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Original Paper
The Impact of Individuals’Social Environments on Contact Tracing
App Use: Survey Study
Atiyeh Sadeghi*, PhD; Sebastian Pape*, PD, PhD; David Harborth*, PhD
Chair of Mobile Business & Multilateral Security, Faculty of Economics and Business, Goethe University Frankfurt, Frankfurt, Germany
*all authors contributed equally
Corresponding Author:
Sebastian Pape, PD, PhD
Chair of Mobile Business & Multilateral Security
Faculty of Economics and Business
Goethe University Frankfurt
Theodor-W.-Adorno-Platz 4
Frankfurt, 60323
Germany
Phone: 49 69798 ext 34701
Email: sebastian.pape@m-chair.de
Abstract
Background: The German Corona-Warn-App (CWA) is a contact tracing app to mitigate the spread of SARS-CoV-2. As of
today, it has been downloaded approximately 45 million times.
Objective: This study aims to investigate the influence of (non)users’ social environments on the usage of the CWA during 2
periods with relatively lower death rates and higher death rates caused by SARS-CoV-2.
Methods: We conducted a longitudinal survey study in Germany with 833 participants in 2 waves to investigate how participants
perceive their peer groups’ opinion about making use of the German CWA to mitigate the risk of SARS-CoV-2. In addition, we
asked whether this perceived opinion, in turn, influences the participants with respect to their own decision to use the CWA. We
analyzed these questions with generalized estimating equations. Further, 2 related sample tests were performed to test for differences
between users of the CWA and nonusers and between the 2 points in time (wave 1 with the highest death rates observable during
the pandemic in Germany versus wave 2 with significantly lower death rates).
Results: Participants perceived that peer groups have a positive opinion toward using the CWA, with more positive opinions
by the media, family doctors, politicians, and virologists/Robert Koch Institute and a lower, only slightly negative opinion
originating from social media. Users of the CWA perceived their peer groups’opinions about using the app as more positive than
nonusers do. Furthermore, the perceived positive opinion of the media (P=.001) and politicians (P<.001) was significantly lower
in wave 2 compared with that in wave 1. The perceived opinion of friends and family (P<.001) as well as their perceived influence
(P=.02) among nonusers toward using the CWA was significantly higher in the latter period compared with that in wave 1. The
influence of virologists (in Germany primarily communicated via the Robert Koch Institute) had the highest positive effect on
using the CWA (B=0.363, P<.001). We only found 1 decreasing effect of the influence of politicians (B=–0.098, P=.04).
Conclusions: Opinions of peer groups play an important role when it comes to the adoption of the CWA. Our results show that
the influence of virologists/Robert Koch Institute and family/friends exerts the strongest effect on participants’ decisions to use
the CWA while politicians had a slightly negative influence. Our results also indicate that it is crucial to accompany the introduction
of such a contact tracing app with explanations and a media campaign to support its adoption that is backed up by political decision
makers and subject matter experts.
(JMIR Hum Factors 2023;10:e45825) doi: 10.2196/45825
KEYWORDS
contact tracing app; corona warning app; Corona-Warn-App; social influence; usage; COVID-19
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Introduction
Background
With the global pandemic caused by SARS-CoV-2, digital
proximity tracing systems to identify people who have been in
contact with an infected person are one approach to trying to
get the pandemic under control. There have been many
discussions on different implementations and their architecture
[1], that is, whether the approach should be centralized or
decentralized. One implementation is the German
Corona-Warn-App (CWA). It is built with privacy in mind, is
based on a decentralized approach [2], and the usage intention
of German citizens has already been widely discussed
concerning privacy concerns [3] and knowledge about the app
[4]. However, the influence of different groups in the social
environments of citizens on the use of contact tracing apps
during the pandemic was—to the best of our knowledge—not
a subject of extensive research before. This is interesting from
a theoretical point of view because research on the acceptance
of new technologies considers social influence as an antecedent
of behavioral intention to use technologies [5]. Consequently,
it also found its way [6,7] into some successors of the
Technology Acceptance Model (TAM; cf. [8]).
Furthermore, this lack of research on social influence and
contact tracing apps is surprising because the medical nature of
the disease (SARS-CoV-2) is inherently based on human
interactions. Furthermore, previous research suggests that
knowledge about the CWA significantly reduces the privacy
concerns about it [4]. However, most citizens do not acquire
knowledge from primary sources but rather from discussions
with their peer groups. Thus, the assumption must be made that
the decision to undertake a disease prevention measure (in our
case using a contact tracing app) is always embedded within
the back and forth of social interactions, perceptions, or even
pressures. This can also be seen in the design of contact tracing
apps. They not only allow their users to see whether they had
potential contact with infected individuals but also to warn
others by entering positive (or negative) SARS-CoV-2 test
results. Consequently, it is crucial to investigate how citizens
perceive the opinion of their peer groups on using contact tracing
apps. However, because this question alone would not suffice
to draw conclusions on the decision of the citizens to use the
app, we also need to ask whether this opinion influences them
for or against using such an app. To address this, we conducted
a longitudinal survey study with 833 participants to investigate
these opinions and the perceived influence of a set of peer
groups on the participants to use the CWA. Peer groups in our
study include media (eg, print media, websites, and television),
family doctors, politicians, virologists/the Robert Koch Institute
(RKI; a German federal government agency and research
institute responsible for disease control and prevention), social
media, and friends and family.
We surveyed participants 2 times with a time distance between
the surveys of approximately 10 months to also investigate
changes over time of the use behavior of the app and the
opinions and influences of the relevant groups and to control
for the severeness of the pandemic. These 2 periods were chosen
because we observed the height of the death rates due to
SARS-CoV-2 in Germany during the first period (wave 1) with
more than 1200 deaths at a given day compared with
significantly lower death rates during the second period (wave
2) with approximately 200 deaths at a given day.
In summary, we investigate the following 4 research questions
(RQs):
•RQ1: How do users and nonusers perceive opinions of
relevant groups and their influence?
•RQ2: What are the differences between users and nonusers?
•RQ3: How do the opinions and the influence change over
time (from wave 1 to wave 2) driven by infection rates
(decreased from wave 1 to wave 2)?
•RQ4: How does the opinion of the relevant groups influence
the usage of the CWA?
Prior Work
Researchers have conducted surveys on adopting SARS-CoV-2
tracing apps in various countries [9]. Although some data point
to reasonably high app support globally [10], other research
highlighted the issue of low usage rates [11]. The majority of
articles use surveys to investigate the users’ adoption of 1 or
more contact tracing apps (eg, in Australia [12], China [13],
France [10], Germany [3,4,10,13,14], Ireland [15,16], Italy [10],
Taiwan [17], the United Kingdom [10,18,19], and the United
States [10,13,20]). For example, Horstmann et al [21] (see also
[3]) found for a sample in Germany that the most common
reasons for nonusers were privacy concerns, lack of technical
equipment, and doubts about the app’s e ectiveness. Most other
studies reported similar results and identified privacy concerns
as one of the main barriers to using contact tracing apps. In
particular, people are worried about corporate or government
surveillance, potentially even after the pandemic [16], leakage
of data to third parties [10], exposure of social interactions [22],
and secondary use of the provided data [22]. However,
misconceptions based on widespread knowledge gaps
accompany the adoption of contract tracing apps [4].
Besides these studies, Blom et al [23] studied potential adoption
barriers of the official contact tracing app (Corona-Warn-App)
that was launched in Germany on June 16, 2020.
Their findings indicate that with low adoption rates in the
general population and problems with selectivity across
subgroups, the data reflect a pessimistic view of the usefulness
of app-based contact tracing to contain the SARS-CoV-2
epidemic in Germany. According to their estimates, roughly
81% of the German population aged between 18 and 77 years
have access to devices that can be used to install the German
Corona-Warn-App. However, the authors found that only 35%
are eager to do so. This indicates that most citizens lack
awareness about the app or the motivation to use it. Thus,
research is needed to investigate individuals’ reasons for and
against using the app.
Previous studies have focused on users’ perceptions and
motivations concerning mobile health (mHealth) apps on a more
general level without considering the aspect of social
interactions and pressure, which are associated with a technology
focusing on combating infectious diseases [24-26]. According
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to prior research on individuals’motivations for using mHealth
apps, factors such as access to a smartphone with the necessary
app installed and internet connectivity [27,28], smartphone
users’ capacity to carry out the functions necessary to use the
app [29], prior experience using mobile technologies [30,31],
reliable information and true performance and functionality
provided by the apps [32-35], trust in data security or authorities
[10,14,36,37], and privacy concerns [16,24,38-51] have a
significant role in their motivation to use mHealth apps.
Less research has, however, examined the effect of social
influence and social relationships [10,14,16,52-59] on the
motivation to use mHealth apps, especially in the context of
infectious disease presentation (which effectively is the target
of contact tracing apps). For mHealth apps in general, research
finds that the more people identify with others, the more
positively they view these other individuals [60-62]. The degree
of identification with the source (or “authority”) predicts the
propensity of individuals to utilize these new technologies
[63,64]. Social influence is also used in related research, which
uses the TAM to investigate factors influencing users’
willingness to use and pay for a mobile health care app [59].
Bettiga et al [59] incorporated the idea of social influence
through subjective norms that play a crucial part in decisions
and health-related choices. A subjective norm is defined as an
individual’s sense of the level to which significant others
approve or disapprove of the target behavior [65]. Self-care and
preventative behavior are frequently driven by a sense of
compliance to social expectations from family members, the
social group to which the individual belongs, and doctors. This
research also shows that the general intention to accept
preventative mHealth technology is influenced by the social
influence of healthy adults. In another investigation, people
used social interactions with their peers as an active
information-seeking strategy to rule out potential negative
effects of using or not using a certain technology. In this way,
social interaction assists in lessening uncertainty by serving as
a mechanism for gathering knowledge and excluding alternatives
[14].
Li et al [66] evaluated a model of trusting bases along with 8
different factors in the context of initial trust in a national
identity system. They found that in the setting of initial trust,
social influence had a greater impact on trusting beliefs than
any of the trusting bases. It is crucial because initial trust
formation is particularly pertinent in information systems, where
users must get past their concerns about risk and uncertainty
before utilizing a technology. The closest related work to ours
is the one by Scholl and Sassenberg [52], which explored
whether a person’s level of identification with 2 groups, namely,
(1) with the beneficiaries of app use (ie, people in their social
surroundings) and (2) the source endorsing the app (ie,
government officials) predicts their propensity to utilize contact
tracing apps. Their results indicate that the more people identify
with members of their social environment (the beneficiaries)
and the government (the source), the more their app acceptance
increases. We have focused on the opinion of more groups with
the lens of social influence as a key driver due to the context of
using the app to prevent an infection with an infectious disease
and warn other members of the society in case one is sick.
Therefore, we contribute to the literature by increasing the detail
concerning the specific social group in question and
disentangling potential relations among the influencing powers
of these different groups.
Methods
Overview
In this section, we briefly cover the data collection, sample
demographics, and the questionnaire development (see
Multimedia Appendix 1 for the questionnaire).
Data Collection and Demographics
We conducted the study with a certified panel provider in
Germany (certified following the ISO 20252 norm [67]). The
survey was implemented with the software LimeSurvey (version
2.72.6; LimeSurvey GmbH) [68], hosted on a university server
and conducted in 2 waves. The first wave was ran in January
2021 and the second wave was ran from mid-October 2021 to
mid-November 2021.
The idea behind the 2 waves was to collect data in 2 points of
time with different acuteness and severeness of the pandemic
(Figures 1 and 2). We chose hospitalization and death rates, as
politicians in Germany decided upon disease prevention
measures (eg, lockdowns) based on these 2 measures later in
the course of the pandemic (initially, the incidence rate was
used as the main indicator for political decisions).
In the first wave, we sampled the participants to achieve a
representative sample for Germany. For that purpose, we set
quotas to end up with approximately 418/833 (50.2%) females
and 415/833 (49.8%) males in the sample and distribution of
age following the EUROSTAT 2018 census [69]. Furthermore,
we set a quota to end up with half of the sample using the CWA
and the other half not using it.
In the second wave, we could only rely on the participants of
the first wave. Therefore, we did not sample using hard quotas
but steered participation by sending out invitations to participate
in bunches. Each bunch addressed the underrepresented
participants to balance the properties use of the CWA, age, and
gender.
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Figure 1. Hospitalization rate in Germany [64].
Figure 2. Number of SARS-CoV-2 deaths in Germany [64].
Questionnaire
To assess the opinion of relevant peer groups and their influence
on the participant, we asked 2 questions in a matrix, where the
participant was asked about each peer group’s opinions on the
app’s usage as well as how the opinion of each group influenced
the participant for or against using the CWA. There was no
suitable construct, thus we developed the 2 questions based on
existing literature on perceived opinions [7] and influence [8]
of related research. As relevant peer groups, we identified media
(eg, print media, websites, television), family doctors,
politicians, virologists/RKI (a German federal government
agency and research institute responsible for disease control
and prevention), social media, and friends/family based on
discussions in the public press. The items for the peer groups’
opinions were measured with a 7-point Likert scale, ranging
from “1=strongly negative” to “7=strongly positive.” The items
for the peer groups’ influence were measured with a 7-point
Likert scale, ranging from “1=strongly against the use of the
app” to “7=strongly for the use of the app.” In addition, we
gathered the demographics age, gender, education, and income
of the participants.
We conducted a pretest with 12 researchers in a workshop. Each
researcher answered the question independently. Afterward, we
discussed the items and clarified their understanding and
meaning. For perceived opinion and influence, only minor
changes were made concerning the peer group names.
Ethical Considerations
Users were informed about the purpose of the study, about the
storage location of the survey data, and that they stay anonymous
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as long as they do not reveal their identity within the free texts.
However, we used an identifier from the panel provider to link
the date for each participant across the 2 waves. We did not
have any further information from the panel provider linked to
the identifier. Minors were not allowed to participate. This was
ensured by our panel provider and an additional information
text before our survey. Participants agreed that their data are
used for research and consequent publications.
The user study was evaluated by the Joint Ethics Committee of
the Faculty of Economics and Business of Goethe University
Frankfurt and the Gutenberg School of Management and
Economics of the Faculty of Law, Management and Economics
of Johannes Gutenberg University Mainz. The project has been
classified as “ethically acceptable.”
Data Analysis
The data have been analyzed using SPSS version 26 (IBM, Inc.)
and R (R Foundation). In the first step, descriptive statistics
were used to show how users and nonusers perceived the
opinions of relevant groups and their influence. In the second
step, as the data were not normally distributed, 2 related samples
tests (including mean, SD, minimum, maximum, number of
nonmissing cases, and quartiles. Tests: Wilcoxon signed-rank,
sign, McNemar) and nonparametric tests (Wilcoxon) were
applied to understand how the opinions and the influence
differed between users and nonusers and changed over time
(from wave 1 to wave 2). And finally, using the marginal model
with the generalized estimating equations, we estimated how
the different groups in the participants’ social environments
influenced the usage of the CWA.
Results
Overview
In this section, the result of the data analysis is reported. We
have 2 main parts in this section: First, we briefly discuss RQ1,
which is primarily a descriptive analysis of our sample. Then,
second, in the data analysis part, we present the results of the
remaining 3 RQs.
Data Collection and Demographics
Our sample from the first wave consisted of 1752 participants.
Following EUROSTAT 2018, participants were representatives
of Germany concerning age and gender, income, and education
(cf. [3,4]); 896 participants use the CWA (51.14%), whereas
856 do not (48.86%). As this is a longitudinal study with the
goal to compare changes over time, we only considered the
participants that took part in waves 1 and 2. This left us with
833 participants who were roughly split into 2 equally sized
groups of the CWA users and nonusers (Table 1).
As we deliberately divided the sample into 2 approximately
equal groups (CWA users and nonusers), we needed to ensure
that the groups were not biased with respect to the demographics
(Table 2). For age, we conducted a Shapiro-Wilk test for
normality and found that the variable was not normally
distributed (P<.001). Therefore, we used a Wilcoxon
signed-rank sum test and found that there were no significant
differences in terms of age between CWA users and nonusers
(P=.85). We also conducted Pearson chi-square tests and found
that age (P=.62) and gender (P=.09) did not reveal a statistically
significant difference between users and nonusers. However,
for income (P=.002) and education (P=.008), there were
statistically significant differences between users and nonusers,
with both of these variables being statistically significantly
higher for the users compared with the nonusers. To evaluate
the effect size, we additionally conducted Kendall τ test and
found that the correlation between users/nonusers and their
income (P=.01, τ=0.085) as well as education (P<.001,
τ=0.116), respectively, was only small. Based on this result, we
argue that the absolute difference does not have a substantial
confounding effect on our later analysis.
Table 1. Participant’s use of the Corona-Warn-App over time.
Wave 2 (N=833)Wave 1 (N=833)Usage/wave
427409Users
406424Nonusers
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Table 2. Demographics of participants who took part in both waves (N=833).
Value, n (%)Demographics
Age
118 (14.2)18-29 years
149 (17.9)30-39 years
166 (19.9)40-49 years
214 (25.7)50-59 years
186 (22.3)60 years and older
Gender
418 (50.2)Female
415 (49.8)Males
0 (0)Divers
0 (0)Prefer not to say
Net income
76 (9.1)
€500-€1000a
177 (21.2)€1001-€2000
202 (24.2)€2001-€3000
146 (17.5)€3001-€4000
156 (18.7)More than €4000
76 (9.1)Prefer not to say
Education
3 (0.4)No degree
99 (11.9)Secondary school
278 (33.4)
Secondary schoolb
184 (22.1)A levels
108 (13.0)Bachelor’s degree
147 (17.6)Master’s degree
14 (1.7)Doctorate
a€1=US $1.08 (data as of May 20, 2023).
bThe German education system does not allow a 1:1 translation, therefore, there are 2 different “grades” of secondary school.
RQ1: How Do Users and Nonusers Perceive Opinions
of Relevant Groups and Their Influence?
To get an impression about the distribution of users and nonusers
and investigate RQ1, we analyzed the distribution of the
participants’peer groups’opinions and their perceived influence
on the participants (Figures 3 and 4).
Figure 5illustrates that the participants’perception of their peer
groups is in general positive, with a higher opinion from media,
family doctors, politicians, and virologists/RKI. The perception
of social media posts was slightly negative for both users and
nonusers. Interestingly, the reported opinions from users for
friends and family were way higher than the ones from nonusers;
besides, the ones from nonusers were slightly negative. A similar
picture was perceived when considering the influence of friends
and family.
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Figure 3. Distribution of the answers regarding the perceived opinion of different groups in participants’ social environments. W: wave.
Figure 4. Distribution of the answers regarding the perceived influence that different groups in participants’ social environments have on using the
Corona-Warn-App. W: wave.
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Figure 5. The mean of group opinion and its influence on (non)users at 2 waves. W: wave.
RQ2: What Are the Differences Between Users and
Nonusers?
As discussed in the previous section, CWA users seem to
perceive their peer groups’opinions more positively. Thus, we
now took up RQ2 and systematically investigated the differences
between users and nonusers. The visual impression from Figure
5is supported by Mann-Whitney tests showing significant
differences between users and nonusers except for the opinion
of social media postings. According to the means, nonusers
generally had a lower mean at both waves (Table 3).
Furthermore, we investigated the influence of gender with a
Mann-Whitney Utest. The test results indicated that gender
does not present any difference in the perception of the peer
groups’ opinion when it comes to the opinion of social media
posts toward the CWA. The mean was higher for men than for
women in both groups (user and nonuser) and in both waves
(Multimedia Appendix 2).
We also investigated the influence of age on the perceived
opinion and influence of the peer groups. For this purpose, we
used a Kruskal-Wallis Htest (Table 4). The result showed that
the differences were significant between the different age groups
for the opinions of virologists/RKI (P=.03) and friends/family
(P=.04), as well as for the influence of the media (P<.001),
family doctors (P=.03), politicians (P<.001), virologists/RKI
(P<.001), and friends/family (P<.001). Although there is a
tendency within these groups that the oldest group had the
highest values, the means do not give a clear picture, as there
was another peak for the “40-49-year” age group.
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Table 3. Differences of perceived opinions and influence between users and nonusers
Wave 2Wave 1MeanMann-Whitney
significance (P
value)
Variable
Nonuser,
mean
User,
mean
Mann-Whitney
significance (P
value)
Nonuser,
mean
User,
mean
Mann-Whitney
significance (P
value)
NonuserUser
4.324.60<.0014.424.83<.0014.374.71<.001Opinion of media
4.415.11<.0014.395.21<.0014.405.16<.001Opinion of family doctor
4.735.23<.0014.965.46<.0014.845.34<.001Opinion of politicians
4.925.66<.0015.045.75<.0014.985.70<.001Opinion of virolo-
gists/Robert Koch Insti-
tute
3.763.86.233.683.80.633.723.83.23Opinion of social media
posts
3.855.01<.0013.744.84<.0013.794.93<.001Opinion of friends/family
4.004.66<.0013.974.88<.0013.984.77<.001The influence of media
4.054.60<.0014.014.59<.0014.034.59<.001The influence of family
doctor
4.234.92<.0014.085.09<.0014.165.00<.001The influence of politi-
cians
4.335.48<.0014.175.58<.0014.255.53<.001The influence of virolo-
gists/Robert Koch Insti-
tute
3.804.11<.0013.754.11<.0013.774.11<.001The influence of social
media posts
3.804.80<.0013.664.81<.0013.734.81<.001The influence of
friends/family
Table 4. Opinions and influence with respect to using the Corona-Warn-App for age.
SignificancezAge among users, meanKruskal-Wallis Htest in terms of age
≥60 years50-59 years40-49 years30-39 years20-29 years
Groups/variable based on
.0310.566.085.795.815.485.55Opinion of virologists/Robert Koch Insti-
tute
.049.695.134.794.924.624.78Opinion of friends/family
<.00117.625.344.645.054.674.78The influence of media
.0310.405.004.434.654.464.45The influence of family doctor
<.00113.955.574.805.125.015.07The influence of politicians
<.00114.716.075.425.565.535.35The influence of virologists/Robert Koch
Institute
<.00113.435.364.694.734.724.59The influence of friends/family
RQ3: How Do the Opinions and the Influence Change
Over Time (From Wave 1 to Wave 2) Driven by
Infection Rates (Decreased From Wave 1 to Wave 2)?
We also investigated the changes in the perceived opinion and
the influence of peer groups over time. Table 5 shows that the
differences among users in the first wave and second wave were
minimal. However, after applying the Wilcoxon test, we found
significant differences:
•Users’ perceived opinion about the media group was
significantly lower in the second wave. This holds true for
all participants (P=.001) and for the users (P=.001), but the
decrease for nonusers was lower (P=.15), and not
statistically significant.
•Users’ perceived opinion of politicians was significantly
lower for all participants (P<.001) as well as for the user
(P=.004) and nonuser (P=.003) subgroups in wave 2.
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•Users’ perceived opinion of friends/family with respect to
using the CWA had significantly increased for all
participants (P>.001) as well as for the user (P=.003) and
nonuser (P=.02) subgroups.
•The perceived influence of media toward using the CWA
significantly decreased among users (P=.002), meaning the
influence was weaker but still toward using the CWA.
•The perceived influence of virologists/RKI toward using
the CWA significantly increased among nonusers (P=.01)
toward using the CWA.
•The perceived influence of friends/family toward using the
CWA significantly increased among nonusers (P=.02)
toward using the CWA.
Table 5. Differences in opinion and influence between the 2 points in time (waves 1 and 2).
Wilcoxon signifi-
cance (Pvalue) of
all participants
NonusersUsersVariable
Wave 2,
mean
Wave 1,
mean
Wilcoxon signifi-
cance (Pvalue)
Wave 2,
mean
Wave 1,
mean
Wilcoxon signifi-
cance (Pvalue)
.0014.324.42.154.604.83.001Opinion of media
.864.414.39.245.115.21.18Opinion of family doctor
<.0014.734.96.0035.235.46.004Opinion of politicians
.064.925.04.155.665.75.23Opinion of virolo-
gists/Robert Koch Institute
.133.763.68.403.863.80.18Opinion of social media
posts
<.0013.853.74.025.014.84.003Opinion of friends/family
.114.003.97.524.664.88.002The influence of media
.534.054.01.124.604.59.66The influence of family
doctor
.904.234.08.204.925.09.08The influence of politicians
.474.334.17.015.485.58.08The influence of virolo-
gists/Robert Koch Institute
.743.803.75.594.114.11.94The influence of social me-
dia posts
.093.803.66.024.804.81.80The influence of
friends/family
RQ4: How Does the Influence of the Relevant Groups
Influence the Usage of the CWA?
We used a marginal model with generalized estimating equations
to investigate the effect of (non)users’ social environment on
the usage of the CWA (Table 6). As can be seen, among social
environment variables, the influence of politicians (P=.04),
virologists/RKI (P<.001), and friends/family (P<.001) was
significant and had changed the usage of the CWA. The other
variables were insignificant (media: P=.13; family doctor:
P=.80; social media: P=.07; and time: P=.26), and their change
did not affect the independent variable. The influence of
virologists/RKI had the most increasing effect (increasing the
odds of using the CWA). Increasing 1 unit of influence of the
virologists/RKI variable and keeping the other variables constant
increased the odds of using the CWA by 44%. The only
decreasing effect (decreasing the odds of using the CWA) was
related to the influence of politicians variable. Increasing 1 unit
of the influence of politicians variable and keeping the other
variables constant decreased the odds of using the CWA by
10%.
We modeled time as a single variable to represent the influence
of the different waves to have a simple model and reduce
complexity. Modeling it as an interaction term with each of the
other independent variables would have resulted in not only a
more complex model, but also one with certain overlaps (each
variable with its interaction of time), which are hard to interpret.
To investigate how the opinion and the influence of peer groups
are correlated, we conducted a Pearson correlation. The Pearson
correlation shows that the opinion of the social environment
and its influence on using the CWA are related but not strongly
correlated (Table 7).
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Table 6. Marginal model with generalized estimating equations for the effects of the influence of the social environment on using the app.
Exp(B)Hypothesis testBParameter
Significance (Pvalue)Wald chi-square
0.071<.001107.323–2.641(Intercept)
1.088.132.3280.085Influence of media (print media, websites, film and television)
0.988.800.062–0.012Influence of family doctor
0.907.044.073–0.098Influence of politicians
1.437<.00147.0240.363Influence of virologists/Robert Koch Institute
0.917.073.261–0.086Influence of social media posts
1.361<.00146.0230.308Influence of friends/family
1.072.261.2910.069Time
Goodness of fit
1931.927NANA
NAa
Quasi likelihood under independence model criteriona
1938.039NANANACorrected quasi likelihood under independence model criterion
aNA: not applicable.
Table 7. The Pearson correlation between opinion toward the social environment and its influence on using the Corona-Warn-App.
NonuserUserCorrelation
Opinion of media and its influence on using the app
0.2190.397Pearson correlation (r)
<.001<.001Significance (Pvalue)
Opinion of family doctor and its influence on using the app
0.3370.395Pearson correlation (r)
<.001<.001Significance (Pvalue)
Opinion of politicians and its influence on using the app
0.1560.383Pearson correlation (r)
<.001<.001Significance (Pvalue)
Opinion of virologists/Robert Koch Institute and its influence on using the app
0.2520.496Pearson correlation (r)
<.001<.001Significance (Pvalue)
Opinion of social media posts and its influence on using the app
0.3240.346Pearson correlation (r)
<.001<.001Significance (Pvalue)
Opinion of friends/family and its influence on using the app
0.3770.434Pearson correlation (r)
<.001<.001Significance (Pvalue)
Discussion
Following the structure of the previous section, we discuss the
RQs one by one.
RQ1: How Do Users and Nonusers Perceive Opinions
of Relevant Groups and Their Influence?
It is surprising that the perception of participants with respect
to their peer groups is in general positive. Given that half of the
participants were not using the CWA, we assumed that the
perception of the nonusers’peer groups would be on the lower
side of the Likert scale. However, that is only the case for family
and friends and social media posts. Social media posts are only
perceived to be slightly negative, but the opinion expressed
there is perceived lower than from all other groups. This might
be related to the ongoing discussions about the Network
Enforcement Act (German: Netzwerkdurchsetzungsgesetz,
NetzDG), which tries to combat fake news, harassment, and
misinformation in social media.
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RQ2: What Are the Differences Between Users and
Nonusers?
Not surprisingly, users perceive their peer groups’ opinions
more positively than nonusers. Gender does not seem to have
an influence. However, with increasing age the tendency
increased to perceive the peer groups’opinion more positively.
However, there was a peak for the “40-49-year” age group for
all peer groups. The “40-49-year” age group overlaps to a large
degree with the so-called Gen X, but we could not find any
indication that SARS-CoV-2 or technology was perceived
differently by this group in Germany.
RQ3: How Do the Opinions and the Influence Change
Over Time Driven by Infection Rates?
Hospitalization rates and number of deaths were significantly
lower during wave 2 compared with wave 1. The perceived
opinion of media and politicians has significantly decreased
from wave 1 to wave 2. This fits with the observation that many
politicians were (wrongly) blaming the app to be not so useful
because it does not send information to the public health
departments or blaming data protection for hindering the
effectiveness of the app.
The perceived opinion of friends and family as well as their
perceived influence toward using the CWA has increased. This
might be related to the perception that many public health
departments in Germany were overloaded and the official fight
against SARS-CoV-2 was given up due to shortage of staff.
RQ4: How Does the Opinion of the Relevant Groups
Influence the Usage of the CWA?
The influence of virologists/RKI has the most increasing effect.
This could be backed up by a huge presence of the RKI in the
media and their decisive role in changing the rules during the
pandemic. The only decreasing effect we found was the
influence of politicians, which could be explained by the
participants getting tired of politicians contradicting each other,
feathering their own nest by promoting companies selling masks
and other medical equipment to fight the pandemic, and a
number of seemingly uncoordinated decisions between the
different states and the federation.
Principal Findings
Our results indicate that participants’ perception of their peer
groups is in general positive, with a higher opinion from media,
family doctors, politicians, and virologists/RKI and a lower,
only slightly negative, opinion from social media posts. Users
perceived their peer group’s opinion higher than nonusers. A
similar pattern can be observed when considering the peer
groups’influence instead of the opinion. The perceived opinion
of media and politicians has significantly decreased from wave
1 to wave 2. The perceived opinion of friends and family as
well as their perceived influence toward using the CWA has
increased. The influence of virologists/RKI has the most
increasing effect. The only decreasing effect we found was the
influence of politicians.
Limitation
Our study has several limitations. First, our measurement of the
opinion and influence of the participants’ peer groups was
self-reported. On the one hand, participants might report not
only wrong values, but also misinterpret their own perception.
On the other hand, this is supported by our evaluation that the
participants’ perception is more important than the actual
opinion of the peer groups. As a consequence, it is unclear
whether lower values stem from a lower perception or a lower
opinion (ie, for the report of nonusers).
Furthermore, we can only evaluate correlations but not causality.
Therefore, we do not know whether the users’ perception of
their peers’ opinion is higher, because they are using the app.
In contrast to nonusers, they might be able to identify wrong
statements within their peer group and disregard them.
In addition, we only differentiated between users and nonusers.
There might be different levels of activity when using the app
(ie, participants might just look at infection rates or the personal
risk or they could share their own infection).
The separation of groups is not very strict, that is, participants
could read statements of the RKI or from other peer groups via
social media. However, there is most likely a different
perception between those groups; therefore, we had included
social media as its own group in the survey.
Our study only had participants located in Germany using the
CWA. While the study could not easily be transferred to other
countries, as all countries have different contact tracing apps,
there might still be cultural influences in the perception of and
interaction with the named peer groups. Thus, it could be
interesting to have similar investigations in other countries or
cultures in the future.
Comparison With Prior Work
To the best of our knowledge, different entities of the social
environment of users and nonusers and their influence on the
usage of contact tracing apps have not been investigated yet.
Only 1 study by Scholl and Sassenberg [52] is related to ours
because it investigated the social environment of contract tracing
users by measuring a person’s level of identification with the
beneficiaries of the contact tracing app (ie, people in their social
surroundings) to predict their willingness to use contact tracing
apps. Thus, this study is only partially related as it covers only
one of the groups we also asked for in our study, namely, friends
and family. The authors found that the closer other people in
individuals’ social environments are, the more likely they are
to use contact tracing apps. This is in line with our finding that
a positive opinion and influence of friends and family positively
influence the use of the CWA. We contribute to the literature
by widening the analysis to different peer groups in the social
environment such as doctors or politicians. In addition, our
variable social influence is conceptually different from the
identification variable in the study by Scholl and Sassenberg
[52].
In addition, Oldeweme et al [14] investigated the influence of
transparency, social influence, trust in the government, and
initial trust in a COVID-19 tracing app on the process of
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adopting the app. Their results showed that the transparency
dimensions of disclosure and accuracy, in addition to social
influence, trust in government, and initial trust, positively
influenced the adoption process. They agree on the definition
of social influence as the “degree to which an individual
perceives that important others believe he or she should use the
new system” [5], but they did not investigate which groups were
more important than others.
Social influence not only directly influences the adoption of the
CWA, but might also influence other important antecedents of
the adoption. We have already mentioned that peer groups most
likely have an influence on the knowledge of the app [4], which
itself influences the privacy concerns, and thus the adoption of
the app [3]. Additionally, the perception of the perceived disease
threat has been shown to influence the adoption of the app when
applying the TAM [70], the Health Belief Model [71], and the
Protection Motivation Theory [55]. However, peer groups might
also influence the perceived disease threat. Kaspar [55] found
that the intention for using a contact tracing app increased when
trust in other people’s social distancing behavior decreased.
Although other people might be not considered as a peer group,
it clearly shows that the perceived behavior of other people
influences the adoption of the app. However, Kaspar [55] did
not further investigate differences between specific groups’
influence on the adoption. Kostka and Habich-Sobiegalla [13]
investigated the public perception toward COVID-19 tracing
apps in Germany (and China and the United States) and
examined variables such as conspiracy belief (not significant),
belief in a second wave (significant), or trust in the state
(partially significant). However, they did not investigate the
influence of peer groups, although connections have been
demonstrated between the COVID-19 pandemic and the 5G
conspiracy theory and the spread of misinformation in social
networks [72].
Alam et al [73] made use of the Health Belief Model to
investigate the public attitude toward vaccinations against
COVID-19. They found that, among other factors, “health
motivation” was an important factor for the willingness to get
vaccinated. Part of this construct is the recommendation of
friends, relatives, and the participants’ physician. However,
they also did not further investigate the influence of the different
groups.
Conclusions and Future Work
Opinions of peer groups play an important role when it comes
to the adoption of the CWA. Naturally, not all groups have the
same importance. Our results show that the influence of
virologists/RKI and family and friends contributed to the
adoption of the CWA the most, while politicians only had a
slightly negative influence on citizens to use the CWA. Our
results indicate that it is crucial to accompany the introduction
of such a contact tracing app with an appropriate media
campaign with easily understandable technical explanations
and the clear approval of political decision makers to support
its adoption among a large group of citizens in a given country.
Although the pandemic is considered by many to be overcome,
these considerations are still important to make, to create a more
resilient society in the future. It is important to investigate not
only the adoption of contact tracing apps, but also the adoption
of data donation apps. Although the CWA has a feature using
which users can report their infections, it would also be
beneficial if data could be collected to learn more about the
specific disease and how it spreads. For that purpose, not only
privacy and privacy concerns should be investigated, but also
the influence of peer groups, as they can play a decisive role in
the adoption of apps. Besides contact tracing and data donation
apps, apps could be used to nudge the users into specific
behaviors, such as physical distancing [74], which again would
rely on the users’ intention to adopt the app(s).
One natural idea of a future work is to extend our study to other
health apps such as those mentioned earlier. One could go even
further and investigate health apps such as fitness tracking apps
or diet diary apps in general. However, it is also important to
scientifically connect the different areas and study the
interdependencies of knowledge, perceived disease threat, the
opinion and influence of peer groups, and the adoption of the
CWA. However, media and misinformation or fake news can
influence people’s opinion about the CWA. Therefore, besides
a solid education and online/computer literacy, it is important
to understand the effects of peer groups to be able to plan and
implement governmental information campaigns accordingly.
Acknowledgments
This work was supported by the Goethe-Corona-Fonds from Goethe University Frankfurt and the European Union’s Horizon
2020 research and innovation program under grant agreement number 830929 (CyberSecurity4Europe).
Conflicts of Interest
None declared.
Multimedia Appendix 1
Questionnaire used in the survey.
[DOCX File , 17 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Mann-Whitney Utest for gender.
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[DOCX File , 19 KB-Multimedia Appendix 2]
References
1. DP-3T Team. Privacy and security risk evaluation of digital proximity tracing systems. GitHub 2020 Apr 21:1-21 [FREE
Full text]
2. Beskorovajnov W, Dörre F, Hartung G, Koch A, Müller-Quade J, Strufe T. Contra corona: Contact tracing against the
coronavirus by bridging the centralized--decentralized divide for stronger privacy. Berlin, Germany: Springer; 2021 Dec
Presented at: International Conference on the Theory and Application of Cryptology and Information Security; December
6-10, 2021; Singapore. [doi: 10.1007/978-3-030-92075-3_23]
3. Harborth D, Pape S. A Privacy Calculus Model for Contact Tracing Apps: Analyzing the German Corona-Warn-App.
Berlin, Germany: Springer; 2022 Jun Presented at: IFIP International Conference on ICT Systems Security and Privacy
Protection; June 13–15, 2022; Copenhagen, Denmark p. 3-19. [doi: 10.1007/978-3-031-06975-8_1]
4. Pape S, Harborth D, Kröger JL. Privacy concerns go hand in hand with lack of knowledge: The case of the German
Corona-Warn-App. 2021 Jun Presented at: IFIP International Conference on ICT Systems Security and Privacy Protection;
June 22–24, 2021; Oslo, Norway p. 256-269. [doi: 10.1007/978-3-030-78120-0_17]
5. Venkatesh V, Morris M, Davis G, Davis F. User Acceptance of Information Technology: Toward a Unified View. MIS
Quarterly 2003;27(3):425-478. [doi: 10.2307/30036540]
6. Robert Koch Institut. SARS-CoV-2-Infektionen_in_Deutschland. GitHub. 2022. URL: https://github.com/robert-koch-institut/
SARS-CoV-2-Infektionen_in_Deutschland [accessed 2023-04-01]
7. Neubaum G, Krämer NC. Monitoring the Opinion of the Crowd: Psychological Mechanisms Underlying Public Opinion
Perceptions on Social Media. Media Psychology 2016 Aug 05;20(3):502-531. [doi: 10.1080/15213269.2016.1211539]
8. Mutz DC. The influence of perceptions of media influence: Third person effects and the public expression of opinions. Int
J Public Opin Res 1989;1(1):3-23. [doi: 10.1093/ijpor/1.1.3]
9. LI J, Guo X. Global Deployment Mappings and Challenges of Contact-tracing Apps for COVID-19. SSRN Journal 2020:1-7.
[doi: 10.2139/ssrn.3609516]
10. Altmann S, Milsom L, Zillessen H, Blasone R, Gerdon F, Bach R, et al. Acceptability of App-Based Contact Tracing for
COVID-19: Cross-Country Survey Study. JMIR Mhealth Uhealth 2020 Aug 28;8(8):e19857 [FREE Full text] [doi:
10.2196/19857] [Medline: 32759102]
11. Montagni I, Roussel N, Thiébaut R, Tzourio C. Health Care Students' Knowledge of and Attitudes, Beliefs, and Practices
Toward the French COVID-19 App: Cross-sectional Questionnaire Study. J Med Internet Res 2021 Mar 03;23(3):e26399
[FREE Full text] [doi: 10.2196/26399] [Medline: 33566793]
12. Duan SX, Deng H. Hybrid analysis for understanding contact tracing apps adoption. IMDS 2021 Apr 30;121(7):1599-1616.
[doi: 10.1108/imds-12-2020-0697]
13. Kostka G, Habich-Sobiegalla S. In Times of Crisis: Public Perceptions Towards COVID-19 Contact Tracing Apps in China,
Germany and the US. SSRN Journal 2021:1-28. [doi: 10.2139/ssrn.3693783]
14. Oldeweme A, Märtins J, Westmattelmann D, Schewe G. The Role of Transparency, Trust, and Social Influence on Uncertainty
Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps. J Med Internet Res 2021
Feb 08;23(2):e25893 [FREE Full text] [doi: 10.2196/25893] [Medline: 33465036]
15. Fox G, Clohessy T, van der Werff L, Rosati P, Lynn T. Exploring the competing influences of privacy concerns and positive
beliefs on citizen acceptance of contact tracing mobile applications. Computers in Human Behavior 2021 Aug;121:106806.
[doi: 10.1016/j.chb.2021.106806]
16. O'Callaghan ME, Buckley J, Fitzgerald B, Johnson K, Laffey J, McNicholas B, et al. A national survey of attitudes to
COVID-19 digital contact tracing in the Republic of Ireland. Ir J Med Sci 2021 Aug 16;190(3):863-887 [FREE Full text]
[doi: 10.1007/s11845-020-02389-y] [Medline: 33063226]
17. Garrett PM, Wang Y, White JP, Hsieh S, Strong C, Lee Y, et al. Young Adults View Smartphone Tracking Technologies
for COVID-19 as Acceptable: The Case of Taiwan. Int J Environ Res Public Health 2021 Feb 02;18(3):1332 [FREE Full
text] [doi: 10.3390/ijerph18031332] [Medline: 33540628]
18. Horvath L, Banducci S, James O. Citizens’Attitudes to Contact Tracing Apps. J Exp Polit Sci 2020 Sep 02;9(1):118-130.
[doi: 10.1017/xps.2020.30]
19. Lewandowsky S, Dennis S, Perfors A, Kashima Y, White JP, Garrett P, et al. Public acceptance of privacy-encroaching
policies to address the COVID-19 pandemic in the United Kingdom. PLoS One 2021 Jan 22;16(1):e0245740 [FREE Full
text] [doi: 10.1371/journal.pone.0245740] [Medline: 33481877]
20. Hassandoust F, Akhlaghpour S, Johnston AC. Individuals' privacy concerns and adoption of contact tracing mobile
applications in a pandemic: A situational privacy calculus perspective. J Am Med Inform Assoc 2021 Mar 01;28(3):463-471
[FREE Full text] [doi: 10.1093/jamia/ocaa240] [Medline: 33164077]
21. Horstmann K, Buecker S, Krasko J, Kritzler S, Terwiel S. Who does or does not use the 'Corona-Warn-App' and why? Eur
J Public Health 2021 Feb 01;31(1):49-51 [FREE Full text] [doi: 10.1093/eurpub/ckaa239] [Medline: 33340328]
JMIR Hum Factors 2023 | vol. 10 | e45825 | p. 14https://humanfactors.jmir.org/2023/1/e45825 (page number not for citation purposes)
Sadeghi et alJMIR HUMAN FACTORS
XSL
•
FO
RenderX
22. Bonner M, Naous D, Legner C, Wagner J. The (lacking) user adoption of covid-19 contact tracing apps--insights from
switzerland and germany. 2020 Presented at: Workshop on Information Security and Privacy 2020; December 12, 2020;
Virtual URL: https://aisel.aisnet.org/wisp2020/12/
23. Blom AG, Wenz A, Cornesse C, Rettig T, Fikel M, Friedel S, et al. Barriers to the Large-Scale Adoption of a COVID-19
Contact Tracing App in Germany: Survey Study. J Med Internet Res 2021 Mar 02;23(3):e23362 [FREE Full text] [doi:
10.2196/23362] [Medline: 33577466]
24. Vo V, Auroy L, Sarradon-Eck A. Patients' Perceptions of mHealth Apps: Meta-Ethnographic Review of Qualitative Studies.
JMIR Mhealth Uhealth 2019 Jul 10;7(7):e13817 [FREE Full text] [doi: 10.2196/13817] [Medline: 31293246]
25. Payne H, Lister C, West J, Bernhardt J. Behavioral functionality of mobile apps in health interventions: a systematic review
of the literature. JMIR Mhealth Uhealth 2015 Feb 26;3(1):e20 [FREE Full text] [doi: 10.2196/mhealth.3335] [Medline:
25803705]
26. Birkhoff SD, Smeltzer SC. Perceptions of Smartphone User-Centered Mobile Health Tracking Apps Across Various Chronic
Illness Populations: An Integrative Review. J Nurs Scholarsh 2017 Jul 12;49(4):371-378. [doi: 10.1111/jnu.12298] [Medline:
28605151]
27. Antoun C, Conrad F, Couper M, West B. Simultaneous estimation of multiple sources of error in a smartphone-based
survey. Journal of Survey Statistics and Methodology 2019;7(1):93-117. [doi: 10.1093/jssam/smy002]
28. Keusch F, Bähr S, Haas G, Kreuter F, Trappmann M. Coverage Error in Data Collection Combining Mobile Surveys With
Passive Measurement Using Apps: Data From a German National Survey. Sociological Methods & Research 2020 Apr
07:004912412091492. [doi: 10.1177/0049124120914924]
29. Hargittai E. Second-level digital divide: Mapping differences in people's online skills. arxiv Preprint posted online on
September 24, 2001 [FREE Full text] [doi: 10.48550/arXiv.cs/0109068]
30. Cajita M, Hodgson N, Lam K, Yoo S, Han HR. Facilitators of and barriers to mHealth adoption in older adults with heart
failure. Computers, informatics, nursing: CIN 2018;36(8):376. [doi: 10.1097/cin.0000000000000442]
31. Spann A, Stewart E. Barriers and facilitators of older people's mHealth usage: A qualitative review of older people's views.
Human Technology 2018 Nov 30;14(3):264-296. [doi: 10.17011/ht/urn.201811224834]
32. Deng Z, Mo X, Liu S. Comparison of the middle-aged and older users' adoption of mobile health services in China. Int J
Med Inform 2014 Mar;83(3):210-224. [doi: 10.1016/j.ijmedinf.2013.12.002] [Medline: 24388129]
33. O'Connor S, Hanlon P, O'Donnell CA, Garcia S, Glanville J, Mair FS. Understanding factors affecting patient and public
engagement and recruitment to digital health interventions: a systematic review of qualitative studies. BMC Med Inform
Decis Mak 2016 Sep 15;16(1):120 [FREE Full text] [doi: 10.1186/s12911-016-0359-3] [Medline: 27630020]
34. Rowe F. Contact tracing apps and values dilemmas: A privacy paradox in a neo-liberal world. Int J Inf Manage 2020
Dec;55:102178 [FREE Full text] [doi: 10.1016/j.ijinfomgt.2020.102178] [Medline: 32836636]
35. Walrave M, Waeterloos C, Ponnet K. Adoption of a Contact Tracing App for Containing COVID-19: A Health Belief
Model Approach. JMIR Public Health Surveill 2020 Sep 01;6(3):e20572 [FREE Full text] [doi: 10.2196/20572] [Medline:
32755882]
36. Rowe F, Ngwenyama O, Richet J. Contact-tracing apps and alienation in the age of COVID-19. European Journal of
Information Systems 2020 Sep 13;29(5):545-562. [doi: 10.1080/0960085x.2020.1803155]
37. Zimmermann BM, Fiske A, Prainsack B, Hangel N, McLennan S, Buyx A. Early Perceptions of COVID-19 Contact Tracing
Apps in German-Speaking Countries: Comparative Mixed Methods Study. J Med Internet Res 2021 Feb 08;23(2):e25525
[FREE Full text] [doi: 10.2196/25525] [Medline: 33503000]
38. Abeler J, Bäcker M, Buermeyer U, Zillessen H. COVID-19 Contact Tracing and Data Protection Can Go Together. JMIR
Mhealth Uhealth 2020 Apr 20;8(4):e19359 [FREE Full text] [doi: 10.2196/19359] [Medline: 32294052]
39. Alexopoulos AR, Hudson JG, Otenigbagbe O. The Use of Digital Applications and COVID-19. Community Ment Health
J 2020 Oct 30;56(7):1202-1203 [FREE Full text] [doi: 10.1007/s10597-020-00689-2] [Medline: 32734311]
40. Dar AB, Lone AH, Zahoor S, Khan AA, Naaz R. Applicability of mobile contact tracing in fighting pandemic (COVID-19):
Issues, challenges and solutions. Comput Sci Rev 2020 Nov;38:100307 [FREE Full text] [doi: 10.1016/j.cosrev.2020.100307]
[Medline: 32989380]
41. Eck K, Hatz S. State surveillance and the COVID-19 crisis. Journal of Human Rights 2020 Nov 11;19(5):603-612. [doi:
10.1080/14754835.2020.1816163]
42. Fahey RA, Hino A. COVID-19, digital privacy, and the social limits on data-focused public health responses. Int J Inf
Manage 2020 Dec;55:102181 [FREE Full text] [doi: 10.1016/j.ijinfomgt.2020.102181] [Medline: 32836638]
43. Felderer B, Blom AG. Acceptance of the Automated Online Collection of Geographical Information. Sociological Methods
& Research 2019 Dec 05;51(2):866-886. [doi: 10.1177/0049124119882480]
44. Häring M, Gerlitz E, Tiefenau C, Smith M, Wermke D, Fahl S, et al. Never ever or no matter what: Investigating Adoption
Intentions and Misconceptions about the {Corona-Warn-App} in Germany. 2021 Presented at: Seventeenth Symposium
on Usable Privacy and Security (SOUPS) 2021; August 8-10, 2021; Virtual p. 77-98.
45. Jacob S, Lawarée J. The adoption of contact tracing applications of COVID-19 by European governments. Policy Design
and Practice 2020 Nov 28;4(1):1-15. [doi: 10.1080/25741292.2020.1850404]
JMIR Hum Factors 2023 | vol. 10 | e45825 | p. 15https://humanfactors.jmir.org/2023/1/e45825 (page number not for citation purposes)
Sadeghi et alJMIR HUMAN FACTORS
XSL
•
FO
RenderX
46. Jansen-Kosterink S, Hurmuz M, den Ouden M, van Velsen L. Predictors to Use Mobile Apps for Monitoring COVID-19
Symptoms and Contact Tracing: Survey Among Dutch Citizens. JMIR Form Res 2021 Dec 20;5(12):e28416 [FREE Full
text] [doi: 10.2196/28416] [Medline: 34818210]
47. Kokolakis S. Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon.
Computers & Security 2017 Jan;64:122-134. [doi: 10.1016/j.cose.2015.07.002]
48. Parker MJ, Fraser C, Abeler-Dörner L, Bonsall D. Ethics of instantaneous contact tracing using mobile phone apps in the
control of the COVID-19 pandemic. J Med Ethics 2020 Jul 04;46(7):427-431 [FREE Full text] [doi:
10.1136/medethics-2020-106314] [Medline: 32366705]
49. Sheats J, Petrin C, Darensbourg R, Wheeler C. A Theoretically-Grounded investigation of perceptions about healthy eating
and mHealth support among African American men and women in New Orleans, Louisiana. Family & Community Health
2018;41(Suppl 2 (Food Insecurity and Obesity)):S15. [doi: 10.1097/fch.0000000000000177]
50. Shi M, Jiang R, Hu X, Shang J. A privacy protection method for health care big data management based on risk access
control. Health Care Manag Sci 2020 Sep 23;23(3):427-442. [doi: 10.1007/s10729-019-09490-4] [Medline: 31338637]
51. Trang S, Trenz M, Weiger WH, Tarafdar M, Cheung CM. One app to trace them all? Examining app specifications for
mass acceptance of contact-tracing apps. European Journal of Information Systems 2020 Jul 27;29(4):415-428. [doi:
10.1080/0960085x.2020.1784046]
52. Scholl A, Sassenberg K. How Identification With the Social Environment and With the Government Guide the Use of the
Official COVID-19 Contact Tracing App: Three Quantitative Survey Studies. JMIR Mhealth Uhealth 2021 Nov
24;9(11):e28146 [FREE Full text] [doi: 10.2196/28146] [Medline: 34662289]
53. Guillon M, Kergall P. Attitudes and opinions on quarantine and support for a contact-tracing application in France during
the COVID-19 outbreak. Public Health 2020 Nov;188:21-31 [FREE Full text] [doi: 10.1016/j.puhe.2020.08.026] [Medline:
33059232]
54. Joo J, Shin M. Resolving the tension between full utilization of contact tracing app services and user stress as an effort to
control the COVID-19 pandemic. Serv Bus 2020 Sep 01;14(4):461-478. [doi: 10.1007/s11628-020-00424-7]
55. Kaspar K. Motivations for Social Distancing and App Use as Complementary Measures to Combat the COVID-19 Pandemic:
Quantitative Survey Study. J Med Internet Res 2020 Aug 27;22(8):e21613 [FREE Full text] [doi: 10.2196/21613] [Medline:
32759100]
56. Sharma S, Singh G, Sharma R, Jones P, Kraus S, Dwivedi YK. Digital Health Innovation: Exploring Adoption of COVID-19
Digital Contact Tracing Apps. IEEE Trans. Eng. Manage 2020:1-17. [doi: 10.1109/tem.2020.3019033]
57. Walrave M, Waeterloos C, Ponnet K. 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. Cyberpsychol
Behav Soc Netw 2021 Jun 01;24(6):377-383. [doi: 10.1089/cyber.2020.0483] [Medline: 33017171]
58. Keshet Y. Fear of panoptic surveillance: using digital technology to control the COVID-19 epidemic. Isr J Health Policy
Res 2020 Nov 25;9(1):67-68 [FREE Full text] [doi: 10.1186/s13584-020-00429-7] [Medline: 33239094]
59. Bettiga D, Lamberti L, Lettieri E. Individuals' adoption of smart technologies for preventive health care: a structural equation
modeling approach. Health Care Manag Sci 2020 Jun 26;23(2):203-214. [doi: 10.1007/s10729-019-09468-2] [Medline:
30684067]
60. van Knippenberg D, De Dreu CKW, Homan AC. Work group diversity and group performance: an integrative model and
research agenda. J Appl Psychol 2004 Dec;89(6):1008-1022. [doi: 10.1037/0021-9010.89.6.1008] [Medline: 15584838]
61. Brewer MB, Kramer RM. The Psychology of Intergroup Attitudes and Behavior. Annu. Rev. Psychol 1985 Jan;36(1):219-243.
[doi: 10.1146/annurev.ps.36.020185.001251]
62. Abrams D, Hogg MA. Social Identifications: A Social Psychology of Intergroup Relations and Group Processes. In:
Contemporary Sociology. Abingdon, Oxfordshire: Routledge; 2006:147.
63. Aron A, McLaughlin-Volpe T, Mashek D, Lewandowski G, Wright SC, Aron EN. Including others in the self. European
Review of Social Psychology 2004 Jan;15(1):101-132. [doi: 10.1080/10463280440000008]
64. Sassenberg K, Matschke C. The impact of exchange programs on the integration of the hostgroup into the self-concept.
Eur. J. Soc. Psychol 2009;40(1):148-159. [doi: 10.1002/ejsp.621]
65. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes 1991 Dec;50(2):179-211.
[doi: 10.1016/0749-5978(91)90020-t]
66. Li X, Hess TJ, Valacich JS. Why do we trust new technology? A study of initial trust formation with organizational
information systems. The Journal of Strategic Information Systems 2008 Mar;17(1):39-71. [doi: 10.1016/j.jsis.2008.01.001]
67. Market, opinion and social research, including insights and data analytics — Vocabulary and service requirements (ISO
Standard No. 20252:2019). International Organization for Standardization (ISO). 2019. URL: https://www.iso.org/standard/
73671.html [accessed 2023-05-18]
68. Schmitz C, LimeSurvey Project Team. LimeSurvey: an open source survey tool. LimeSurvey GmBH. Hamburg, Germany:
LimeSurvey GmBH; 2015. URL: https://www.limesurvey.org/en/ [accessed 2023-04-01]
69. EUROSTAT 2018. EUROSTAT. 2018. URL: https://ec.europa.eu/eurostat/de/home [accessed 2023-05-18]
70. Chopdar PK. Adoption of Covid-19 contact tracing app by extending UTAUT theory: Perceived disease threat as moderator.
Health Policy Technol 2022 Sep;11(3):100651 [FREE Full text] [doi: 10.1016/j.hlpt.2022.100651] [Medline: 35855013]
JMIR Hum Factors 2023 | vol. 10 | e45825 | p. 16https://humanfactors.jmir.org/2023/1/e45825 (page number not for citation purposes)
Sadeghi et alJMIR HUMAN FACTORS
XSL
•
FO
RenderX
71. Harborth D, Pape S, McKenzie LT. Why Individuals Do (Not) Use Contact Tracing Apps: A Health Belief Model Perspective
on the German Corona-Warn-App. Healthcare (Basel) 2023 Feb 15;11(4):583 [FREE Full text] [doi:
10.3390/healthcare11040583] [Medline: 36833115]
72. Ahmed W, Vidal-Alaball J, Downing J, López Seguí F. COVID-19 and the 5G Conspiracy Theory: Social Network Analysis
of Twitter Data. J Med Internet Res 2020 May 06;22(5):e19458. [doi: 10.2196/19458] [Medline: 32352383]
73. Alam MM, Melhim LKB, Ahmad MT, Jemmali M. Public Attitude Towards COVID-19 Vaccination: Validation of
COVID-Vaccination Attitude Scale (C-VAS). JMDH 2022 Apr;Volume 15:941-954. [doi: 10.2147/jmdh.s353594]
74. Villius Zetterholm M, Nilsson L, Jokela P. Using a Proximity-Detection Technology to Nudge for Physical Distancing in
a Swedish Workplace During the COVID-19 Pandemic: Retrospective Case Study. JMIR Form Res 2022 Dec 12;6(12):e39570
[FREE Full text] [doi: 10.2196/39570] [Medline: 36343202]
Abbreviations
CWA: Corona-Warn-App
mHealth: mobile health
RKI: Robert Koch Institute
RQ: research question
TAM: Technology Acceptance Model
Edited by C Jacob; submitted 19.01.23; peer-reviewed by J Li; comments to author 28.02.23; revised version received 24.03.23;
accepted 10.04.23; published 31.05.23
Please cite as:
Sadeghi A, Pape S, Harborth D
The Impact of Individuals’Social Environments on Contact Tracing App Use: Survey Study
JMIR Hum Factors 2023;10:e45825
URL: https://humanfactors.jmir.org/2023/1/e45825
doi: 10.2196/45825
PMID:
©Atiyeh Sadeghi, Sebastian Pape, David Harborth. Originally published in JMIR Human Factors (https://humanfactors.jmir.org),
31.05.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information,
a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be
included.
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