The Corona Warn App in Germany
Investigating Adoption Intentions and Misconceptions
We surveyed 744 participants about their knowledge of the (then soon to be released) German COVID tracking app, their willingness to use it, and how different features would affect this willingness.

We found many false beliefs, especially concerning technical details, i.e., 30.0% of the participants thought the Corona Warn App would use location services (other than Bluetooth).

Positive factors for the intention to use the app were worries about general health and trust in government, negative factors included a perceived intrusions into privacy or restriction of basic rights.

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Publication #


First page of the publications
Never ever no matter what: Investigating Adoption Intentions and Misconceptions about the Corona-Warn-App in Germany
Maximilian Häring, Eva Gerlitz, Christian Tiefenau, Matthew Smith, Dominik Wermke, Yasemin Acar, and Sascha Fahl.
Seventeenth Symposium on Usable Privacy and Security (SOUPS 2021), August 8-10, 2021.

Abstract

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 in Germany focused on the trade-off between a centralized or decentralized approach for data collection and processing and their implications. Specifically, privacy dominated the public debate 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 perception of 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 the government and public communication on the population’s willingness to adopt the app.

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Overview #

We conducted a survey covering the German “Corona Warn App” (CWA) right before its launch. Guided by public discussion, we were especially interested in the information background of potential users, as discussions focused on whether enough people would install it and why (not). We were also interested in the broader topic of acceptance and beliefs.

Survey #

For survey structure and questions, we considered the different available approaches to develop a contact tracing app and included input from ongoing discussions in the media:

  1. Media Sources and Knowledge. We were interested in whether the participants had already heard of the planned app and from which sources (e.g., public broadcasters, family members, social media, or official government websites). We wanted to assess their knowledge of the properties of the app in general and for two specific scenarios:
    • What happens if other users are infected.
    • What happens if the users themselves are infected.
  2. Disposition to Use. In the second part, we wanted to explore whether our participants are planning to use the planned app and why. For this, we provided a quick overview over the planned app and its features.
  3. Potential Properties. In the third part, we presented 23 statements about a hypothetical contact tracing app. We asked our participants, how these statements would affect their willingness to use the app.

A translated version of the questionnaire is below:

Questionnaire

Screening Questions

  • Q1 What is your age? [Free Text]
  • Q2 In which federal state do you live
  • 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]
  • 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 technical school diploma / Vocational school diploma / Technical school diploma / No vocational qualification / Technical college degree (or engineering school diploma) / I don’t want to state / Abitur (German university entrance qualification)]

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 health authorities in tracing and containing infection, being developed by SAP to run 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’.

  • Q7 Have you heard of the plans for this app? If ‘yes’, please select where 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.
    • Social media (Twitter, Facebook, YouTube, TikTok, etc.)
    • Official government/state agency websites (Robert Koch Institute, Federal Government, etc.)
    • Other websites: [Free text]
    • Family member
    • Friends
    • Work colleagues/associates
    • I have not heard about this app
    • Don’t know/I don’t want to state / Official Corona Warning App website

Knowledge

  • Q8 Which of the below statements do you think will apply regarding the app? (please check all that apply.)
    • 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 that are also using the app.
    • The app shares temporary IDs and timestamps.
    • The app enables the government to see my current location.
    • 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.
    • The app infringes my basic rights.
    • The app can be used to demonstrate to others that I am not currently COVID-19 positive.
    • The app facilitates decision-making on who should be tested for COVID-19.
    • The app shares fitness data.
    • The app can help fight the spread of the COVID-19 virus.
    • The app uses location services (like GPS).
    • The app shares a profile of my movement.
    • The app undermines my privacy.
    • None of the above applies.
    • Don’t know
  • Q9 What statements do you think apply regarding the app when other users are COVID-19 positive? (please check all that apply.)
    • The app enables the government to see if someone is not complying with quarantine orders.
    • The app notifies me if I have had contact with an individual who later tested positive for COVID-19.
    • The app notifies me when an infected person is located nearby.
    • None of the above applies.
    • Don’t know
  • Q10 What statements do you think apply regarding the app when you yourself are COVID-19 positive? (please check all that apply.)
    • The app informs other app users who have been close to me that they may have contracted the virus.
    • The app sends data continuously to the RKI.
    • 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 enables the government to see if I am not complying with quarantine orders.
    • None of the above applies.
    • Don’t know

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 a smartphone 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 to ensure 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 near you. The app does this via Bluetooth. The app will alert you if you have been near 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.

  • Q11 How will the described app determine what people have been near me? [Bluetooth / Location services (such as GPS) / My phone Contacts list / Don’t know]

Install General

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

  • 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]
  • Q13 What is the primary reason for your answer? [Free text]

Potential Properties

  • 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 select how these statements, if true, would influence your willingness to use the app. [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 government would be prevented by law, but not by technical means, from misusing the data for surveillance purposes.
    • Using the app would enable the RKI to find out if I am not complying with minimum distancing to other individuals.
    • The RKI would have a database with the contact data of infected individuals and the people they have had contact with.
    • 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
    • The German Federal Office for Information Security (BSI) would verify that the app fulfills data security and data protection requirements.
    • Using the app would make possible a speedier return to normal public life.
    • Technical measures would be implemented to ensure the data are protected.
    • 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
    • If the app notifies me that I may have been infected, I would have be required by law to quarantine.
    • The app would notify me if I have been in a situation putting me at risk of contracting COVID-19.
    • There is a possibility that the app could incorrectly report infection risk, resulting in me having to get tested unnecessarily
    • 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.
    • This question pertains to attentive completion of the survey. Please select “No influence” as response.
    • 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.
    • Protection of the data would be guaranteed under a new law drafted especially for the app.
    • If I have tested positive for COVID-19, the app would automatically notify other users of the app who are at risk being exposed through contact with me.
    • The app would be open-source
    • The app would support the RKI to better assess the COVID-19 situation.

It is being discussed whether use of the app should be made mandatory in certain situations where people come in contact in groups, such as patronizing restaurants or utilizing bus or train services, to facilitate targeted monitoring of infection risk. It must be considered however that roughly 20% of the German population would be excluded from using such services due to not having 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]

Demographics

  • Q16 What is your gender? [Male / Female / Non-binary / Would like to self-describe: / I don’t want to state]
  • Q17 What is your work status? [School student / University/college student / Employee / Civil servant / Self-employed / Freelancer / Unemployed / Retiree / I don’t want to state]
  • 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]
  • 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]
  • 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 with people 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]
  • Q22 How concerned or unconcerned are you about COVID-19 in regard 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]
  • 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]
  • 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]
  • 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]

Our study was reviewed and approved by our institution’s Research Ethics Board. We also adhered to the German data protection laws and the GDPR in the EU.

For all answers, we provided an option for participants not wanting to give any details (i.e., “I don’t want to state” or “I don’t know”). Participants could drop out at any time. Participants had to give consent to take part.

Findings #

In this section, we present the results of the survey.

  1. We describe the accuracy of our participants' knowledge about the app and what sources they consulted.
  2. We describe their intention to use the app and which factors and beliefs play into this decision.
  3. We describe how potential properties and features influence their willingness to install the app.

Knowledge #

As the app was not released at the time of our survey, answers were not based on actual experience with the app, but features announced and to be expected in the app release:

  • It would use Bluetooth-Low-Energy
  • Its primary purpose would be to warn users who had been in close contact with later positively tested persons
  • Users would not learn who of their contacts reported an infection.

App Awareness. Few but a non-negligible amount of participants (11.7%) reported to never have heard of the app. This leaves 657 participants who were at least somehow aware of the app. Note that the following percentages do not add up to 100%, as as participants could report more than one source.

  • More than half of the participants (54.7%) reported that they received information about the app from public broadcasters.
  • The second most common marked source was social media (29.6%), such as Twitter or Facebook.
  • Scientific publications were used by 7.9% to get information about the CWA.

Functionality Knowledge. For participants who previously reported that they already heard about the app (n=657):

  • 59.5% of the participants were aware of the app’s basic functionality, i.e., that it would warn its users when they had been in contact with another user who later tested positive.
  • 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 to prevent misuse and false warnings.

However, participants were less aware about the app’s technical setup:

  • Only 29.8% of the participants who reported to have heard about the app knew that the app would share temporary IDs and timestamps.
  • 43.5% were aware the app would use Bluetooth, but 54.6% of the participants falsely believed that the app would use location services, and 24.7% believed the app would use both combined.
  • A common misconception (57.5%) was that the app would warn users if an infected person is in their vicinity.

Misconceptions. We asked for the primary reason for their installation intention 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.
  • 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 the app and argued: “So I can see who is infected nearby to keep a larger distance to them and protect myself and fellow people”.
  • Participants misunderstood what data will be used and shared: “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”.
  • Participants also indicated that they are confused by the amount of (different) information: “I don’t have any trust. With all the news, I don’t know what to believe anymore!!!".

Key Findings: Knowledge.

  • The majority of our participants were aware about the CWA. 59.5% correctly assumed that it would warn users with a risk of infection.
  • Participants were worried about their privacy in combination with the app (27.4%) and their basic rights (20.1%). 14.9% were worried about both.
  • We conclude that many participants did not wholly understand the apps' functionality and thus have misconceptions about who will be protected by the app, what data it collects, and with whom the data will be shared.

Intention to Use #

As of May 28, 2021 the reported download number of the Corona Warn App is about 28 millions. That estimates to around 46% of smartphone users in Germany. This estimate does not take into account that that the same person could download the app onto multiple devices.

In our survey, usage intention was distributed as follows:

  • More participants indicated to definitely install the app (21.2 %) than to definitely not install it (13.4 %).
  • Almost a third reported they will probably use the it (28.8 %) compared to 12.9% who reported to probably not use it.
  • 23.4 % were still undecided about the installation.

We report indications for reasons of the installation intention. For this, we selected an ordered logit regression with a model selection process via best AIC and BIC

  • Trust in Government: Both trust and distrust of the government correlate heavily with app usage intention.
  • Worries: Of the worries about future health, economy, and social life, only the health scale appears in the final model. This hints at a positive correlation between future health concerns and app usage intention.
  • Beliefs: One of the previously identified misconceptions ((SLF) GOVERNMENT SEES QUARANTINE VIOLATION) has a negative impact on the installation intention. A perceived threat to privacy and the potential restriction of basic rights are also negatively correlated.

Key Findings: Intention to Use.

  • Both trust and distrust of the government correlate heavily with app usage intention.
  • We found a positive correlation between concerns about future health and app usage intention.

Potential Properties #

We presented the participants different hypothetical statements and consequences of the app (potential properties, PP), asking whether and how they would influence their decision to use it.

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.

Figure 1: All potential properties and the distribution of ratings of how they would influence the usage intention. Entries with a * apply to the actual app. D | Decentralized, C | Centralized, B | Both, - | not included in either app design.

Figure 1: All potential properties and the distribution of ratings of how they would influence the usage intention. Entries with a * apply to the actual app. D | Decentralized, C | Centralized, B | Both, - | not included in either app design.

It can be seen that 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 a clear positive tendency ((PP) WARNS ME IF EXPOSED TO COVID, (PP) HELPS RKI ASSESS SITUATION).

Changes in Usage intention. The answers of the participant differ visibly based on the previously stated general usage intention of the app as it was going to be released. Participants who stated that they would want to install the app were more positive about all the potential properties than those who stated that they did not want to use the app and vice versa.

To assess the impact of each property, we report for each group whether the given answer suggests a positive, negative, or no change for the previously stated general intent to use the app.

  • Participants who reported to definitely not use the app are seldom really positive about any property.
  • Participants who reported to definitely use the app are seldom very negative about any property.

To better assess the individual effects of the different potential properties, we built an ordinal regression model based on a combined score of app usage intention and changes in intention due to these properties. Nine out of 12 properties that apply to the actual app have a positive effect on usage intention:

  • E.g., (PP) WARNS ME IF EXPOSED TO COVID and (PP) INFORMS OTHERWISE UNINFORMED USERS. Both concern the fact that the app would notify users if they could have been at risk of contracting COVID-19. This was the main feature of the app as communicated to the population.
  • The intention to install the app increases if it would help returning to a pre-COVID-19 situation: (PP) FASTER RETURN TO NORMAL and (PP) FASTER ECONOMY RECOVERY.
  • Two properties that apply to the app impacted the participants negatively: (PP) HACKERS KNOW INFECTION STATUS and (PP) UNNECESSARY TESTING DUE TO FALSE POSITIVE WARNING. The risk for unnecessary testing applies to the app, but this could happen without the app and in both the central and decentral approaches.

Eleven potential properties do not apply to the app, of which five positively correlated with app usage.

  • 3 belong to the centralized approach and offer the Robert Koch-Institute (RKI) additional insights:
    • (PP) HELPS RKI ASSESS SITUATION
    • (PP) RKI SEES MY CONTACTS TO INFORM OTHERS
    • (PP) RKI SEES INFECTED’S CONTACTS TO INFORM ME

Currently, users have to share their positive test results if they want others to be warned. Three potential properties that do not apply to the app had a negative influence on the installation intention.

  • (PP) RKI SEES DISTANCE VIOLATION and (PP) ONLY LAW PREVENTS SURVEILLANCE open up the possibility of using the app for surveillance and can fall into the centralized approach.
  • (PP) UNNECESSARY QUARANTINE DUE TO FALSE POSITIVE WARNING could be seen as a clear disadvantage for the individual user.

Trust in Entities. Some potential properties are connected to measures that should build trust, regardless of the apps’ design choices. E.g., different levels of (data) protection by law, experts testing the app, and the possibility to access the code itself.

  • The idea to protect the data by a new law, as well as the existing protection by the GDPR have a positive influence on app use intention.
  • The technical protection of the data positively influenced the participants (PP) TECHNICAL PROTECTION OF DATA.
  • It was also rated positively if the CWA would be tested by the German Federal Office for Information Security (BSI) and experts.
  • Unlike previous expert discussions could have suggested, (PP) CODE IS OPEN SOURCE is not statistically significant and received the most “I don’t know” answers of all properties. We believe many participants lacked a deeper understanding of the “Open Source” concept.

However, the participants did not seem to like the idea that the government would only be hindered by law to misuse the data for surveillance (PP) ONLY LAW PREVENTS SURVEILLANCE as it shows a negative effect size

Key Findings: Potential Properties.

  • Usage willingness increased if participants thought the CWA would help fight the spread of COVID.
  • The belief that one’s privacy or basic rights were in danger lowered the willingness significantly.
  • Whether to install the CWA or not seems primarily based on the sentiment of trust and the expectancy of a positive effect.

Considerations #

  • Focus on German Population: Our results may not be generalizable to app users in other countries. This bias is expected, as this study was specifically about the effects of the German Corona Warn App.
  • Sampling Bias. We used Qualtrics to recruit a representative German sample according to age, education, household income, and federal state/region. Due to the nature of online surveys, older participants are underrepresented in our sample. To take part in the study, we required participants to own a smartphone, as that is a precondition to use the app.
  • Self-reporting Bias. As with every survey study, we have to take into consideration that the data is self-reported.
  • Uncertainty Bias. We asked participants about their future behavior, which is prone to uncertainty. Many possible properties of the app have consequences that are not easy to estimate. We cannot assume that participants understood and thought of the consequences, especially considering some participants did not understand how the app worked in detail.

Summary #

We surveyed 744 participants about their knowledge of the (then soon to be released) German COVID tracking app, their willingness to use it, and how different features would affect this willingness.

The majority of our participants was aware of the Corona Warn App. 59.5% knew about the basic concept, that the CWA would warn users with a risk of infection.

We surveyed the usage intention of the CWA in Germany right before its launch.

  • 50% of the participants reported their intent to use the app,
  • 26.3% refrained from usage.
  • 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).
  • Perceived privacy or basic rights intrusions correlated with a lower intention to use it.
  • As also reported by other researchers, we found a positive effect when people were worried about general health and trusted the government.

We also highlight that the general population’s views were more diverse and more open to a central entity getting an overview to help fight the pandemic than the public discussion indicated.

Centralized vs. Decentralized. 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 the user’s phone. The German app is based on the decentralized approach due to public pressure to chose a more privacy-preserving approach.

Many of our participants had very positive views concerning the increased capabilities the centralized app would have had. They were in general inclined to rate properties of the centralized approach positively while they rate the consequences (in the current technical landscape) that come with it rather low.

This might suggest that there could have been more support in the population for a more feature-rich app than academics and privacy advocates acknowledged in the discussion preceding the CWA’s release.

Bluetooth and Location Services. We identified a lack in knowledge regarding location usage:

  • The technical details that the CWA would use Bluetooth were only known by 43.5%.
  • 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, many people did not seem to realize that the CWA could do tracing without GPS or the like. We also hypothesize that many who caught the term “Bluetooth” in the debate did not eliminate GPS from their mental model of the CWA.

Infected Persons Nearby. 57.5 % of our participants believed the CWA would warn its users if an infected person is nearby.

  • This was also found by Thomas et al.1 who studied participants’ knowledge regarding the already released Australian app and who found 57.4% of their participants believed this.
  • This was also the most common misconception found by Williams et al.2 in the UK.

This belief seems very common, even if it was never planned nor (to our knowledge) communicated through official channels that the CWA would be able to warn users of infected persons in their vicinity directly.

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.

Privacy Concerns. 27% of the participants believed the CWA would restrict their basic rights or threats their privacy. These beliefs had significant negative influences on usage intention. Previous work found that one of the reasons participants did not want to use an app was because they feared data misuse or surveillance.

As the German 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.

We hypothesize they project the worries around the centralized app onto the decentralized one, even if not all concerns are plausible for this approach.

“Never ever or no matter what”. We can confirm the finding of other researchers, who identified participants that did not like any app, regardless of its design choices.3 4 5 We also saw the exact opposite: Participants belonging to the “Definitely Yes” group rated every single theoretical additional aspect more positively than all other groups.

Cite This Work #

Feel free to cite this publication as:

@inproceedings{conf/soups/haering21,
	title = {Never ever no matter what: Investigating Adoption Intentions and Misconceptions about the Corona-Warn-App in Germany},
	author = {Maximilian H{\"a}ring and
		Eva Gerlitz and
		Christian Tiefenau and
		Matthew Smith and
		Dominik Wermke and
		Yasemin Acar and
		Sascha Fahl},
	booktitle = {Seventeenth Symposium on Usable Privacy and Security, SOUPS 2021, August 8-10, 2021},
	month = {Aug},
	year = {2021}
}
Häring et al. "Never ever no matter what: Investigating Adoption Intentions and Misconceptions about the Corona-Warn-App in Germany" Proceedings of the Seventeenth Symposium on Usable Privacy and Security. 2021.

Footnotes


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

  2. Public attitudes towards COVID-19 contact tracing apps: A UK-based focus group study. Simon N. Williams, Christopher J. Armitage, Tova Tampe, and Kimberly Dienes. Health Expectations, 2020. ↩︎

  3. Attitudes and opinions on quarantine and support for a contact-tracing application in France during the COVID19 outbreak. M. Guillon and P. Kergall. Public health, 188:21–31, 2020. ↩︎

  4. Decentralized is not risk-free: Understanding public perceptions of privacy-utility trade-offs in COVID-19 contacttracing apps. Tianshi Li, Cori Faklaris, Jennifer King, Yuvraj Agarwal, Laura Dabbish, Jason I. Hong, et al. arXiv preprint arXiv:2005.11957, 2020. ↩︎

  5. Apps against the spread: Privacy implications and user acceptance of covid-19-related smartphone apps on three continents . Christine Utz, Steffen Becker, Theodor Schnitzler, Florian M. Farke, Franziska Herbert, Leonie Schaewitz, Martin Degeling, and Markus Dürmuth. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, CHI ’21, 2021. ↩︎