Less About Privacy: Revisiting a Survey about the German COVID-19 Contact Tracing App

The release of COVID-19 contact tracing apps was accompanied by a heated public debate with much focus on privacy concerns, e.g., possible government surveillance. Many papers studied people's intended behavior to research potential features and uptake of the apps. Studies in Germany conducted before the app's release, such as that by Häring et al., showed that privacy was an important factor in the intention to install the app. We conducted a follow-up study two months post-release to investigate the intention-behavior-gap, see how attitudes changed after the release, and capture reported behavior. Analyzing a quota sample (n=837) for Germany, we found that fewer participants mentioned privacy concerns post-release, whereas utility now plays a greater role. We provide further evidence that the results of intention-based studies should be handled with care when used for prediction purposes. © 2023 ACM.

How Mass surveillance Crowds Out Installations of COVID-19 Contact Tracing Applications

During the COVID-19 pandemic, many countries have developed contact tracing technologies to curb the spread of the disease by locating and isolating people who have been in contact with coronavirus carriers. Subsequently, understanding why people install and use contact tracing applications is becoming central to their effectiveness and impact. However, involuntary systems can crowd out the use of voluntary applications when several contact tracing initiatives are employed simultaneously. To investigate this hypothesis, we analyze the concurrent deployment of two contact tracing technologies in Israel: centralized mass surveillance technologies and a voluntary contact tracing mobile app. Based on a representative survey of Israelis (n=519), our findings show that positive attitudes toward mass surveillance were related to a reduced likelihood of installing contact tracing apps and an increased likelihood of uninstalling them. These results also hold when controlling for privacy concerns, attitudes toward the app, trust in authorities, and demographic properties. We conclude the paper by suggesting a broader framework for analyzing crowding out effects in ecosystems that combine involuntary surveillance and voluntary participation. © 2023 ACM.

Attitudes Towards COVID-19 Contact Tracing Apps: A Cross-National Survey

To help prevent the spread of COVID-19, countries around the world have implemented a range of measures and virus containment strategies, including digital contact-tracing (DCT) in the form of smartphone apps. While early studies showed a high level of acceptability of such technologies, the adoption rates varied greatly between countries after contact-tracing apps became available to download. This cross-national user survey (n=871) aims to explore public attitudes and factors that affect user acceptability and adoption of contact-tracing apps in the USA, UK, and the Republic of Ireland, which employ similar underlying technology, but have uneven adoption rates. The results indicate interactions between installation decisions and public trust in actors and institutions communicating COVID-related information, and releasing such technologies. Beyond the immediate case of contact tracing, our findings hold implications for the deployment and communicative framing of technology for public health and the public good, and inform the design of crisis response public health information systems.

Family Tensions and Information Privacy: A Barrier to Diffusion of Proximity Tracing Applications?

Technology played a central role during the pandemic for communications and services. It was also touted as a potential solution to control the spread of COVID-19 via proximity tracing applications, also known as contact tracing (CT) apps worldwide. In non-mandated settings, however, these apps did not attain popularity. Privacy concerns were highlighted as one reason. We explored how family perceptions of CT apps can affect the family's use of such apps. We surveyed parent-teen dyads twice over a 5-month period. We analyzed parent-teen perceptions of each other's intentions and use of CT apps at time 1 and 2, exploring changes over time. Parents' use intentions were influenced by their and their teens' perceptions of the benefits but not privacy concerns. Teen intentions were influenced by their own perceptions of benefits, not their parent's, and their parent's concerns for the family. Intentions always influenced usage, including intentions at time 1 influencing use at time 2, demonstrating a longitudinal effect of intentions on usage existed for parents and teens. © 2022 IEEE Computer Society. All rights reserved.

Remote Workers' Privacy Concerns, Psychological Climate for Face Time, and Organizational Affective Commitment

During the Covid-19 pandemic, the shift to high-intensity remote work-three days or more a week-accelerated the digitalization of work processes and the blurring of boundaries between work and personal life through videoconferencing and the use of personal devices for work. This paper explores the relationships between high-intensity remote workers' information and communication technologies (ICT) privacy concerns, psychological climate for face time, and organizational affective commitment. Building on organizational support and social information processing theories, we argue that ICT privacy concerns and perceptions that an organization values physical presence in-office may undermine commitment to the organization. Based on a two-wave study of 1065 remote workers in a large multinational bank, we find that ICT privacy concerns and psychological climate for face time reinforce one another and are negatively associated with subsequent affective organizational commitment. © 2023 IEEE Computer Society. All rights reserved.

Coping with COVID-19 using contact tracing mobile apps

Purpose: To cope with the COVID-19 pandemic, contact tracing mobile apps (CTMAs) have been developed to trace contact among infected individuals and alert people at risk of infection. To disrupt virus transmission until the majority of the population has been vaccinated, achieving the herd immunity threshold, CTMA continuance usage is essential in managing the COVID-19 pandemic. This study seeks to examine what motivates individuals to continue using CTMAs. Design/methodology/approach: Following the coping theory, this study proposes a research model to examine CTMA continuance usage, conceptualizing opportunity appraisals (perceived usefulness and perceived distress relief), threat appraisals (privacy concerns) and secondary appraisals (perceived response efficacy) as the predictors of individuals' CTMA continuance usage during the pandemic. In the United States, an online survey was administered to 551 respondents. Findings: The results revealed that perceived usefulness and response efficacy motivate CTMA continuance usage, while privacy concerns do not. Originality/value: This study enriches the understanding of CTMA continuance usage during a public health crisis, and it offers practical recommendations for authorities. © 2023, Chenglong Li, Hongxiu Li and Shaoxiong Fu.

Promoting smart wearable devices in the health-AI market: the role of health consciousness and privacy protection: An International Journal

PurposeConsumers interacting with smart wearable devices is on the rise in the current health-AI market, which offers a great opportunity for companies to execute interactive marketing. However, this opportunity is mainly reliant on consumers' use of smart wearable devices. This paper aims to develop a model considering health and privacy factors to elucidate consumers' use of smart wearable devices for unleashing their full potential in interactive marketing.Design/methodology/approachThe authors collected 250 samples via an online survey to validate the smart wearable devices usage model that elucidates factors that stimulate consumer usage, including privacy concerns, health consciousness and consumer innovativeness. The authors used structural equation modeling and multi-group analysis to test the hypotheses.FindingsPrivacy concerns of consumers have a negative effect on smart wearable devices usage, while health consciousness positively impacts consumers' usage of smart wearable devices. Consumer innovativeness indirectly affects smart wearable devices usage via effort expectancy. Experienced consumers are less sensitive to the performance expectancy but more affected by effort expectancy regarding smart wearable devices.Originality/valueThe present study contributes to the literature stream of health-AI usage by unraveling the impacts of privacy concerns and health consciousness and examining the moderating role of prior experience. The findings suggest marketers in the health-AI industry should endeavor to build transparent and sound privacy protection mechanisms and promote smart wearable devices by fostering health awareness of potential consumers.

Contact Tracing Platform in OSN for Prevention of Infectious Disease Outbreaks

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection. © 2023 IEEE.

Protection Motivation Perspective Regarding the Use of COVID-19 Mobile Tracing Apps Among Public Users: Empirical Study.

BACKGROUND: Access to data is crucial for decision-making; this fact has become more evident during the pandemic. Data collected using mobile apps can positively influence diagnosis and treatment, the supply chain, and the staffing resources of health care facilities. Developers and health care professionals have worked to create apps that can track a person's COVID-19 status. For example, these apps can monitor positive COVID-19 test results and vaccination status. Regrettably, people may be concerned about sharing their data with government or private sector organizations that are developing apps. Understanding user perceptions is essential; without substantial user adoption and the use of mobile tracing apps, benefits cannot be achieved. OBJECTIVE: This study aimed to assess the factors that positively and negatively affect the use of COVID-19 tracing apps by examining individuals' perceptions about sharing data on mobile apps, such as testing regularity, infection, and immunization status. METHODS: The hypothesized research model was tested using a cross-sectional survey instrument. The survey contained 5 reflective constructs and 4 control variables selected after reviewing the literature and interviewing health care professionals. A digital copy of the survey was created using Qualtrics. After receiving approval, data were collected from 367 participants through Amazon Mechanical Turk (MTurk). Participants of any gender who were 18 years or older were considered for inclusion to complete the anonymized survey. We then analyzed the theoretical model using structural equation modeling. RESULTS: After analyzing the quality of responses, 325 participants were included. Of these 325 participants, 216 (66.5%) were male and 109 (33.5%) were female. Among the participants in the final data set, 72.6% (236/325) were employed. The results of structural equation modeling showed that perceived vulnerability (ß=0.688; P<.001), self-efficacy (ß=0.292; P<.001), and an individual's prior infection with COVID-19 (ß=0.194; P=.002) had statistically significant positive impacts on the intention to use mobile tracing apps. Privacy concerns (ß=-0.360; P<.001), risk aversion (ß=-0.150; P=.09), and a family member's prior infection with COVID-19 (ß=-0.139; P=.02) had statistically significant negative influences on a person's intention to use mobile tracing apps. CONCLUSIONS: This study illustrates that various user perceptions affect whether individuals use COVID-19 tracing apps. By working collaboratively on legislation and the messaging provided to potential users before releasing an app, developers, health care professionals, and policymakers can improve the use of tracking apps. Health care professionals need to emphasize disease vulnerability to motivate people to use mobile tracing apps, which can help reduce the spread of viruses and diseases. In addition, more work is needed at the policy-making level to protect the privacy of users, which in return can increase user engagement.

The double-edged effects of data privacy practices on customer responses

Requesting personal information in frontline service encounters raises privacy concerns among customers. The proximity contact tracing that occurred during the COVID-19 pandemic provides an intriguing context of information requests. Hospitality venues required contact tracing details from customers, and customer cooperation varied with concerns about privacy. Drawing on gossip theory, we investigate the roles of businesses' data privacy practices and government support in driving customers' responses to contact tracing. Our findings show that perceived transparency of a business's privacy practices has a positive effect on customers' commitment to the business, while perceived control exerts a negative effect on commitment. These effects are mediated by customers' information falsification rather than disclosure, because the former is a sensitive behavioral indicator of privacy concerns. The results also reveal the moderating roles of government support. This research contributes to the customer data privacy literature by demonstrating the distinct effects of perceived transparency and control on commitment and revealing the underlying mechanism. Moreover, the research extends the conceptual understanding of privacy practices from online contexts to face-to-face contexts of frontline service. The findings offer implications for the management of customer data privacy. © 2022 Elsevier Ltd

Privacy Regulation and Barriers to Public Health

The COVID-19 pandemic has killed millions and gravely disrupted the world's economy. A safe and effective vaccine was developed remarkably swiftly, but as of yet, uptake of the vaccine has been slow. This paper explores one potential explanation of delayed adoption of the vaccine, which is data privacy concerns. We explore two contrasting regulations that vary across U.S. states that have the potential to affect the perceived privacy risk associated with receiving a COVID-19 vaccine. The first regulation—an "identification requirement”—increases privacy concerns by requiring individuals to verify residency with government approved documentation. The second regulation—"anonymity protection”—reduces privacy concerns by allowing individuals to remove personally identifying information from state-operated immunization registry systems. We investigate the effects of these privacy-reducing and privacy-protecting regulations on U.S. state-level COVID-19 vaccination rates. Using a panel data set, we find that identification requirements decrease vaccine demand but that this negative effect is offset when individuals are able to remove information from an immunization registry. Our results remain consistent when controlling for CDC-defined barriers to vaccination, levels of misinformation, vaccine incentives, and states' phased distribution of vaccine supply. These findings yield significant theoretical and practical contributions for privacy policy and public health. © 2022 INFORMS.

Federated machine learning for privacy preserving, collective supply chain risk prediction

The use of Artificial Intelligence (AI) for predicting supply chain risk has gained popularity. However, proposed approaches are based on the premise that organisations act alone, rather than a collective when predicting risk, despite the interconnected nature of supply chains. This yields a problem: organisations that have inadequate datasets cannot predict risk. While data-sharing has been proposed to evaluate risk, in practice this does not happen due to privacy concerns. We propose a federated learning approach for collective risk prediction without the risk of data exposure. We ask: Can organisations who have inadequate datasets tap into collective knowledge? This raises a second question: Under what circumstances would collective risk prediction be beneficial? We present an empirical case study where buyers predict order delays from their shared suppliers before and after Covid-19. Results show that federated learning can indeed help supply chain members predict risk effectively, especially for buyers with limited datasets. Training data-imbalance, disruptions, and algorithm choice are significant factors in the efficacy of this approach. Interestingly, data-sharing or collective risk prediction is not always the best choice for buyers with disproportionately larger order-books. We thus call for further research on on local and collective learning paradigms in supply chains. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Privacy concerns with COVID-19 tracking apps: a privacy calculus approach

Purpose>The COVID-19 pandemic represents a unique challenge for public health worldwide. In this context, smartphone-based tracking apps play an important role in controlling transmission. However, privacy concerns may compromise the population's willingness to adopt this mobile health (mHealth) technology. Based on the privacy calculus theory, this study aims to examine what factors drive or hinder adoption and disclosure, considering the moderating role of age and health status.Design/methodology/approach>A cross-sectional survey was conducted in a European country hit by the pandemic that has recently launched a COVID-19 contact-tracing app. Data from 504 potential users was analyzed through partial least squares structural equation modeling.Findings>Results indicate that perceived benefits and privacy concerns impact adoption and disclosure and confirm the existence of a privacy paradox. However, for young and healthy users, only benefits have a significant effect. Moreover, older people value more personal than societal benefits while for respondents with a chronical disease privacy concerns outweigh personal benefits.Originality/value>The study contributes to consumer privacy research and to the mHealth literature, where privacy issues have been rarely explored, particularly regarding COVID-19 contact-tracing apps. The study re-examines the privacy calculus by incorporating societal benefits and moving from a traditional "self-focus” approach to an "other-focus” perspective. This study further adds to prior research by examining the moderating role of age and health condition, two COVID-19 risk factors. This study thus offers critical insights for governments and health organizations aiming to use these tools to reduce COVID-19 transmission rates.

Users' Expectations, Experiences, and Concerns With COVID Alert, an Exposure-Notification App

We conducted semi-structured interviews with 20 users of Canada's exposure-notification app, COVID Alert. We identified several types of users' mental models for the app. Participants' concerns were found to correlate with their level of understanding of the app. Compared to a centralized contact-tracing app, COVID Alert was favored for its more efficient notification delivery method, its higher privacy protection, and its optional level of cooperation. Based on our findings, we suggest decision-makers rethink the app's privacy-utility trade-off and improve its utility by giving users more control over their data. We also suggest technology companies build and maintain trust with the public. Further, we recommend increasing diagnosed users' motivation to notify the app and encouraging exposed users to follow the guidelines. Last, we provide design suggestions to help users with Unsound and Innocent mental models to better understand the app. © 2022 ACM.

Speculative Vulnerability: Uncovering the Temporalities of Vulnerability in People s Experiences of the Pandemic

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-Computer Interaction (HCI) and for the oversight of app-based public health surveillance. © 2022 Owner/Author.

Factors Affecting Continuance Intention in Non-Face-to-Face Telemedicine Services: Trust Typology and Privacy Concern Perspectives.

As the COVID-19 pandemic progressed, the resulting demand for telemedicine services increased. This research empirically examines the role of trust, privacy concerns, and perceived usefulness in customer confirmation, satisfaction, and continuing intention in telemedicine. A typology of trust was employed to classify trust into three dimensions and explore the mediating role of the three dimensions of trust in the relationship between satisfaction, perceived usefulness, and continued intention. We also examined the moderating role of personal privacy concerns in the relationship between trust and continued intention. For this study, we developed a structural equation model based on expectation confirmation theory and analyzed 465 questionnaires from Chinese online users. The expectancy confirmation theory (ECT) was reaffirmed by empirical evidence. The results showed that the relationship between perceived usefulness and satisfaction with continued intention is moderated by the three dimensions of trust. Privacy concerns can negatively moderate the relationship between structural assurance-based trust and continued intention. This study also identified potential threats to telehealth market growth alongside new insights.

Exploring the challenges of and solutions to sharing personal genomic data for use in healthcare.

Boosted by the COVID-19 pandemic, as well as the tightened General Data Protection Regulation (GDPR) legislation within the European Union (EU), individuals have become increasingly concerned about privacy. This is also reflected in how willing individuals are to consent to sharing personal data, including their health data. To understand this behaviour better, this study focuses on willingness to consent in relation to genomic data. The study explores how the provision of educational information relates to willingness to consent, as well as differences in privacy concerns, information sensitivity and the perceived trade-off value between individuals willing versus unwilling to consent to sharing their genomic data. Of the respondents, 65% were initially willing to consent, but after educational information 89% were willing to consent and only 11% remained unwilling to consent. Educating individuals about potential health benefits can thus help to correct the beliefs that originally led to the unwillingness to share genomic data.

Covid-19 contact tracing through multipath profile similarity

Contact tracing is a key approach to control the spread of Covid-19 and any other pandemia. Recent attempts have followed either traditional ways of tracing (e.g. patient interviews) or unreliable app-based localization solutions. The latter has raised both privacy concerns and low precision in the contact inference. In this work, we present the idea of contact tracing through the multipath profile similarity. At first, we collect Channel State Information (CSI) traces from mobile devices, and then we estimate the multipath profile. We then show that positions that are close obtain similar multipath profiles, and only this information is shared outside the local network. This result can be applied for deploying a privacy-preserving contact tracing system for healthcare authorities. © 2022 Owner/Author.

A Privacy Paradox? Impact of Privacy Concerns on Willingness to Disclose COVID-19 Health Status in the United States

Privacy concerns around sharing personal health information are frequently cited as hindering COVID-19 contact tracing app adoption. We conducted a nationally representative survey of 304 adults in the United States to investigate their attitudes towards sharing two types of COVID-19 health status (COVID-19 Diagnosis, Exposure to COVID-19) with three different audiences (Anyone, Frequent Contacts, Occasional Contacts). Using the Internet User's Information Privacy Concern (IUIPC) scale, we were able to identify the effect of different types of privacy concerns on sharing this information with various audiences. We found that privacy concerns around data Collection predicted lower willingness to share either type of health status to all of these audiences. However, desire for Control and for Awareness of data practices increased willingness to share health information with certain audiences. We discuss the implications of our findings. © 2022 Owner/Author.

Digital, Online, Take-Home - University Students' Attitude towards Different Examination Formats

At German universities, recent semesters have seen a significant move towards the digitalization of summative assessments. This development is in part due to the COVID19 pandemic, which presented a number of challenges with respect to classical exams. In particular, many universities have introduced the possibility of online exams for the very first time during these semesters. In order to investigate the attitude towards such new exam formats among students compared to classical written exams, we present a survey with more than 1,000 university students on their experience with digital and analog exams, taken both in presence and at home. Our results show that these newly introduced formats are well received by students and can, in particular, significantly reduce the stress and anxiety experienced by students before and during examinations. However, privacy concerns need to be addressed by educators relying on digital proctoring systems. © 2022 IEEE.