working in short-time. 74.9% said they did
5not have specialized tech skills. According to the “Sonntagsfrage” [42],4 our sample includes 20.7% fewer participants
who would have voted for the CDU/CSU5 at the time thesurvey was conducted, but 6.6% more participants who would
vote for The Greens. All other parties are close to the percentages of the “Sonntagsfrage”. We hypothesized the party
preference might be an indicator of the attitude towards theapp as at least one party publicly criticized the app [33].

4.2

shows all statements that are false for the released app. We
classified participants who marked any of the false statements
as correct as having “False Beliefs”.
59.5% of the participants knew about the app’s basic functionality, i.e., that it would warn its users when they had beenin contact with another user who later tested positive ((OTH)
INFORMS IF CONTACTED INFECTED and (SLF) INFORMS
MY CONTACTS). Around half of the participants knew about
the detailed flow that a lab has to confirm the infection before
it can be registered in the app ((SLF) DATA TRANSMISSIONONLY AFTER CONFIRMATION) to prevent misuse of the app
and many false warnings.
However, the app’s technical basis was less known: Only
29.8% of the participants who reported to have heard about
the app knew that the app would share temporary IDs andtimestamps, and 43.5% were aware the app would use Bluetooth. At the same time, 54.6% of the participants thought thatthe app would use location services, and 24.7% believed theapp would use Bluetooth and location services in combination.
Although Bluetooth is not a technique developed for position
finding, it is, next to GPS, listed as a “location service” insome circumstances [3]. We assume that only participants
who marked Bluetooth and location service could have beenaware of this detail. 30.0% did not think the app would use
Bluetooth but checked location services.
A common misconception (57.5%) was that the app would
warn users if an infected person is in their vicinity.
9.89 % of the participants knew all the information thatwas included in the official press release about the app ((GEN)SHARES TEMPORARY IDS, (GEN) DETECTS NEARBY USERS,(GEN) USES BLUETOOTH,(GEN) FIGHTS DISEASESPREAD,(OTH) INFORMS IF CONTACTED INFECTED,(SLF) INFORMS MY CONTACTS [27]).
On average, the participants correctly recognized aroundhalf of the eight aspects that are true for the app (median =4, mean = 4.26, std = 2.05), but none was known to everyone.
Only five participants marked all correct attributes as suchand did not believe any incorrect statement.
We asked for the classification of two statements ((GEN)RESTRICTS BASIC RIGHTS,(GEN) THREATS PRIVACY)
that cannot be classified as correct or incorrect but are
based on personal sentiments. We saw that participants were
worried about their privacy in combination with the app(27.4%) and their basic rights (20.1%). 14.9% stated both incombination.

Knowledge

We asked participants to select what they believe are correctstatements about the app (Q8-10). As the app was not released
when the survey was conducted, answers were not based on
experience with the app. However, a press release had been
publicized that gave information about the app [27], suchas that it would use Bluetooth-Low-Energy, that its primary
purpose would be to warn users who had been in close contact
with infected people, and that users would not learn whoof their contacts reported an infection. At the same time,
much misinformation about the app, its purpose, and technicaldetails were spread as well [5].
This section describes the sources participants used andpresents participants’ beliefs about the to-be-released CWA.
Sources We asked the participants whether and where they
heard about the planned corona app (Q7). Figure 3 shows
the frequency of how often the participants reported a source.
Please note that as participants could report more than one
source, the percentages do not add up to 100%. Few but a
non-negligible amount of participants (11.7%) reported tonever have heard of the app. This leaves 657 participants who
were at least somehow aware of the app.
More than half of the participants (54.7%) reported thatthey received information about the app from public broadcasters. The second most common marked source was social
media (29.6%), such as Twitter or Facebook.6 Scientific publications were used by 7.9% to get information about the
CWA.
Correctness of assumptions The following paragraphgives an overview of the participants’ assumptions about theCWA (Q8-10). It should be noted that we only included participants who previously reported that they already heard about
the app (n=657).
Figure 4 depicts the correct statements for the app that was
shortly released after the survey was conducted and shows
how many participants marked those to be true. Figure 5

Misconceptions and lack of information After asking participants how likely they will use the app (Q12), we asked
for the primary reason for their installation intention (Q13)in free text form. As we saw many false beliefs, we coded
the answers according to underlying misconceptions. We saw
statements that were incorrect concerning the app’s functionality and its data usage.
Some statements we observed were incorrect but might be

4 Regular opinion research in Germany, asking, “Which party would you
vote for if federal elections were held this Sunday?”
5 The Christian Democratic Union of Germany / Christian Social Union
in Bavaria
6 Following a statistic from Statista, 65% of the citizens use social media
in Germany in general [2].

6Gender
Age
Education
Household Income
Work Status

IT-Knowledge
Smartphone OS
P OLITICAL A FFILIATIONF EDERAL STATE

Female18-24
65+
ISCED 0-2<= 1300 C
3600-5000 C
School student
Self-employedNot disclosed
Yes
Android
The Greens
AfD
BW
HB
NI
SN

55.9
24.2 (9.2)
10.9 (25.1)
5.9 (16.5)
18.3 (16)14.0 (17)
4.7
4.6
3.4
20.9
71.5
21.6
6.1
12.9 (13.1)
1.5 (0.8)
6.7 (9.6)
5.0 (5.0)

Male
25-34

43.3
14.3 (15.3)

Other
35-49

0.8
23.9 (23.9)

ISCED 3-4
1300-1700 C>5000C
Univ./col. student
Freelancer

48.9 (58.1)
13.4 (8)6.2 (20)
9.8
2.2

ISCED 5-8
1700-2600 C
Not disclosed
Employee
Unemployed

40.5 (25.4)
20.8 (20)
10.6
48.9
7.5

No
iOS
CDU/CSUThe Left
BY
HH
NW
ST

74.9
26.1
19.4
10.1
12.1 (15.6)
3.6 (2.2)
22.9 (21.6)
4.7 (2.8)

Not disclosed
Other
SPD
Others
BE
HE
RP
SH

4.2
2.4
11.7
24.5
7.0 (4.3)
6.9 (7.5)
4.6 (4.9)
3.5 (3.5)

50-64

26.8 (26.4)

Not disclosed
2600-3600 C

2.2
16.7 (18)

Civil servant
Retiree

2.0
16.9

FDP

6.7

BB
MV
SL
TH

2.4 (3.1)
2.4 (2.0)
1.5 (1.2)
2.4 (2.7)

Table 1: Participants’ demographics (N= 744), in percentages. Numbers in brackets = the targeted distribution [17, 29].
correct with the further context of the answer. Participants
who (probably) wanted to use the app, for example, stated:“My safety”, “To protect myself” or “I want to stay healthy”.
Since the app cannot protect its users directly (users have already been exposed to infected people before they are warned)
but only indirectly (the more people download the app, themore people might be influenced and will also download it,leading to better protection of all of its users), these answers
indicate a misunderstanding of what the app can do for individual users.
Other answers were incorrect beyond doubt. One participant, for example, thought they would be able to see thenumber of current infections: “To follow the spread of thepandemic” (Probably will use the app).7
As already seen in Figure 5, participants believed the app
would inform its users if infected people are close. This argument was used both as a positive as well as a negative reason
to use the app. One participant probably wanted to use theapp and argued: “So I can see who is infected nearby to keepa larger distance to them and protect myself and fellow people.”. Another one did not want to use the app and wrote:“The determination of the location is too inaccurate. It might
happen that other people see me as infected, even though it issomebody else. I have concerns that this might lead to public
hostilities or bullying.”
Participants also misunderstood what data will be used andshared: “I don’t want the government to know where I am
in each and every second - especially as three other companies are involved as well”8 (Definitely will not use it) and “I
don’t want the government to have all my numbers and names”(Definitely will not use it).
Additional to the location misconception, we observed aparticipant who believed it would be necessary at all times tohave access to the internet: “I don’t know how it works but
if I need internet you can already forget about it, as I don’t

have mobile data.”. Anecdotally the participant was not able
to correctly answer that the app will use Bluetooth.
Participants indicated that they are confused by the amountof (different) information: “I don’t have any trust. With all thenews, I don’t know what to believe anymore!!!” (Probably not
use the app). One participant, who failed the comprehension
question and was undecided about the app, said: “Everybody
says the opposite of the others. Many say you lose your privacy.”
Following these answers and the data reported previously
in this section, we conclude that many participants did not
wholly understand the apps’ functionality and thus assume a
misconception in who will be protected by the app, what datait collects, and with whom the data will be shared.

Figure 1: Reported intention to use the app.

4.3

Intention to use

Figure 1 shows the usage intention of all 744 participants.
When looking at those participants who were very certain inwhat they will do, more participants indicated to definitely
install the app (Def-Yes, 21.2 %) than to definitely not installit (Def-No, 13.4 %). Almost a third reported they will probably use the CWA (Prob-Yes, 28.8 %) compared to 12.9 %who reported to probably not use it (Prob-No). 23.4 % werestill undecided (Undecided) about the installation. As of May
28, 2021 the reported download number of the CWA is 28
millions [22]. That estimates to around 46% of smartphoneusers in Germany [32]. This estimate does not take into account that that the same person could download the app onto
multiple devices.
In the following, we report indications for reasons of the7 This is, in fact, possible since version 1.11 which was released at theend of January 2021 [37].
8 It is not fully clear who the participants refers to. Telekom and SAP
developed the app. Two research institutes advised. The RKI is publisher [13]7

installation intention. For this, we selected an ordered logit
regression with a model selection process via best AIC and
BIC (c.f. Table 4). In the following paragraphs, we focus thereport only on the statistically significant values.

influence their decision to use it (Q14). Table 7 in the Appendix shows whether these properties apply to the app as it
was described pre-release or not and whether they describe acentral or decentral property. We were particularly interested
in seeing whether the centralized versus decentralized debate,in which computer scientists and privacy advocates were dominating, was reflected in the broader population’s opinions. In
the following, we highlight whether the properties belong to
the centralized (C) or decentralized (D) approach or if they
are independent of the apps’ architecture and could be applied
for both approaches (B).
Figure 2 shows all potential properties and the distributionof how they would influence the participants. It can be seenthat no property is rated exclusively positively or negatively.
However, some have a clear negative tendency (i.e., (PP)HACKERS KNOW INFECTION STATUS, (PP) UNNECESSARY
QUARANTINE DUE TO FALSE POSITIVE WARNING), or aclear positive tendency ((PP) WARNS ME IF EXPOSED TO
COVID, (PP) HELPS RKI ASSESS SITUATION).

Trust in Government Both trust and distrust of the government correlate heavily with app usage intention. The log
odds for both “Somewhat distrust” and “Fully distrust” areproportionally negative compared to the neutral baseline (Log
Odds = −0.56 and −1.12 respectively). Contrarily, log odds
for both “‘Somewhat trust” and “Fully trust” are positive
compared to the baseline (Log Odds = 0.81 and 1.88 respectively).
Worries Of the worries about future health, economy, andsocial life, only the health scale was included in the final
model. All scale points of this scale are significant and show
proportional positive log odds compared to the baseline of“No worries about health” (Log Odds in order of rising concern: 0.53, 0.76, and 1.21). This hints at a positive correlation
between future health concerns and app usage intention.

Usage intention All potential properties were rated from
“Definitely would use it” to “Definitely would not use it”.
The answers of the participant differ visibly based on thepreviously stated general usage intention of the app as it wasgoing to be released, i.e., participants who stated that theywould want to install the app were more positive about all thepotential properties than those who stated that they did notwant to use the app and vice versa. We tested this observation
with Kruskal-Wallis tests. The results show medium to large
effects for all 23 potential properties [50]. This means that perproperty, there is at least one group that differs from the othersin their rating. To find out more, we ran pairwise Wilcoxon
rank-sum tests and corrected the p-values with a Bonferroni
correction. The results can be seen in Table 9. The poles(“Definitely will use the app” and “Definitely will not use
the app”) of the installation intention differ from all othergroups for each property. Most but not all of the other groupcomparisons also show a statistically significant difference.
To assess the impact of each property, we report for eachgroup whether the given answer suggests a positive, negative,
or no change for the previously stated general intent to use
the app. To clarify, if a participant stated that they wanted
to install the to-be-released app, then any potential property
which was rated “Definitely would use it”, “Probably would
be willing to use it” and “No influence on my willingness”
would lead to no change in their intention and we summarize
that as: “No change”. However, for the same group, a property
rated as either “Definitely would NOT use it” or “Probably
would NOT be willing to use it” could lead to a negative effecton the previously positive attitude. We rated these properties
as “negative change”. The same goes for participants whose
general usage intention was negative. Any negative properties
would lead to “no change” while a positive property mightlead to a “positive change”. Participants who stated they were

Correlation with beliefs As previously reported, we identified many misconceptions. One of them ((SLF) GOVERNMENTSEES QUARANTINE VIOLATION) has a negative impact on the
installation intention. Two other attributes that also negatively
correlate with it are attributes that can neither be classified
as correct or incorrect but are based on personal sentiment:(GEN) THREATS PRIVACY (Log Odds = −1.33) and (GEN)RESTRICTS BASIC RIGHTS (Log Odds = −1.32). (GEN)USES LOCATION SERVICES likely is an overestimation of thefunctionality and correlates positively with the intent to install. Another positive correlating attribute is (GEN) FIGHTSDISEASE SPREAD (Log Odds = 0.55). Its correctness is hardto measure, as there is no central entity that keeps records of
how many people were warned by the app and thus ultimately
prevented the spread of COVID-19.
Demographics & Personal Experiences We also were interested in which demographic factors and personal experiences with COVID-19 influence participants’ decision to use
the CWA. We found a statistically significant effect for “Notknowing” whether oneself or someone close was infectedby COVID-19. There were negative log odds compared tothe baseline of not being infected (Log Odds = −0.84). This
could be due to a “Don’t care” (instead of “Don’t know”)
effect.

4.4

Potential Properties

As mentioned in Section 3, we presented the participants different hypothetical statements and consequences of the app(potential properties), asking whether and how that would
8

Figure 2: All presented potential properties and the distribution of the ratings of how these would influence the usage intention.
* indicates that they apply to the real app. D = decentralized, C = centralized, B = both, “-” = not included in either app design
TO NORMAL (Log Odds = 1.17) and (PP) FASTER ECONOMYRECOVERY (Log Odds = 0.81).

undecided could be swayed in either direction, so only properties rated with “No influence on my willingness” were rated
with “no change”, and the other received either a positive or
negative rating.
We can see large differences between the usage intention
groups (cf. Figure 6 to Figure 10), especially when looking
at the poles of the intention: participants who reported todefinitely not use the app (Def-No) (Figure 6) are seldom
really positive about any property. In contrast, participants
who reported to definitely use the app (Def-Yes) (Figure 10)
are seldom very negative about any property. While we canmake no causal claims, the polarisation is noteworthy.
To better assess the individual effects of the different potential properties, we built an ordinal regression model basedon a combined score of app usage intention and changes inintention due to these properties (cf. Table 3). Care needs tobe taken when interpreting the regression model. Its intention
is to highlight the direction of change as described above.
However, since both the dependent and independent variables
are non-equidistant and contain very strong poles (definitively
use/definitively not use), the log odds should probably be seen
as an upper bound of the change and needs to be used withcare.

Two properties that apply to the app impacted the participants negatively: (PP) HACKERS KNOW INFECTIONSTATUS (Log Odds = −1.48) and (PP) UNNECESSARYTESTING DUE TO FALSE POSITIVE WARNING (Log Odds
= −0.87). The potential of being exposed by a hacker
exists [47], but there are methods to mitigate this threat [20].
The risk for unnecessary testing applies to the app, but this
could happen without the app and in both the central anddecentral approaches.
Eleven potential properties do not apply to the app,of which five have a statistically significant positive influence on the app usage Three of them belong to the centralized approach and offer the Robert
Koch-Institute (RKI) additional insights: (PP) HELPS RKIASSESS SITUATION (Log Odds = 1.28), (PP) RKI SEES
MY CONTACTS TO INFORM OTHERS (Log Odds = 1.12)
and (PP) RKI SEES INFECTED’S CONTACTS TO INFORM
ME (Log Odds = 1.20). (PP) INFORMS MY CONTACTS IF
INFECTED (Log Odds = 1.15) includes the additional featureof warning users automatically if they had been in contact
with an infected person. Currently, users have to actively share
their positive test results if they want others to be warned [26].

Twelve of the potential properties apply to the to-bereleased app. Nine of those have a positive effect onusage intention, e.g., (PP) WARNS ME IF EXPOSED TO
COVID (Log Odds = 1.52) and (PP) INFORMS OTHERWISE
UNINFORMED USERS (Log Odds = 1.01). Both concern thefact that the app would notify users if they could have been
at risk of contracting COVID-19. This was the main featureof the app as communicated to the population. Additionally,
the intention to install the app increased if it would help returning to a pre-COVID-19 situation: (PP) FASTER RETURN

Three potential properties that do not apply to theapp had a negative influence on the installation intention. Two of them ((PP) RKI SEES DISTANCEVIOLATION (Log Odds = −0.87) and (PP) ONLY LAWPREVENTS SURVEILLANCE (Log Odds = −0.53)) open
up the possibility of using the app for surveillance
9and can fall into the centralized approach; one ((PP)
UNNECESSARY QUARANTINE DUE TO FALSE POSITIVEWARNING (Log Odds = −1.34)) could be seen as a clear
disadvantage for the individual user.

did not believe the CWA uses location services regarding
their general usage intention. 49.8 % of 197 vs 49.7 % of 298.
We then also checked whether participants rated the potential
properties more positive if they previously indicated that theCWA would use location services. Table 2 shows the percentages of participants who were positively influenced by theproperty (i.e., answered “Probably would install it” or “Definitely would install it”), split by the general usage intention.
As can be seen, the belief that the CWA uses location data did
not positively affect the participants’ sentiment when being
asked how not enabling the government to see their current
location would influence them.

Trust in different entities Some potential properties areconnected to measures taken that should build trust regarding the CWA, regardless of the apps’ design choices. These
measures included different levels of (data) protection by law,
experts testing the app, and the possibility to access the code
itself.
As can be seen in Table 3, the idea to protect the data by a
new law ((PP) DATA PROTECTED BY NEW LAW (Log Odds
= 0.73)) as well as the existing protection by the GDPR((PP) DATA PROTECTED BY GDPR (Log Odds = 0.88)) had
a positive influence on the intention to use the CWA. Additionally, the technical protection of the data positively influenced the participants ((PP) TECHNICAL PROTECTION OFDATA (Log Odds = 1.00)). However, the participants did notseem to like the idea that the government would only be hindered by law to misuse the data for surveillance ((PP) ONLYLAW PREVENTS SURVEILLANCE (Log Odds = −0.53)).
It was also rated positively if the CWA would be testedby the German Federal Office for Information Security (BSI)((PP) TESTED BY BSI (Log Odds = 0.9)) and experts ((PP)
TESTED BY IT EXPERTS (Log Odds = 1.12)).
Interestingly unlike the expert discussion would have suggested, (PP) CODE IS OPEN SOURCE did not have a positiveeffect.
The influence of this property is not statistically significant
and it received the most “I don’t know” answers comparedto all other properties. Even though the terminology “OpenSource” is also used in Germany and was communicated inthis way in the press release [38] in order to create transparency and trust, we believe many participants lacked anunderstanding of “Open Source”. It thus does not yet seem tohave the positive image the technical community would likeit to have.

5

Discussion

In the following section, we discuss our results, connect themwith previous work, and propose directions for future research.

5.1

Participant Beliefs

The majority of the participants knew something about the
CWA: Only 5.0% were not able to mark any of the correct
app features as true, and the basic idea behind the CWA (thatit would warn users with a risk of infection) was known by
59.5% (see Figure 4).
Bluetooth and Location Services We saw a lot of missing
information. The technical details that the CWA would use
Bluetooth were only known by 43.5%. Interestingly, 30.0%of the participants with some knowledge thought the CWA
would use location services but not Bluetooth. . While this
topic was discussed quite extensively in the media [10, 40],
many people did not seem to think that the CWA would do
tracing without GPS or the like. We also hypothesize thatmany who caught the term “Bluetooth” in the debate did not
eliminate GPS from their mental model of the CWA. Anotherelement that could get mixed up with information about theCWA might be the use of cellular network data to measurechanges in mobility at the population level, as introduced
earlier in the year [34]. Interestingly, we did not see any correlation between the assumption that the CWA uses location
services and the usage intention.

Perception of location services (PP) LOCATION NOTCOLLECTED had a statistically significant positive influenceon the installation intention (Log Odds = 1.10). (PP) USES
MY LOCATION TO PROTECT OTHERS did not have a significant influence. At the beginning of the survey, we asked
participants whether they believed the CWA would use location services. As a reminder: using the users’ position wasneither the case for the CWA nor was it communicated at
any point. However, as mentioned in Section 4.2, the survey
question asked about “location services”, and Bluetooth may
be known as such; therefore, participants could have interpreted it this way. We looked at whether the aspect of location
services would make a difference for the installation intention.
We compared the participants who a) thought that the CWA
would use location services but not Bluetooth to b) those who

Infected Persons Nearby 57.5 % of the participants believed the CWA would warn its users if an infected person is
nearby. This was also found by Thomas et al. [66], who studied participants’ knowledge regarding the already released
Australian app and who found 57.4% of their participants
believed this. It was also the most common misconception
found by Williams et al. [72] (conducted in the UK). This belief seems very common, even if it was never planned nor (toour knowledge) communicated through official channels that10

the CWA would be able to warn users of infected persons intheir vicinity directly. We are unaware of work that provides
insight into why people assume this to be true. However, we
hypothesize that many people mixed the two possible app features of being warned afterward and being alerted in real-time.
With an incorrect understanding of how contacts are captured
and in which cases the infection status is sent or downloaded,
the belief that the CWA could provide real-time warnings is
not too far-fetched. Future research should investigate if suchvital differences can be communicated, maybe even without
going into technical details. It should be noted that this is anoverestimation of the CWA’s functionality and could lead toincautious behavior based on a false sense of security.

this absence with a higher usage intention even when previously thinking this would be the case. For this, we have two
possible plausible explanations: a) people do not care aboutlocation service usage or b) other factors override concerns,
e.g., believing in the necessity of the CWA.
Both hypotheses are valuable input for the HCI-communityand should be further investigated.
Depending on this, it should be evaluated whether conjoint
studies are reliable methods to measure possible acceptance inthis domain and how the complexity of reality can be included(i.e., incomplete information or consequential thinking). It
seems essential to know the participants’ attitudes to the real
objective of interest (in our case, the tracing app).
Whether to install the CWA or not seems primarily based on
the sentiment of trust and the expectancy of a positive effect.
This shows that it is important not only to develop trustworthy
technologies but also to communicate their trustworthinessand effectiveness successfully. Technical measures aimed atcreating trust do not automatically result in such (e.g., as seen
for the CWA’s open source property).

Privacy Concerns 27% of the participants believed theCWA would restrict their basic rights or threats their privacy.
These beliefs had significant negative influences on usage
intention. Related work found that one of the reasons participants did not want to use an app was because they feareddata misuse or surveillance [59, 68]. Some even thought theywould receive the names of infected persons [73]. Since theGerman app (CWA) follows the decentralized approach, only
very little data is sent to a central server. While privacy concerns may be valid, we believe many participants did not
follow the discussion enough to understand that data storage
criticism only concerned the centralized approach. The decentralized app, which was being implemented, stored very
little data centrally. We hypothesize they project the worries
around the centralized app onto the decentralized one, even
if not all concerns are plausible for this approach. Future research is needed to investigate how old mental models canbe updated when the underlying system changes and what
influences privacy perception. Although, usage intention does
not seem to be driven by knowledge about technical details.

5.2

Demographic Factors

A study by Utz et al. [68] was conducted at the same timeas this study in Germany and can thus be used to compare
the results directly. While the authors conducted an experiment about hypothetical apps and how a tracing app could
or should be built, we asked about an app that had been officially announced with a detailed description of features andwas near launch. We can confirm part of their findings: We
found a positive influence on the willingness to use a corona
app a) if the opinion on state government was favorable, b) if
participants were concerned about their health, and c) return
to a normal life are possible due to the app. Participants withprivacy concerns were less likely to use the app, which we
can also confirm.

Usage Intention We looked at participants’ knowledge andbeliefs and how they are connected to participants’ intentions to use the CWA. Only two attributes to which a correctness value can be assigned had a statistically significant impact on the participants’ willingness to use the CWA.
The misconception (OTH) GOVERNMENT SEES QUARANTINEVIOLATION had a negative impact, the correct attribute (SLF)
INFORMS MY CONTACTS a positive one. The belief that one’s
privacy or basic rights were in danger lowered the willingness
significantly. It increased if participants thought the CWA
would help fight the spread. These assumptions do not reflectknowledge about the app but are based on personal estimations.
As discussed in Section 4.2, (GEN) USES LOCATIONSERVICES is technically correct in some cases. If participants
marked this attribute to be true for the CWA, they were significantly more willing to install the app. Even though theabsence of location services as a potential property had a positive influence on using the CWA, participants did not value

5.3

Never ever or no matter what

Like in our study, other researchers identified participants who
did not like any app, regardless of its design choices [58, 62,68]. We can confirm this finding. Participants within the DefNo group were mostly negative about any of the presented
potential properties. 7.1% did not rate a single presented
potential property as a positive change. This is similar to thereported 15-21% by Utz et al. [68]. We also saw the exact
opposite: Participants belonging to the Def-Yes group rated
every single theoretical additional aspect more positively than
all other groups. For all potential properties, participants fromthe installation intention poles (Def-Yes and Def-No) give
statistically significantly different answers compared to allother groups.
11

5.4

Centralized versus Decentralized

mitigate this threat [20], it is something that our participants
did not like. However, it did not feature significantly in the
public debate as far as we know and, as such, is unlikely tohave had much of an impact.

Large parts of the discussion around corona tracing apps
concerned the technical approach and whether encounters
between app users should be stored on a central server or theuser’s phone. Both approaches come with their advantages
and disadvantages. For instance, the centralized app could
give the RKI9 better insights into how people get infected.
Since the central database would be in charge of selecting
which users need to be warned, the RKI could see how
many people are warned per positive case. Since the risk iscomputed on the users’ devices in the decentralized app, theRKI does not know how many people receive warnings. In thecentralized app, it would also have been possible to track how
many other positive cases come out of any case, potentiallygiving more insights into how the virus spreads. On the other
hand, the decentralized approach does not facilitate getting
an overview but is also not in danger of being extended andmisused for surveillance. In general, the centralized app,
as it had been planned, offered more insights to healthcare
professionals but bore a higher risk of compromise andmisuse. However, it is worth noting that the decentralized
approach relies on making anonymized infection information
public on a central server. This opens the system up for local
deanonymization attacks. Suppose an attacker can capture
the ephemeral BT-IDs from a target and thus tying thoseIDs to that target. In that case, they can then monitor thesystem and see whether they report themselves as positive
or not. The German app was based on the decentralized
approach (see Section 2) due to public pressure to chosea more privacy-preserving approach. So in the context of
this study, we were especially interested in how participants
rate the possible benefits and dangers of a centralized app
and to see if the debate led by researchers and privacy
advocates well represented the feeling of the general public.
We included 6 potential properties (Q14) in the survey thatwere connected to the centralized approach (Table 7). central:
All in all, we saw a mix of sentiments. Three central potential
properties ((PP) RKI SEES INFECTED’S CONTACTS TO
INFORM ME, (PP) RKI SEES MY CONTACTS TO INFORMOTHERS, (PP) HELPS RKI ASSESS SITUATION) had astatistically significant positive influence on the intention
to install. All three concern individual or societal benefits.
Two other central properties ((PP) RKI SEES DISTANCEVIOLATION, (PP) ONLY LAW PREVENTS SURVEILLANCE)
impacted the intention to install negatively. Both focus on the
disadvantages of the centralized approach and do not haveany clear advantage for the individual user.
The decentral property (PP) HACKERS KNOW INFECTIONSTATUS impacted the participants in a negative way. While
this risk is limited to local attackers, and there are methods to

It seems participants are in general inclined to rate properties of the centralized approach positively while they rate theconsequences (in the current technical landscape) that comewith it rather low.
In summary, many of our participants had very positive
views concerning the increased capabilities the centralized
app would have had. This suggests that there could have been
more support in the population for a more feature-rich app
than academics and privacy advocates acknowledged in thediscussion preceding the CWA’s publication. Relevant healthofficials have since stated that the app in its current form is no
great support [16], and due to the privacy design, it is hard toevaluate its efficacy. We think it is worth discussing whether
a more nuanced discussion about the feature/privacy trade-off
would be warranted for the future.

6

Conclusion

We surveyed the usage intention of the CWA in Germany rightbefore its launch. 50% of the participants reported their intent
to use the CWA, 26.3% refrained from usage and 23.4% were
undecided. This seems reasonably close to the most recent(May 28, 2021) download numbers. To understand their decision, we investigated what beliefs participants had about the
CWA. We saw many false beliefs, especially concerning technical details, i.e., 30.0% of the participants thought the CWA
would use location services (other than Bluetooth). Actual
knowledge about the CWA does not seem to be the primary
driver for the decision to use the CWA. Instead, perceived privacy or basic rights intrusions led to a lower intention to use
it. As also reported by other researchers, we found a positiveeffect when people were worried about general health andtrusted the government. We also highlight that the general
population’s views were more diverse and more open to acentral entity getting an overview to help fight the pandemic
than the public discussion indicated. Based on our results, werecommend future work on a) where the privacy concerns
come from, as in our view many of the concern did not matchthe actual CWA and b) how the perceptions can be alignedwith the actual facts of the CWA, as this is necessary to discuss features based on the facts. And c) whether the CWA
can be extended in a way that it becomes more useful to therelevant parties, e.g., the public health departments, while atthe same time implementing technical countermeasures toprevent the data from being abused.

9 According to their website, "The Robert Koch Institute (RKI) is thegovernment’s central scientific institution [a federal government agency]
in the field of biomedicine. It is one of the most important bodies for thesafeguarding of public health in Germany." [31]12Acknowledgements

corona-warn-app/corona-apps-im-ueberblick/.
Accessed: May 26, 2021.

We thank, in alphabethical order: Yomna Abdelrahman, Ruba
Ali Mahmoud Abu-Salma, Florian Alt, Zinaida Benenson, Nataliia Bielova, Freya Gassmann, Katharina Krombholz, Mattia
Mossano and Melanie Volkamer for their insightful discussions and helpful remarks on the survey. This work was partially funded by the Werner Siemens Foundation.

[10] Corona-Pandemie: Alles Wissenswerte rund um die
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keine persönlichen Kontaktdaten.
https://correctiv.org/faktencheck/2020/06/25/
nein-die-corona-warn-app-nutzt-keinepersoenlichen-kontaktdaten/. Accessed: February 17, 2021.

[8] CoroBlog zur wissenschaftlichen Auswertung derCorona-Datenspende-App.
https://www.rki.de/
DE/Content/InfAZ/N/Neuartiges_Coronavirus/
Corona-Datenspende.html. Accessed: January 26,
2021.

[19] Fünf Monate nach dem Start: Die Tücken der CoronaWarn-App. https://www.tagesschau.de/inland/
faq-corona-warn-app-101.html. Accessed: February 17, 2021.

[9] Corona-Apps
imÜberblick–
Digitale
Informations- und Hilfsangebote.
https://www.zusammengegencorona.de/informieren/13[20] Google Exposure Notification Key Server. https://google.github.io/exposure-notificationsserver/server_functional_requirements.html.
Accessed: May 28, 2021.

[30] Risikogruppen sind überall.
https://
de.statista.com/infografik/21145/groessevon-ausgewaehlten-risikogruppen-indeutschland/. Accessed: February 23, 2021.

[21] Help speed up contact tracing with TraceTogether.
https://www.gov.sg/article/help-speed-upcontact-tracing-with-tracetogether. Accessed:
February 09, 2021.

https://www.rki.de/EN/[31] RKI - The Institute.
Content/Institute/institute_node.html.
Accessed: February 15, 2021.
[32] Statistiken zur Smartphone-Nutzung in Deutschland.
https://de.statista.com/themen/6137/
smartphone-nutzung-in-deutschland/. Accessed:
February 23, 2021.

[22] Kennzahlen
zurCorona-Warn-App.
https://www.rki.de/DE/Content/
InfAZ/N/Neuartiges_Coronavirus/
WarnApp/Archiv_Kennzahlen/
Kennzahlen_28052021.pdf?__blob=
publicationFile. Accessed: June 1, 2021.

Corona-Appidemie stoppen!
[33] Sylvia Limmer:
https://www.afd.de/sylvia-limmer-coronaappidemie-stoppen/. Accessed: May 28, 2021.

[23] Offener Brief: Geplante Corona-App ist höchst
problematisch.
http://www.fiff.de/presse/
coronaappproblematisch. Accessed: January 28,2021.

[34] Süddeutsche Zeitung GmbH.
https://sz.de/
1.4850094. Accessed: February 25, 2021.
[35] Timeline of ECDC’s reponse to COVID-19.
https://www.ecdc.europa.eu/en/covid-19/
timeline-ecdc-response. Accessed: February 09,2021.

[24] Pan-European
Privacy-Preserving
Proximity
Tracing.
https://github.com/pepp-pt/
pepp-pt-documentation/blob/master/10data-protection/PEPP-PT-data-protectioninformation-security-architectureGermany.pdf. Accessed: January 22, 2021.

[36] Transkript:
Erklärfilm
zur „Corona-WarnApp“.
https://www.bundesregierung.de/resource/blob/1726066/1760258/
c4247487c176123b13003e6125ead7aa/2020-0610-corona-warn-app-de-textversion-data.pdf.
Accessed: February 17, 2021.

[25] Pläne von Minister Spahn - Kritik an CoronaApp wächst. https://www.tagesschau.de/inland/corona-app-spahn-101.html. Accessed: January 28,2021.

[37] Version 1.11: Corona-Warn-App nun mit Kennzahlen zum Infektionsgeschehen.
https://www.coronawarn.app/de/blog/2021-01-28corona-warn-app-version-1-11/.
Accessed:
February 15, 2021.

[26] Poster: Positiv getestet? So teilen Sie Ihr Ergebnisüber die Corona-Warn-App. https://www.rki.de/
DE/Content/InfAZ/N/Neuartiges_Coronavirus/
WarnApp/Poster.pdf?__blob=publicationFile.
Accessed: June 09, 2021.

[38] Veröffentlichung der Corona-Warn-App.
https://www.bundesregierung.de/breg-de/themen/coronavirus/veroeffentlichung-der-coronawarn-app-1760892. Accessed: February 01, 2021.

[27] Pressemitteilung des Bundesministeriums für Gesundheit, des Bundesministeriums des Innern, für Bau
und Heimat und des Bundeskanzleramts zum Projekt "Corona-App" der Bundesregierung.
https://www.bundesregierung.de/breg-de/aktuelles/pressemitteilung-des-bundesministeriumsfuer-gesundheit-des-bundesministeriumsdes-innern-fuer-bau-und-heimat-und-desbundeskanzleramts-zum-projekt-corona-appder-bundesregierung-1747916. Accessed: January
28, 2021.

[39] "Vorbildlichgelaufen"-ChaosComputerClub lobt deutsche Corona-App.
https://www.zdf.de/nachrichten/politik/coronaapp-launch-100.html. Accessed: February 17, 2021.
[40] Wann kommt die App, die hilft?
https://www.zeit.de/digital/datenschutz/2020-04/
corona-app-tracking-handydaten-bluetoothdatenschutz. Accessed: February 19, 2021.

[28] Privacy notice CWA. https://www.coronawarn.app/
assets/documents/cwa-privacy-notice-en.pdf.
Accessed: February 23, 2021.

[41] Washington Post-University of Maryland national poll, April 21-26, 2020.
https://
web.archive.org/web/20200501144955if_/https://www.washingtonpost.com/context/washington-[29] Qualtrics. qualtrics.com. Accessed: January 22,
2021.
14

[52] M. Guillon and P. Kergall. Attitudes and opinions on
quarantine and support for a contact-tracing applicationin France during the COVID-19 outbreak. Public health,188:21–31, 2020.

post-university-of-maryland-national-pollapril-21-26-2020/3583b4e9-66be-4ed6-a457f6630a550ddf/. Accessed: February 10, 2021.
[42] Wenn am nächsten Sonntag Bundestagswahl wäre ...
https://www.wahlrecht.de/umfragen/forsa.htm.
Accessed: January 27, 2021.

[53] Eszter Hargittai, Elissa M. Redmiles, Jessica Vitak, andMichael Zimmer. Americans’ willingness to adopt a
COVID-19 tracking app. First Monday, 2020.

derCorona-Nachverfolgung:[43] Zwischenbilanz
Wie erfolgreich funktionieren Corona-Apps inder EU?
https://www.treffpunkteuropa.de/zwischenbilanz-der-corona-nachverfolgungwie-erfolgreich-funktionieren.
Accessed:
February 23, 2021.

[54] Farkhondeh Hassandoust, Saeed Akhlaghpour, andAllen C. Johnston. Individuals’ privacy concerns andadoption of contact tracing mobile applications in a pandemic: A situational privacy calculus perspective. Journal of the American Medical Informatics Association,
2020.

[44] “Decentralised Privacy-Preserving Proximity Tracing. https://github.com/DP-3T/documents/blob/master/DP3T%20White%20Paper.pdf. Accessed: January 22, 2021.

[55] Robert Hinch, Will Probert, Anel Nurtay, Michelle
Kendall, Chris Wymant, Matthew Hall, Katrina Lythgoe, Ana Bulas Cruz, Lele Zhao, Andrea Stewart, et al.
Effective configurations of a digital contact tracing app:
A report to nhsx. Retrieved July, 23:2020, 2020.

[45] Samuel Altmann, Luke Milsom, Hannah Zillessen, Raffaele Blasone, Frederic Gerdon, Ruben Bach, Frauke
Kreuter, Daniele Nosenzo, Séverine Toussaert, and Johannes Abeler. Acceptability of app-based contact tracing for COVID-19: Cross-country survey study. JMIR
mHealth and uHealth, 8(8):e19857, 2020.

[56] Kai T. Horstmann, Susanne Buecker, Julia Krasko, Sarah
Kritzler, and Sophia Terwiel. Who does or does not use
the “Corona-Warn-App” and why? European Journalof Public Health, 2020.
[57] Laszlo Horvath, Susan Banducci, and Oliver James. Citizens’ Attitudes to Contact Tracing Apps. Journal of
Experimental Political Science, pages 1–13, 2020.

[46] Patrik Bachtiger, Alexander Adamson, Jennifer K.
Quint, and Nicholas S. Peters. Belief of having had
unconfirmed Covid-19 infection reduces willingness toparticipate in app-based contact tracing. NPJ digital
medicine, 3(1):1–7, 2020.

[58] Gabriel Kaptchuk, Eszter Hargittai, and Elissa M. Redmiles. How good is good enough for COVID19 apps?
The influence of benefits, accuracy, and privacy on willingness to adopt. arXiv preprint arXiv:2005.04343,
2020.

[47] Lars Baumgärtner, Alexandra Dmitrienko, Bernd
Freisleben, Alexander Gruler, Jonas Höchst, Joshua
Kühlberg, Mira Mezini, Markus Miettinen, Anel
Muhamedagic, Thien Duc Nguyen, et al. Mind the gap:
Security & privacy risks of contact tracing apps. arXiv
preprint arXiv:2006.05914, 2020.

[59] Genia Kostka and Sabrina Habich-Sobiegalla. In Timesof Crisis: Public Perceptions Towards COVID-19 Contact Tracing Apps in China, Germany and the US. Germany and the US (September 16, 2020), 2020.

[48] Fabian Buder, Anja Dieckmann, Vladimir Manewitsch,
Holger Dietrich, Caroline Wiertz, Aneesh Banerjee,
Oguz A. Acar, and Adi Ghosh. Adoption Rates for
Contact Tracing App Configurations in Germany, 2020.

[60] Wassili Lasarov. Im Spannungsfeld zwischen Sicherheit
und Freiheit. HMD Praxis der Wirtschaftsinformatik,
pages 1–18, 2020.
[61] Tianshi Li, Camille Cobb, Sagar Baviskar, Yuvraj Agarwal, Beibei Li, Lujo Bauer, Jason I. Hong, et al. What
Makes People Install a COVID-19 Contact-Tracing
App? Understanding the Influence of App Design andIndividual Difference on Contact-Tracing App Adoption
Intention. arXiv preprint arXiv:2012.12415, 2020.

[49] Kenneth P. Burnham and David R. Anderson. Multimodel inference: understanding AIC and BIC in model
selection. Sociological methods & research, 33(2):261–
304, 2004.
[50] Jacob Cohen. Statistical power analysis for the behavioral sciences. Academic press, 2013.

[62] Tianshi Li, Cori Faklaris, Jennifer King, Yuvraj Agarwal,
Laura Dabbish, Jason I. Hong, et al. Decentralized
is not risk-free: Understanding public perceptions ofprivacy-utility trade-offs in COVID-19 contact-tracing
apps. arXiv preprint arXiv:2005.11957, 2020.

[51] Jemima A. Frimpong and Stephane Helleringer. Financial Incentives for Downloading COVID–19 Digital
Contact Tracing Apps. SocArXiv 9vp7x, Center forOpen Science, June 2020.
15

[63] Michael E. O’Callaghan, Jim Buckley, Brian Fitzgerald, Kevin Johnson, John Laffey, Bairbre McNicholas,
Bashar Nuseibeh, Derek O’Keeffe, Ian O’Keeffe, Abdul
Razzaq, et al. A national survey of attitudes to COVID19 digital contact tracing in the Republic of Ireland. Irish
Journal of Medical Science, pages 1–25, 2020.

[72] Simon N. Williams, Christopher J. Armitage, Tova
Tampe, and Kimberly Dienes. Public attitudes towards
COVID-19 contact tracing apps: A UK-based focus
group study. Health Expectations, 2020.
[73] Baobao Zhang, Sarah Kreps, Nina McMurry, and
R. Miles McCain. Americans’ perceptions of privacy
and surveillance in the COVID-19 pandemic. Plos one,15(12):e0242652, 2020.

[64] Elissa M. Redmiles. User Concerns & Tradeoffs inTechnology-facilitated COVID-19 Response. Digital
Government: Research and Practice, 2(1):1–12, 2020.

A[65] Lucy Simko, Jack L. Chang, Maggie Jiang, Ryan Calo,Franziska Roesner, and Tadayoshi Kohno. COVID-19
Contact Tracing and Privacy: A Longitudinal Study of
Public Opinion. arXiv preprint arXiv:2012.01553, 2020.

Englisch Survey

Screening Questions• Q1 What is your age?
[Free Text]• Q2 In which federal state do you live?

[66] Rae Thomas, Zoe A. Michaleff, Hannah Greenwood,
Eman Abukmail, and Paul Glasziou. Concerns andMisconceptions About the Australian Government’s
COVIDSafe App: Cross-Sectional Survey Study. JMIR
public health and surveillance, 6(4):e23081, 2020.

• Q3 Do you use a smartphone?
[Yes, an Android / Yes, an IPhone / Yes, another smartphone / Yes, but
I don’t know which / No / I don’t want to state]• Q4 What is your netto household income?
[<= 1300 / 1300-1700C / 1700-2600C / 2600-3600C / 3600-5000C/ > 5000 / I don’t want to state][67] Simon Trang, Manuel Trenz, Welf H. Weiger,
Monideepa Tarafdar, and Christy MK Cheung. One
app to trace them all? Examining app specifications formass acceptance of contact-tracing apps. European
Journal of Information Systems, 29(4):415–428, 2020.

• Q5 What is the number of individuals living in your household?
[1 / 2 / 3 / 4 or more / I don’t want to state]• Q6 What is the highest-level vocational qualification you hold?
[Completed apprenticeship / Other; Vocational qualification: / University degree / Master or Technician certification or equivalent technicalschool diploma / Vocational school diploma / Technical school diploma/ No vocational qualification / Technical college degree (or engineeringschool diploma) / I don’t want to state / Abitur (German university
entrance qualification)][68] Christine Utz, Steffen Becker, Theodor Schnitzler, Florian M. Farke, Franziska Herbert, Leonie Schaewitz,
Martin Degeling, and Markus Dürmuth. Apps against
the spread: Privacy implications and user acceptance ofcovid-19-related smartphone apps on three continents.
In Proceedings of the 2021 CHI Conference on Human
Factors in Computing Systems, CHI ’21, New York, NY,
USA, 2021. Association for Computing Machinery.

App Description and Media Sources
The COVID-19 coronavirus pandemic is a worldwide problem. The Corona
warning app for Germany is one of the measures planned to assist healthauthorities in tracing and containing infection, being developed by SAP torun on Deutsche Telekom infrastructure. The Robert Koch Institute (RKI)
will publish the app when it is ready. It is also referred to as the ‘Corona app’,‘COVID app’ or ‘contact tracing app’.

[69] Viktor von Wyl, Marc Hoeglinger, Chloe Sieber, Marco
Kaufmann, Andre Moser, Miquel Serra-Burriel, Tala
Ballouz, Dominik Menges, Anja Frei, and Milo A.
Puhan. Drivers of acceptance of COVID-19 proximity tracing apps in Switzerland. medRxiv, 2020.

• Q7 Have you heard of the plans for this app? If ‘yes’, please selectwhere you heard about the app. Multiple selections possible.
[Public broadcasters (ARD, ZDF, WDR, etc.) / Non-public TV (Pro7,
Vox, N24, etc.) / Scientific publications / Newspapers, journals, magazines, etc. / Family member / Official government/state agency websites(Robert Koch Institute, Federal Government, etc.) / Other websites:/ I have not heard about this app / Friends / Social media (Twitter,
Facebook, YouTube, TikTok, etc.) / Work colleagues/associates / Don’tknow/I don’t want to state / Official Corona Warning App website[70] Michel Walrave, Cato Waeterloos, and Koen Ponnet.
Ready or Not for Contact Tracing? Investigating the
Adoption Intention of COVID-19 Contact-Tracing Technology Using an Extended Unified Theory of Acceptance and Use of Technology Model. Cyberpsychology,
Behavior, and Social Networking, 2020.

Knowledge• Q8 Which of the below statements do you think will apply regarding
the app? (please check all that apply.)[71] Caroline Wiertz, Aneesh Banerjee, Oguz A. Acar, and
Adi Ghosh. Predicted Adoption Rates of Contact Tracing App Configurations-Insights from a choice-based
conjoint study with a representative sample of the UK
population. Available at SSRN 3589199, 2020.

– The app uses Bluetooth.
– Through the app I can donate health data to the Robert Koch
Institute for research purposes.
– The app determines when other smartphones are nearby thatare also using the app.

16

– The app shares temporary IDs and timestamps.

Install General– The app enables the government to see my current location.

In answering the following questions, please imagine that the app describedabove has already been released. The app is being developed by SAP to run
on Deutsche Telekom infrastructure. The Robert Koch Institute (RKI) is incharge of the app and evaluates the data. The exclusive permissible usage ofthe data is to fight COVID-19.

– The app enables the government to see if people are not keeping
a safe distance from others.
– Usage of the app will be mandatory.
– The app shares the names and phone numbers of my contacts
with the government.

• Q12 How likely is it that you will use the app?
[Definitely will use it / Probably will use it / Undecided / Probably will
not use it / Definitely will not use it / Response declined / Don’t know]– The app infringes my basic rights.
– The app can be used to demonstrate to others that I am notcurrently COVID-19 positive.

• Q13 What is the primary reason for your answer?
[Free text]– The app facilitates decision-making on who should be tested
for COVID-19.

Potential Properties– The app shares fitness data.
– The app can help fight the spread of the COVID-19 virus.

Q14 You will now be presented with 24 statements. These statements concern
characteristics or things that could apply or be true with the app. Please selecthow these statements, if true, would influence your willingness to use theapp.
[Definitely would use it / Probably would be willing to use it / No influence
on my willingness / Probably would not be willing to use it / Definitely would
not use it / Don’t know]– The app uses location services (like GPS).
– The app shares a profile of my movement.
– None of the above applies.
– Don’t know
– The app undermines my privacy.
• Q9 What statements do you think apply regarding the app when otherusers are COVID-19 positive? (please check all that apply.)• The government would be prevented by law, but not by technical means,from misusing the data for surveillance purposes.

– The app enables the government to see if someone is not complying with quarantine orders.

• Using the app would enable the RKI to find out if I am not complying
with minimum distancing to other individuals.

– The app notifies me if I have had contact with an individual
who later tested positive for COVID-19.

• The RKI would have a database with the contact data of infectedindividuals and the people they have had contact with.

– The app notifies me when an infected person is located nearby.

• If I test positive for COVID-19, the app would allow the RKI to see
who I had contact with in order to notify those individuals– None of the above applies.
– Don’t know

• The German Federal Office for Information Security (BSI) would verify that the app fulfills data security and data protection requirements.

• Q10 What statements do you think apply regarding the app when you
yourself are COVID-19 positive? (please check all that apply.)• Using the app would make possible a speedier return to normal public
life.

– The app informs other app users who have been close to me
that they may have contracted the virus.

• Technical measures would be implemented to ensure the data areprotected.

– The app sends data continuously to the RKI.
– A physician or the public health authority has to confirm mypositive COVID-19 test result before the app sends data to the
RKI.

• There is a possibility that the app could incorrectly report infection
risk, resulting in me having to quarantine unnecessarily• Using the app would help re-start the economy faster– The app enables the government to see if I am not complying
with quarantine orders.

• If the app notifies me that I may have been infected, I would have berequired by law to quarantine.

– None of the above applies.

• The app would notify me if I have been in a situation putting me atrisk of contracting COVID-19.

– Don’t know

• There is a possibility that the app could incorrectly report infection
risk, resulting in me having to get tested unnecessarily

App Description and Comprehension
A brief introduction is provided below on the planned capabilities of
the contact tracing app. The federal government intends to introduce asmartphone app to trace COVID-19 transmission in the near future. The app
is to be very user-friendly and its usage voluntary. The app is designed toensure that virus transmission is detected more quickly. This allows taking
targeted containment measures.
When in use, the app determines what other users of the app are located nearyou. The app does this via Bluetooth. The app will alert you if you have beennear someone within the past few days who subsequently tested positive for
COVID-19. The app then informs you of what you need to do next, such as
get tested for COVID-19.

• Independent security experts would verify that the app fulfills data
security and data protection requirements.
• The app would use information about my location to more accurately
monitor infection risk for others• Protection of the data would be guaranteed pursuant to a data protection
policy and the General Data Protection Regulation.
• The app would not collect any data about my location.
• The app would inform people of infection risk who would not otherwise be contacted by the public health authority.
• Any nearby hackers could find out if I have tested positive for COVID19.

• Q11 How will the described app determine what people have beennear me?
[Bluetooth / Location services (such as GPS) / My phone Contacts list
/ Don’t know]• This question pertains to attentive completion of the survey. Please
select “No influence” as response.

17• If somebody near me has tested positive for COVID-19, the app would
enable the RKI to see that I have had contact with that individual inorder to notify me accordingly.

• Q28 Has the Covid-19 pandemic resulted in you having to look after/care for someone at home?
[Yes / No / I don’t want to state]• Protection of the data would be guaranteed under a new law drafted
especially for the app• Q29 How has the crisis affected your work?
[Unaffected / Working from home / Short-time work / Became unemployed / Found employment / I don’t want to state]• If I have tested positive for COVID-19, the app would automaticallynotify other users of the app who are at risk being exposed throughcontact with me• The app would be open-source

Thank you!

• The app would support the RKI to better assess the COVID-19 situation.

To find out more about the coronavirus and how to best protect yourself andyour family, follow this link to the coronavirus info web pages of the Federal
Center for Health Education: https://www.infektionsschutz.de/coronavirus/
Thank you very much for participating in this survey! You can enter any
comments or your opinion on the app in the field below, or contact the survey
administrators directly via e-mail.

It is being discussed whether use of the app should be made mandatory incertain situations where people come in contact in groups, such as patronizing
restaurants or utilizing bus or train services, to facilitate targeted monitoringof infection risk. It must be considered however that roughly 20% of theGerman population would be excluded from using such services due to nothaving a smartphone.
• Q15 Would you approve or disapprove of such mandatory usage?
[Approve entirely / Mainly approve / Neither approve nor disapprove /Mainly disapprove / Disapprove entirely / Response declined / Don’t
know]

B

German Survey

Screening Fragen• Q1 Wie alt sind Sie?
[Freitext]

Demographics• Q16 What is your gender?
[Male / Female / Non-binary / Would like to self-describe: / I don’t
want to state]• Q2 In welchem Bundesland wohnen Sie derzeit?
• Q3 Nutzen Sie ein Smartphone?
[Ja, ein Android / Ja, ein IPhone / Ja, ein anderes / Ja, weiß aber nicht
was für eins / Nein / Möchte ich nicht sagen]• Q17 What is your work status?
[School student / University/college student / Employee / Civil servant/ Self-employed / Freelancer / Unemployed / Retiree / I don’t want tostate]• Q4 Was ist Ihr netto Haushaltseinkommen?
[<= 1300 / 1300-1700C / 1700-2600C / 2600-3600C / 3600-5000C/ > 5000 / Möchte ich nicht sagen]• Q18 Do you have specialized computing skills, such as: system administration, programming, IT security, tech support, power user, etc?
[Yes / No / I don’t want to state]• Q5 Wie viele Personen leben in Ihrem Haushalt?
[1 / 2 / 3 / 4 oder mehr / Möchte ich nicht sagen]• Q19 Please indicate your agreement or disagreement with the following: “I generally trust the government to do the right thing.”[Fully agree / Mostly agree / Neither agree nor disagree / Mostly
disagree / Fully disagree / I don’t want to state]• Q6 Was ist Ihr höchster beruflicher Abschluss?
[Abgeschlossene Lehre / Anderen; beruflichen Ausbildungsabschluss,
und zwar: / Hochschulabschluss / Meister-, Techniker- oder gleichwertiger Fachschulabschluss / Berufsfachschulabschluss / Fachschulabschluss / Keinen beruflichen Ausbildungsabschluss / Fachhochschulabschluss (auch Abschluss einer Ingenieurschule) / Möchte ich nicht
sagen / Abitur]• Q20 What party do you have the most affinity with?
[The Greens / CDU/CSU / SPD / FDP / AfD / The Left / Others/I don’t
want to state]• Q21 Currently, how frequently do you have close personal contact withpeople not from your household?
[Once a week at most / A few times a week / A few times a day / Several
times a day / I don’t want to state]

App Beschreibung und Nachrichtenquellen
Die derzeitige Coronavirus-Pandemie („COVID-19“) ist allgegenwärtig. Eineder geplanten Maßnahmen, um die Gesundheitsämter beim Nachverfolgen
vom Infektionsgeschehen zu unterstützen, ist die Corona-Warn-App für
Deutschland. Die App wird von SAP entwickelt. Die Deutsche Telekom
betreibt die Infrastruktur. Nach Fertigstellung der App wird das Robert KochInstitut (RKI) die App herausgeben. Diese App wird auch als Corona-App,
COVID-App oder Contact-Tracing App bezeichnet.

• Q22 How concerned or unconcerned are you about COVID-19 inregard to the following three areas?
Health, The economy, Society[Unconcerned / A bit concerned / Concerned / Very concerned / I don’t
want to state]• Q23 Do you fall within a COVID-19 high-risk group?
[Yes / No / Don’t know / I don’t want to state]• Q24 Does someone close to you fall within a COVID-19 high-risk
group?
[Yes / No / Don’t know / I don’t want to state]• Q7 Haben Sie schon von der geplanten App gehört? Wenn ja, wählen
Sie bitte aus, wo Sie Informationen über die App bekommen haben.
Mehrfachauswahl möglich.
[Öffentlich rechtliche Sender (ARD, ZDF, WDR, etc.) / Private Sender(Pro7, Vox, N24, etc.) / Wissenschaftliche Veröffentlichungen / Zeitungen, Zeitschriften, Magazine, etc. / Familie / Staatliche Webseiten(Robert Koch-Institut, Bundesregierung, etc.) / Webseiten, z.B.: / Ichhabe bisher nichts von der App gehört / FreundInnen / Soziale Medien(Twitter, Facebook, YouTube, TikTok, etc.) / KollegInnen / Weiß ich
nicht / Möchte ich nicht beantworten / Offizielle Corona-Warn-App
Homepage• Q25 Have you or any person close to you fallen ill with Covid-19?
[Yes / No / Don’t know / I don’t want to state]• Q26 Has anyone close to you died of Covid-19?
[Yes / No / Don’t know / I don’t want to state]• Q27 How has the Covid-19 pandemic affected you financially?
[Positive impact / No impact / Negative impact / Critical impact / I
don’t want to state]

18

Wissen

App Beschreibung und Verständnis• Q8 Welche Aussagen meinen Sie werden auf die App zutreffen? (Bittekreuzen Sie alle an, die zutreffen.)

Im Folgenden geben wir eine kurze Einführung über die geplanten
Fähigkeiten der Contact-Tracing App.
Die Bundesregierung plant, in naher Zukunft eine App für Smartphones zur
Nachverfolgung der Ansteckungen mit COVID-19 einzuführen. Die Nutzung
der App soll freiwillig und sehr einfach sein. Die App soll dabei helfen
Infektionen früher zu erkennen. Damit können gezielte Maßnahmen zur
Eindämmung ermöglicht werden.
Wenn Sie die App benutzen, würde die App feststellen, welche anderen
NutzerInnen der App in Ihrer Nähe sind. Hierfür würde die App Bluetoothnutzen. Die App kann Sie dann darüber informieren, falls Sie sich in den
letzten Tagen in der Nähe einer Person befunden haben, die später positiv
auf COVID-19 getestet wurde. Die App würde Sie dann darüber informieren
wie Sie sich verhalten sollen, z.B. sich auf COVID-19 testen zu lassen.

– Die App benutzt Bluetooth.
– Die App stellt fest, welche anderen Smartphones, die auch die
App nutzen, in der Nähe sind.
– Die Nutzung der App wird verpflichtend.
– Die App benutzt Ortungsdienste (wie GPS).
– Die App ermöglicht es mir, Gesundheitsdaten fürForschungszwecke an das Robert Koch-Institut zu spenden.
– Die App kann genutzt werden, um anderen zu zeigen, dass ich
aktuell nicht mit COVID-19 infiziert bin.

• Q11 Was wird die beschriebene App nutzen, um festzustellen wer sichin der Nähe aufgehalten hat?
[Bluetooth / Ortungsdienste (wie GPS) / Kontakte aus meinem Telefonbuch / Weiß ich nicht]– Die App ermöglicht es der Regierung zu sehen, wo ich michaktuell befinde.
– Die App bedroht meine Privatsphäre.
– Die App schränkt meine Grundrechte ein.

Installation der App– Die App kann helfen die Verbreitung des COVID-19 Virus zu
bekämpfen.

Stellen Sie sich für die folgenden Fragen bitte vor, dass die zuvor
beschriebene App bereits existiert. Die App wird von SAP entwickelt und
die Infrastruktur wird von der Deutschen Telekom betrieben. Das Robert
Koch-Institut (RKI) ist für die App verantwortlich und wertet die Daten aus.
Die Daten dürfen nur für die Bekämpfung von COVID-19 genutzt werden.

– Die App teilt Namen und Telefonnummern meiner Kontakte
mit der Regierung.
– Die App ermöglicht es der Regierung zu sehen, wenn Menschen
den Sicherheitsabstand zu Anderen nicht einhalten.
– Die App teilt temporäre IDs und Zeitstempel.

• Q12 Mit welcher Wahrscheinlichkeit würden Sie die App nutzen?
[Auf jeden Fall nutzen / Wahrscheinlich nutzen / Ich bin unentschieden/ Wahrscheinlich nicht nutzen / Auf keinen Fall nutzen / Möchte ichnicht sagen / Weiß ich nicht]– Die App teilt Fitnessdaten.
– Die App teilt ein Bewegungsprofil von mir.
– Die App hilft zu entscheiden, wer auf COVID-19 getestet werden sollte.

• Q13 Was ist der Hauptgrund für Ihre Antwort?
[Freitext]– Keine der genannten Eigenschaften.
– Weiß ich nicht

Mögliche Eigenschaften• Q9 Welche Aussagen glauben Sie treffen auf die App zu, wenn andere
NutzerInnen sich mit COVID-19 infiziert haben? (Bitte kreuzen Sie
alle an, die zutreffen.)

Q14 Im Folgenden werden wir Ihnen 24 Aussagen präsentieren. Diese Aussagen sind Eigenschaften, die die App haben könnte. Bitte wählen Sie aus,
wie diese Aussagen, wenn sie zutreffen würden, Ihre Bereitschaft die Appzu nutzen beeinflussen würden.
[Auf jeden Fall nutzen / Eher nutzen / Kein Einfluss / Eher nicht nutzen / Auf
keinen Fall nutzen / Weiß ich nicht]– Die App informiert mich, wenn eine infizierte Person in derNähe ist.
– Die App informiert mich im Nachhinein, wenn ich mit einer
Person Kontakt hatte, die später positiv auf COVID-19 getestet
wurde.

• Die App würde mich benachrichtigen, falls ich einem COVID-19
Ansteckungsrisiko ausgesetzt war.
• Falls ich positiv auf COVID-19 getestet werde, würde die App automatisch andere NutzerInnen der App benachrichtigen, die sich bei mir
anstecken konnten.

– Die App ermöglicht es der Regierung zu sehen, wenn jemandsich nicht an eine verordnete Quarantäne hält.
– Keine der genannten Eigenschaften.

• Die App würde Menschen über Ansteckungsrisiken informieren, die
darüber sonst nicht vom Gesundheitsamt informiert werden würden.

– Weiß ich nicht.
• Q10 Welche Aussagen glauben Sie treffen auf die App zu, wenn Sie
selbst positiv auf COVID-19 getestet wurden? (Bitte kreuzen Sie allean, die zutreffen.)• Die App würde dem RKI helfen die COVID-19 Lage in Deutschland
besser einzuschätzen.
• Die Nutzung der App würde es der Bevölkerung ermöglichen schnellerzum öffentlichen Leben zurückzukehren.

– Die App informiert andere NutzerInnen der App, die in meiner
Nähe waren, dass sie sich vielleicht angesteckt haben.

• Die Nutzung der App würde es ermöglichen, die Wirtschaft schneller
wieder hochzufahren.

– Ein Arzt / Eine Ärztin oder das Gesundheitsamt muss meinen
positiven COVID-19 Test bestätigen, bevor die Daten aus der
App an das RKI geschickt werden.

• Falls ich positiv auf COVID-19 getestet werde, würde die App es
dem RKI ermöglichen zu sehen mit wem ich Kontakt hatte, um diese
Personen zu informieren.

– Die Daten aus der App werden kontinuierlich an das RKI
geschickt.

• Falls jemand in meinem Umfeld positiv auf COVID-19 getestet wird,
würde die App es dem RKI ermöglichen zu sehen, dass ich mit dieser
Person Kontakt hatte, um mich zu informieren.

– Die App ermöglicht es der Regierung zu sehen, wenn ich michnicht an eine verordnete Quarantäne halte.
– Keine der genannten Eigenschaften.

• Falls ich positiv auf COVID-19 getestet werde, könnten Hacker inmeiner Umgebung rausfinden, dass ich infiziert bin.

– Weiß ich nicht.

19

• Die Nutzung der App würde es dem RKI ermöglichen zu erfahren,
wenn ich den Mindestabstand zu anderen Personen nicht einhalte.

• Q20 Welcher Partei stehen Sie am nächsten?
[Grüne / CDU/CSU / SPD / FDP / AfD / Die Linke / Andere/Möchte
ich nicht sagen]• Die App würde Informationen über meinen Aufenthaltsort nutzen, um
das Infektionsrisiko für andere besser einzuschätzen.

• Q21 Wie häufig haben Sie derzeit in Person engen Kontakt zu Menschen außerhalb Ihres Haushaltes?
[Höchstens einmal pro Woche / Ein paar Mal pro Woche / Ein paar
Mal pro Tag / Viele Male pro Tag / Möchte ich nicht sagen]• Es bestünde die Möglichkeit, dass die App fälschlicherweise ein
Ansteckungsrisiko meldet und ich mich deshalb unnötig in Quarantäne
begeben muss.

• Falls mich die App über ein Ansteckungsrisiko informiert, müsste ich
mich in gesetzlich verordnete Quarantäne begeben.

• Q22 Wie besorgt bzw. unbesorgt sind Sie wegen COVID-19 in den
folgenden drei Bereichen?
Gesundheit, Wirtschaft, Soziales[Unbesorgt / Ein wenig besorgt / Besorgt / Sehr besorgt / Möchte ichnicht sagen]• Das RKI hätte eine Datenbank mit Kontaktinformationen von Infizierten und von Personen mit denen diese Kontakt hatten.

• Q23 Gehören Sie zu einer COVID-19 Risikogruppe?
[Ja / Nein / Weiß ich nicht / Möchte ich nicht sagen]• Der Schutz der Daten würde durch ein für die App entworfenes, neues
Gesetz gewährleistet.

• Q24 Gehört eine Ihnen nahestehende Person zu einer COVID-19
Risikogruppe?
[Ja / Nein / Weiß ich nicht / Möchte ich nicht sagen]• Es bestünde die Möglichkeit, dass die App fälschlicherweise ein
Ansteckungsrisiko meldet und ich mich deshalb unnötig testen lasse.

• Der Schutz der Daten würde durch eine Datenschutzerklärung und die
Datenschutzgrundverordnung gewährleistet.

• Q25 Sind Sie oder ist jemand in Ihrem persönlichen Umfeld bereits an
Covid-19 erkrankt?
[Ja / Nein / Weiß ich nicht / Möchte ich nicht sagen]• Der Schutz der Daten würde durch technische Maßnahmen gewährleistet.

• Q26 Ist in Ihrem persönlichen Umfeld bereits jemand an Covid-19
verstorben?
[Ja / Nein / Weiß ich nicht / Möchte ich nicht sagen]• Die App würde vom deutschen Bundesamt für Sicherheit in der Informationstechnik (BSI) auf Datensicherheit und Datenschutz überprüft.
• Die App würde von unabhängigen SicherheitsexpertInnen auf Datensicherheit und Datenschutz überprüft.

• Q27 Wie wirkt sich die Covid-19 Pandemie auf Ihre finanzielle Situation aus?
[Positiv / Gar nicht / Negativ / Wirtschaftliche Existenz ist bedroht /Möchte ich nicht sagen]• Die App würde keine Informationen über meinen Aufenthaltsort erheben.

• Q28 Müssen Sie jemanden wegen der Covid-19 Pandemie zuhausebetreuen?
[Ja / Nein / Möchte ich nicht sagen]• Dies ist eine Aufmerksamkeitsfrage. Bitte wählen Sie als Antwort"Kein Einfluss" aus.
• Die App wäre Open Source.

• Q29 Wie hat sich die Krise auf Ihre Arbeit ausgewirkt?
[Gar nicht / Home Office / Kurzarbeit / Job verloren / Job gefunden /Möchte ich nicht sagen]• Der Staat würde durch Gesetze, aber nicht durch technische Maßnahmen daran gehindert, die Daten für Überwachung zu missbrauchen.
Es wird diskutiert, ob die App eine Voraussetzung für die Nutzung verschiedener Einrichtungen sein sollte, z.B. Restaurants, Bus und Bahn, etc.
Da in solchen Einrichtungen viele Menschen zusammenkommen, wäre
eine gezielte Ermittlung von Infektionsrisiken hilfreich. Bedenken Sie dabei
jedoch bitte, dass ca. 20% der Bevölkerung in Deutschland kein Smartphone
besitzt und somit diese Einrichtungen nicht mehr nutzen könnte.

Danke!
Wenn Sie mehr über das Coronavirus erfahren und herausfinden möchten,
wie Sie sich und Ihre Familie bestmöglich schützen können, klicken Sie auf
diesen Link zur Coronavirus-Website der Bundeszentrale für gesundheitliche
Aufklärung: https://www.infektionsschutz.de/coronavirus/ Vielen Dank dassSie an unserer Studie teilgenommen haben!
Wenn Sie Feedback haben oder noch Ihre Meinung zu der App mitteilen
wollen, können Sie das in dem untenstehenden Feld tun. Alternativ können Sie sich auch direkt an die Forscher wenden, indem Sie eine Mail an
study@lists.iai.uni-bonn.de senden.

• Q15 Würden Sie diese Art Nutzungsvoraussetzung befürworten oderablehnen?
[Voll befürworten / Eher befürworten / Weder befürworten noch
ablehnen / Eher ablehnen / Voll ablehnen / Möchte ich nicht sagen /Weiß ich nicht]

Demographische Daten• Q16 Was ist Ihr Geschlecht?
[Männlich / Weiblich / Nicht binär / Möchte ich selbst beschreiben: /Möchte ich nicht sagen]• Q17 Was ist Ihr beruflicher Status?
[SchülerIn / StudentIn / Angestellt / Beamter / Selbstständig / Freiberuflich / Arbeitssuchend / RentnerIn / Möchte ich nicht sagen]• Q18 Haben Sie spezielle Computerfähigkeiten wie: SystemAdministration, Programmieren, IT-Sicherheit, Tech-support, PowerUser etc.
[Ja / Nein / Möchte ich nicht sagen]• Q19 Inwieweit stimmen Sie der folgenden Aussage zu oder lehnen sie
ab: "In der Regel vertraue ich darauf, dass die Regierung das Richtige
tut."[Stimme vollständig zu / Stimme eher zu / Stimme weder zu, noch lehne
ab / Lehne eher ab / Lehne vollständig ab / Möchte ich nicht sagen]

20

C

Additional Tables and Figures

Figure 5: Attributes that are wrong for the current app andthe percentage of participants that checked the correspondingbox.
Figure 3: Frequency of reported information sources (n=744).

Usage intention

LBNB belief?

Positive influence if
no location usage

Undecided

Yes (n= 46)
No (n= 108)

26.1%
28.7%

Prob-NoYes (n= 27)
No (n= 53)

3.7%
15.1%

Def-NoYes (n= 25)
No (n= 65)

4%
7.7%

Table 2: Percentage of participants who rated the potential
property that the app would not collect data about users’ position positively based on their general usage intention andwhether they believed the app would be working with location
data. LBNB = Location service but no Bluetooth.

Figure 4: Attributes that are correct for the current app andthe percentage of participants who checked the correspondingbox. OTH: other is infected, SLF: self infected, GEN: general
attribute21

Figure 6: No way

Figure 7: Probably not

Figure 8: Neutral22

Figure 9: Probably Yes

Figure 10: Definitely yes

23Factor
Factor(PP) WARNS ME IF EXPOSED TO
COVID(PP) INFORMS MY CONTACTS IF
INFECTED(PP) INFORMS OTHERWISE
UNINFORMED USERS(PP) HELPS RKI ASSESSSITUATION(PP) FASTER RETURN TO NORMAL(PP) FASTER ECONOMY RECOVERY(PP) RKI SEES MY CONTACTS TO
INFORM OTHERS(PP) RKI SEES INFECTED’SCONTACTS TO INFORM ME(PP) HACKERS KNOW INFECTIONSTATUS(PP) RKI SEES DISTANCEVIOLATION(PP) USES MY LOCATION TO
PROTECT OTHERS(PP) UNNECESSARY QUARANTINEDUE TO FALSE POSITIVE WARNING(PP) UNNECESSARY TESTING DUE
TO FALSE POSITIVE WARNING(PP) WARNING RESULTS IN
QUARANTINE ENFORCEMENT(PP) HAS DATABASE OF INFECTED
AND CONTACTS(PP) DATA PROTECTED BY NEW LAW(PP) DATA PROTECTED BY GDPR
(PP) TECHNICAL PROTECTION OF
DATA(PP) TESTED BY BSI(PP) TESTED BY IT EXPERTS(PP) LOCATION NOT COLLECTED(PP) CODE IS OPEN SOURCE(PP) ONLY LAW PREVENTSSURVEILLANCE
Neg. change | No change
No change | Pos. change

Log
Odds C.I.

p-value

1.52 [1.31, 1.73]< 0.001∗

1.15 [0.94, 1.37]< 0.001∗

1.01 [0.80, 1.23]< 0.001∗

1.28 [1.07, 1.49]< 0.001∗

1.17 [0.96, 1.39]
0.81 [0.59, 1.03]
1.12 [0.90, 1.33]< 0.001∗< 0.001∗< 0.001∗

1.20 [0.98, 1.41]< 0.001∗

Trust in Government (Q19)
Trust: Fully agree
1.88
Trust: Somewhat agree
0.81
Trust: Somewhat disagree−0.56
Trust: Fully disagree−1.12Beliefs(GEN) THREATS PRIVACY−1, 33(GEN) FIGHTS DISEASE
0.55
SPREAD(GEN) RESTRICTS BASIC−1.32
RIGHTS(OTH) GOVERNMENT SEES
−0.70
QUARANTINE VIOLATION(SLF) INFORMS MY
0.51
CONTACTS(GEN) MANDATORY USAGE
0.66(GEN) USES LOCATION
0.42
SERVICES(SLF) DATA
0.31
TRANSMISSION ONLY
AFTER CONFIRMATION(OTH) INFORMS IF−0.28
INFECTED NEARBYWorries (Q22)
Health: Somewhat worried
0.53
Health: Worried
0.76
Health: Very worried
1.21
Media Sources (Q7)Media: Off. Homepage
0.99
Media: Publications
0.62
Media: Public Broadcasters−0.30
Personal Experience (Q25)
Was or knows infected: Yes−0.06
Was or knows infected: −0.84
Don’t know
Demographics (Q16, Q18)
Tech Background
0.24
Intercepts (App usage intention)
Definitely not | Probably not−1.99
Probably not | Undecided
0.35
Undecided | Probably would
0.53
Probably would | Definitely
0.61
would not−1.48 [−1.69, −1.27] < 0.001∗−0.87 [−1.09, −0.65] < 0.001∗−0.10 [−0.33, 0.12]

0.359−1.34 [−1.55, −1.13] < 0.001∗−0.87 [−1.09, −0.66] < 0.001∗
0.04 [−0.18, 0.27]

0.709

0.18 [−0.04, 0.41]

0.105

0.73 [0.52, 0.95]
0.88 [0.67, 1.10]
1.00 [0.79, 1.22]< 0.001∗< 0.001∗< 0.001∗

Log
Odds

0.90 [0.69, 1.12]< 0.001∗
1.12 [0.91, 1.33]< 0.001∗1.10 [0.88, 1.31]< 0.001∗−0.12 [−0.34, 0.10]
0.277−0.53 [−0.75, −0.31] < 0.001∗−1.89 [−2.05, −1.74] < 0.001∗
1.33 [1.31, 1.35]< 0.001∗

Table 3: Ordered logit regression model correlating different
app properties against a combined “Usage Intention Change”
scale ranging from ‘Negative change” to “Positive change”.

C.I.

p-value[1.26, 2.50][0.41, 1.20][−1.08, −0.04][−1.85, −0.39]< 0.001 ∗< 0.001 ∗0.035 ∗
0.003 ∗[−1.82, −0.84][0.16, 0.94]< 0.001 ∗0.005 ∗[−1.88, −0.77]< 0.001 ∗[−1.08, −0.33]< 0.001 ∗[0.15, 0.88]

0.006 ∗[−0.07, 1.39][0.06, 0.77]

0.075
0.022 ∗[−0.02, 0.65]

0.069[−0.61, 0.06]

0.107[0.04, 1.02][0.24, 1.28][0.63, 1.79]

0.036 ∗
0.004 ∗
< 0.001 ∗[−0.14, 2.12][0.07, 1.18][−0.63, 0.04]

0.085
0.028 ∗
0.082[−0.63, 0.51][−1.57, −0.11]

0.840
0.024 ∗[−0.14, 0.62]

0.208[−2.61, −1.37][0.14, 0.56][0.38, 0.68][0.48, 0.74]< 0.001 ∗0.001 ∗
< 0.001 ∗< 0.001 ∗Table 4: Results of the final ordered logit regression model
correlating factors with app usage intention. “Don’t want toanswer” answers were omitted. See Section 3.5 and Table 5
for further details.

24Factor
Required
Trust in Government
Optional
Beliefs
Worries
Media Sources
Personal
Demographics

Description

Baseline

5-point scale. Fully trust to fully distrust towards the government.

Neither

3 multi-choice questions. Beliefs about the app in general, personal context, and related to others.
3 questions; 4-point scales. How worried are participants regarding future health, economy, and social life.
Multi-choice question. From which media sources participants learned about the app.
6 questions; Yes, No & “Don’t know”. Health risks, previous infection, deaths, and other personal effects.
7 questions. General demographic questions such as tech background, age, gender, and job.

n/a
Not worriedn/aNovarious

Table 5: Factor categories appearing in the candidate regression models. Model candidates always included the required factors
and covered all possible combinations of optional factors. Final models were selected based on lowest AIC. Categorical factors
are individually compared to their listed baseline.

Abbreviation
General attributes

Question

True?

(GEN) SHARES MOTION PROFILE(GEN) SHARES TEMPORARY IDS(GEN) SHARE PHONE CONTACTS WITH
GOVERNMENT(GEN) GOVERNMENT CAN TRACK ME(GEN) THREATS PRIVACY(GEN) DETECTS NEARBY USERS(GEN) HELPS WITH TESTING DECISION(GEN) SHARES FITNESS DATA
(GEN) SHOWS NEGATIVE INFECTION
STATE(GEN) FIGHTS DISEASE SPREAD(GEN) CAN DONATE HEALTH DATA
(GEN) RESTRICTS BASIC RIGHTS(GEN) GOVERNMENT SEES DISTANCEVIOLATION(GEN) USES LOCATION SERVICES(GEN) MANDATORY USAGE(GEN) USES BLUETOOTH(GEN) NONE

The app shares a profile of my movement.
The app shares temporary IDs and timestamps.
The app shares the names and phone numbers of my contacts with the government.

7 [36]
3 [38]
7 [18]

The app enables the government to see my current location.
The app undermines my privacy.
The app determines when other smartphones are nearby that are also using the app.
The app facilitates decision-making on who should be tested for COVID-19.
The app shares fitness data.
The app can be used to demonstrate to others that I am not currently COVID-19 positive.

7 [36]
3 [38]
3 [19]7 [9]7The app can help fight the spread of the COVID-19 virus.
Through the app I can donate health data to the RKI for research purposes.
The app infringes my fundamental rights.
The app enables the government to see if people are not keeping a safe distance from others.

3 [38]7 [9]
7 [14]

The app uses location services (like GPS).
Usage of the app will be mandatory.
The app uses Bluetooth.
None of the above applies- [3, 14]
7 [38]
3 [38]
7

The app notifies me when an infected person is located nearby.
The app notifies me if I have had contact with an individual who later tested positive for COVID19.
The app enables the government to see if someone is not complying with quarantine orders.

7 [14]
3 [38]

None of the above applies

7

A physician or the public health authority has to confirm my positive COVID-19 test result
before the app sends data to the RKI.
The app informs other app users who have been close to me that they may have contracted thevirus.
The app enables the government to see if I am not complying with quarantine orders.

3 [14]

The app sends data continuously to the RKI.
None of the above applies

7 [14]
7

Attributes if others are infected(OTH) INFORMS IF INFECTED NEARBY(OTH) INFORMS IF CONTACTED INFECTED(OTH) GOVERNMENT SEES QUARANTINEVIOLATION(OTH) NONE

7 [14]

Attributes if I myself am infected(SLF) DATA TRANSMISSION ONLY AFTERCONFIRMATION(SLF) INFORMS MY CONTACTS(SLF) GOVERNMENT SEES QUARANTINEVIOLATION(SLF) SHARES DATA CONTINUOUSLY(SLF) NONE

3 [38]
7 [14]

Table 6: Overview of all statements the participants were presented with and for which they had to decide whether they apply tothe to be released CWA. The last column indicates if the attribute is correct for the app.

25

Abbreviation(Potential Property)(PP) ONLY LAW PREVENTSSURVEILLANCE(PP) TECHNICALPROTECTION OF DATA
(PP) RKI SEES INFECTED’SCONTACTS TO INFORM ME(PP) RKI SEES MY
CONTACTS TO INFORMOTHERS(PP) RKI SEES DISTANCEVIOLATION(PP) HAS DATABASE OF
INFECTED AND CONTACTS(PP) HELPS RKI ASSESSSITUATION(PP) USES MY LOCATION TO
PROTECT OTHERS(PP) LOCATION NOTCOLLECTED(PP) TESTED BY BSI(PP) FASTER RETURN TONORMAL(PP) UNNECESSARY
QUARANTINE DUE TO FALSEPOSITIVE WARNING(PP) FASTER ECONOMYRECOVERY(PP) WARNING RESULTS IN
QUARANTINE ENFORCEMENT(PP) WARNS ME IF EXPOSEDTO COVID(PP) UNNECESSARY TESTING
DUE TO FALSE POSITIVEWARNING(PP) TESTED BY IT
EXPERTS(PP) DATA PROTECTED BY
GDPR(PP) INFORMS OTHERWISE
UNINFORMED USERS(PP) HACKERS KNOWINFECTION STATUS(PP) DATA PROTECTED BY
NEW LAW(PP) INFORMS MY CONTACTSIF INFECTED(PP) CODE IS OPEN SOURCE

Full statement

True?

The government would be prevented by law, but not by technical means, frommisusing the data for surveillance purposes.
Technical measures would be implemented to ensure the data are protected.

7 [28]

Approach (Central/
Decentral/Both)C

3 [28]

B

If somebody near me has tested positive for COVID-19, the app would enable
the RKI to see that I have had contact with that individual in order to notify me
accordingly.
If I test positive for COVID-19, the app would allow the RKI to see who I had
contact with in order to notify those individuals.

7 [28]

C

7 [28]C

Using the app would enable the RKI to find out if I am not complying withminimum distancing to other individuals.
The RKI would have a database with the contact data of infected individuals andthe people they have had contact with.
The app would support the RKI to better assess the COVID-19 situation.

7 [28]

C

7 [28]C

7

C

The app would use information about my location to more accurately monitor
infection risk for others.
The app would not collect any data about my location.

7 [14]-

3 [14]B

The German Federal Office for Information Security(BSI) would verify that theapp fulfills data security and data protection requirements.
Using the app would make possible a speedier return to normal public life.

3 [4]

B

3 [55]B

There is a possibility that the app could incorrectly report infection risk, resulting
in me having to quarantine unnecessarily.

7 [13]

B

Using the app would help restart the economy faster.

3

B

If the app notifies me that I may have been infected, I would have be required bylaw to quarantine.
The app would notify me if I have been in a situation putting me at risk ofcontracting COVID-19.
There is a possibility that the app could incorrectly report infection risk, resulting
in me having to get tested unnecessarily.

7 [13]

B

3 [38]

B

3 [19]

B

Independent security experts would verify that the app fulfills data security anddata protection requirements.
Protection of the data would be guaranteed pursuant to a data protection policy
and the General Data Protection Regulation.
The app would inform people of infection risk who would not otherwise becontacted by the public health authority.
Any nearby hackers could find out if I have tested positive for COVID-19.

3 [39]

B

3 [28]

B

3 [38]

B

3 [47]

D

Protection of the data would be guaranteed under a new law drafted especiallyfor the app.
If I have tested positive for COVID-19, the app would automatically notify otherusers of the app who are at risk being exposed through contact with me.
The app would be open-source

7B

7 [28]

B

3 [15]B

Table 7: The presented potential properties are either true for the centralized (C) or the decentralized (D) approach, or true forboth (B) app designs. The properties that did not depend on the design approach is marked with “-”.

26

Currently, how frequently do you have close personal contact with people not from your household?
Once a week at most
39.5
A few times a week
37.8
A few times a day
10.2
Not disclosed
2.4
How concerned or unconcerned are you about COVID-19 in regard to the following three areas?
HealthUnconcerned
16.0
A bit concerned
39.4
Concerned
25.7
Not disclosed
0.7
The economy
Unconcerned
7.3
A bit concerned
22.5
Concerned
34.5
Not disclosed
0.5
Society
Unconcerned
11.3
A bit concerned
23.9
Concerned
35.8
Not disclosed
0.9
Do you fall within a COVID-19 high-risk group?
Yes
31.1
No
58.1
Don’t know
9.8
Does someone close to you fall within a COVID-19 high-risk group?
Yes
62.2
No
31.3
Don’t know
5.8
Have you or any person close to you fallen ill with Covid-19?
Yes
7.5
No
86.8
Don’t know
5.0
Has anyone close to you died of Covid-19?
Yes
3.0
No
94.9
Don’t know
1.8
How has the Covid-19 pandemic affected you financially?
Positive impact
3.2
No impact
58.9
Negative impact
32.4
Not disclosed
2.3
Has the Covid-19 pandemic resulted in you having to look after/care for someone at home?
Yes
9.4
No
89.9
Not disclosed
0.7
How has the crisis affected your work?
Unaffected
52.7
Working from home
24.5
Short-time work
13.6
Found employment
0.9
Not disclosed
3.4

Several times a day

10.1

Very concerned

18.3

Very concerned

35.2

Very concerned

28.1

Not disclosed

1.9

Not disclosed

0.7

Not disclosed

0.7

Not disclosed

0.4

Critical impact

3.2

Became unemployed

5.0

Potential Property

Def-No and Prob-No

Def-No and Undecided

Def-No and Prob-Yes

Def-No and Def-Yes

Prob-No and Undecided

Prob-No and Prob-Yes

Prob-No and Def-Yes

Undecided and Prob-Yes

Undecided and Def-Yes

Prob-Yes and Def-Yes

Table 8: Impact of the Covid-19 pandemic on participants. Numbers report the percentages in each question (n = 744)

(PP) WARNS ME IF EXPOSED TO COVID(PP) INFORMS MY CONTACTS IF INFECTED(PP) INFORMS OTHERWISE UNINFORMED USERS(PP) HELPS RKI ASSESS SITUATION(PP) FASTER RETURN TO NORMAL(PP) FASTER ECONOMY RECOVERY(PP) RKI SEES MY CONTACTS TO INFORM OTHERS(PP) RKI SEES INFECTED’S CONTACTS TO INFORM ME(PP) HACKERS KNOW INFECTION STATUS(PP) RKI SEES DISTANCE VIOLATION(PP) USES MY LOCATION TO PROTECT OTHERS(PP) UNNECESSARY QUARANTINE DUE TO FALSE POSITIVE WARNING(PP) UNNECESSARY TESTING DUE TO FALSE POSITIVE WARNING(PP) WARNING RESULTS IN QUARANTINE ENFORCEMENT(PP) HAS DATABASE OF INFECTED AND CONTACTS(PP) DATA PROTECTED BY NEW LAW(PP) DATA PROTECTED BY GDPR
(PP) TECHNICAL PROTECTION OF DATA
(PP) TESTED BY BSI(PP) TESTED BY IT EXPERTS(PP) LOCATION NOT COLLECTED(PP) CODE IS OPEN SOURCE(PP) ONLY LAW PREVENTS SURVEILLANCE

0.42
0.37
0.43
0.48
0.37
0.29
0.39
0.39
0.31
0.24
0.36
0.29
0.32
0.26
0.25
0.4
0.38
0.35
0.32
0.38
0.35
0.36
0.32

0.55
0.55
0.58
0.61
0.49
0.45
0.55
0.59
0.35
0.42
0.5
0.44
0.46
0.53
0.48
0.49
0.56
0.51
0.49
0.52
0.38
0.44
0.39

0.68
0.67
0.66
0.71
0.63
0.53
0.64
0.67
0.46
0.49
0.64
0.52
0.51
0.59
0.6
0.61
0.65
0.63
0.64
0.63
0.42
0.46
0.48

0.81
0.81
0.77
0.85
0.77
0.66
0.79
0.8
0.58
0.61
0.75
0.63
0.67
0.74
0.72
0.74
0.79
0.76
0.79
0.76
0.61
0.53
0.64

0.27
0.32
0.23
0.25
0.22
0.23
0.24
0.35
0.06
0.22
0.22
0.17
0.18
0.36
0.27
0.14
0.31
0.27
0.24
0.23
0.06
0.12
0.09

0.54
0.56
0.42
0.51
0.47
0.37
0.45
0.51
0.19
0.31
0.49
0.28
0.27
0.49
0.48
0.41
0.5
0.48
0.48
0.45
0.16
0.2
0.22

0.76
0.75
0.62
0.76
0.68
0.54
0.7
0.71
0.36
0.47
0.66
0.43
0.5
0.67
0.63
0.62
0.71
0.68
0.7
0.66
0.44
0.32
0.45

0.37
0.33
0.25
0.33
0.3
0.19
0.3
0.26
0.15
0.12
0.34
0.15
0.11
0.23
0.31
0.29
0.28
0.3
0.31
0.28
0.11
0.1
0.14

0.62
0.59
0.51
0.63
0.55
0.41
0.6
0.55
0.31
0.32
0.56
0.33
0.38
0.5
0.51
0.53
0.57
0.57
0.57
0.52
0.4
0.24
0.38

0.34
0.33
0.34
0.38
0.33
0.25
0.34
0.34
0.19
0.22
0.31
0.2
0.3
0.31
0.27
0.31
0.36
0.34
0.33
0.3
0.28
0.15
0.26

Table 9: Effect sizes of comparison between the general installation intention groups. Bright grey indicate a small effect (0.10 - <0.3), darker grey a moderate effect (0.30 - < 0.5) and the darkest grey a large effect (>= 0.5).

27