The Roles of Trust in Government and Sense of Community in the COVID-19 Contact Tracing Privacy Calculus: Mixed Method Study Using a 2-Wave Survey and In-Depth Interviews

BACKGROUND

Contact tracing technology has been adopted in many countries to aid in identifying, evaluating, and handling individuals who have had contact with those infected with COVID-19. Singapore was among the countries that actively implemented the government-led contact tracing program known as TraceTogether. Despite the benefits the contact tracing program could provide to individuals and the community, privacy issues were a significant barrier to individuals' acceptance of the program.

OBJECTIVE

Building on the privacy calculus model, this study investigates how the perceptions of the 2 key groups (ie, government and community members) involved in the digital contact tracing factor into individuals' privacy calculus of digital contact tracing.

METHODS

Using a mixed method approach, we conducted (1) a 2-wave survey (n=674) and (2) in-depth interviews (n=12) with TraceTogether users in Singapore. Using structural equation modeling, this study investigated how trust in the government and the sense of community exhibited by individuals during the early stage of implementation (time 1) predicted privacy concerns, perceived benefits, and future use intentions, measured after the program was fully implemented (time 2). Expanding on the survey results, this study conducted one-on-one interviews to gain in-depth insights into the privacy considerations involved in digital contact tracing.

RESULTS

The results from the survey showed that trust in the government increased perceived benefits while decreasing privacy concerns regarding the use of TraceTogether. Furthermore, individuals who felt a connection to community members by participating in the program (ie, the sense of community) were more inclined to believe in its benefits. The sense of community also played a moderating role in the influence of government trust on perceived benefits. Follow-up in-depth interviews highlighted that having a sense of control over information and transparency in the government's data management were crucial factors in privacy considerations. The interviews also highlighted surveillance as the most prevalent aspect of privacy concerns regarding TraceTogether use. In addition, our findings revealed that trust in the government, particularly the perceived transparency of government actions, was most strongly associated with concerns regarding the secondary use of data.

CONCLUSIONS

Using a mixed method approach involving a 2-wave survey and in-depth interview data, we expanded our understanding of privacy decisions and the privacy calculus in the context of digital contact tracing. The opposite influences of privacy concerns and perceived benefit on use intention suggest that the privacy calculus in TraceTogether might be viewed as a rational process of weighing between privacy risks and use benefits to make an uptake decision. However, our study demonstrated that existing perceptions toward the provider and the government in the contact tracing context, as well as the perception of the community triggered by TraceTogether use, may bias user appraisals of privacy risks and the benefits of contact tracing.

Extending the Privacy Calculus to the mHealth Domain: Survey Study on the Intention to Use mHealth Apps in Germany

BACKGROUND

With the increasing digitalization of the health sector, more and more mobile health (mHealth) apps are coming to the market to continuously collect and process sensitive health data for the benefit of patients and providers. These technologies open up new opportunities to make the health care system more efficient and save costs but also pose potential threats such as loss of data or finances.

OBJECTIVE

This study aims to present an empirical review and adaptation of the extended privacy calculus model to the mHealth domain and to understand what factors influence the intended usage of mHealth technologies.

METHODS

A survey study was conducted to empirically validate our model, using a case vignette as cover story. Data were collected from 250 German participants and analyzed using a covariance-based structural equation model.

RESULTS

The model explains R2=79.3% of the variance in intention to use. The 3 main factors (social norms, attitude to privacy, and perceived control over personal data) influenced the intention to use mHealth apps, albeit partially indirectly. The intention to use mHealth apps is driven by the perceived benefits of the technology, trust in the provider, and social norms. Privacy concerns have no bearing on the intention to use. The attitude to privacy has a large inhibiting effect on perceived benefits, as well as on trust in the provider. Perceived control over personal data clearly dispels privacy concerns and supports the relationship of trust between the user and the provider.

CONCLUSIONS

Based on the privacy calculus, our domain-specific model explains the intention to use mHealth apps better than previous, more general models. The findings allow health care providers to improve their products and to increase usage by targeting specific user groups.

Determining factors affecting the user's intention to disclose privacy in online health communities: a dual-calculus model

Background

As a new type of medical service application for doctor-patient interaction, online health communities (OHCs) have alleviated the imbalance between the supply and demand of medical resources in different regions and the problems of "difficult and expensive access to medical care", but also raised the concern of patients about the risk of disclosure of their health privacy information.

Methods

In this study, a dual-calculus model was developed to explore users' motivation and decision-making mechanism in disclosing privacy information in OHCs by combining risk calculus and privacy calculus theories.

Results

In OHCs, users' trust in physicians and applications is a prerequisite for their willingness to disclose health information. Meanwhile, during the privacy calculation, users' perceived benefits in OHCs had a positive effect on both trust in doctors and trust in applications, while perceived risks had a negative effect on both trusts in doctors and trust in applications. Furthermore, in the risk calculation, the perceived threat assessment in OHCs had a significant positive effect on perceived risk, while the response assessment had a significant negative effect on perceived risk, and the effect of users' trust in physicians far exceeded the effect of trust in applications. Finally, users' trust in physicians/applications is a mediating effect between perceived benefits/risks and privacy disclosure intentions.

Conclusion

We combine risk calculus and privacy calculus theories to construct a dual-calculus model, which divides trust into trust in physicians and trust in applications, in order to explore the intrinsic motivation and decision-making mechanism of users' participation in privacy disclosure in OHCs. On the one hand, this theoretically compensates for the fact that privacy computing often underestimates perceived risk, complements the research on trust in OHCs, and reveals the influencing factors and decision transmission mechanisms of user privacy disclosure in OHCs. On the other hand, it also provides guidance for developing reasonable privacy policies and health information protection mechanisms for platform developers of OHCs.

Factors Influencing the Sharing of Personal Health Data Based on the Integrated Theory of Privacy Calculus and Theory of Planned Behaviors Framework: Results of a Cross-Sectional Study of Chinese Patients in the Yangtze River Delta

BACKGROUND

The health care system in China is fragmented, and the distribution of high-quality resources remains uneven and irrational. Information sharing is essential to the development of an integrated health care system and maximizing its benefits. Nevertheless, data sharing raises concerns regarding the privacy and confidentiality of personal health information, which affect the willingness of patients to share information.

OBJECTIVE

This study aims to investigate patients' willingness to share personal health data at different levels of maternal and child specialized hospitals in China, to propose and test a conceptual model to identify key influencing factors, and to provide countermeasures and suggestions to improve the level of data sharing.

METHODS

A research framework based on the Theory of Privacy Calculus and the Theory of Planned Behavior was developed and empirically tested through a cross-sectional field survey from September 2022 to October 2022 in the Yangtze River Delta region, China. A 33-item measurement instrument was developed. Descriptive statistics, chi-square tests, and logistic regression analyses were conducted to characterize the willingness of sharing personal health data and differences by sociodemographic factors. Structural equation modeling was used to assess the reliability and validity of the measurement as well as to test the research hypotheses. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist for cross-sectional studies was applied for reporting results.

RESULTS

The empirical framework had a good fit with the chi-square/degree of freedom (χ2/df)=2.637, root-mean-square residual=0.032, root-mean-square error of approximation=0.048, goodness-of-fit index=0.950, and normed fit index=0.955. A total of 2060 completed questionnaires were received (response rate: 2060/2400, 85.83%). Moral motive (β=.803, P<.001), perceived benefit (β=.123, P=.04), and perceived effectiveness of government regulation (β=.110, P=.001) had a significantly positive association with sharing willingness, while perceived risk (β=-.143, P<.001) had a significant negative impact, with moral motive having the greatest impact. The estimated model explained 90.5% of the variance in sharing willingness.

CONCLUSIONS

This study contributes to the literature on personal health data sharing by integrating the Theory of Privacy Calculus and the Theory of Planned Behavior. Most Chinese patients are willing to share their personal health data, which is primarily motivated by moral concerns to improve public health and assist in the diagnosis and treatment of illnesses. Patients with no prior experience with personal information disclosure and those who have tertiary hospital visits were more likely to share their health data. Practical guidelines are provided to health policy makers and health care practitioners to encourage patients to share their personal health information.

How do risks and benefits affect user' privacy decisions? An event-related potential study on privacy calculus process

Purpose

The purpose of this study is to examine how risks and benefits affect users' privacy-related decision-making processes.

Design/methods/approach

This study collected and analyzed the neural activity processes of users' privacy-related decisions when faced with personalized services with different risks and benefits through an ERP experiment that included 40 participants.

Findings/results

The findings show that users subconsciously categorize personalized services based on benefit; Privacy calculus affects privacy decision by influencing the allocation of cognitive resources for personalized service, and the scarcity of cognitive resources increases the degree of privacy disclosure; Emotional change in privacy decision is the result of many factors, not the result of privacy risk alone.

Originality/Discussion

This study provides a new perspective to explain the process of privacy decision-making, and a new approach to investigate the privacy paradox.

High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis

Taiwan has been a world leader in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic. Recently, the Taiwan Government launched its COVID-19 tracing app, 'Taiwan Social Distancing App'; however, the effectiveness of this tracing app depends on its acceptance and uptake among the general population. We measured the acceptance of three hypothetical tracing technologies (telecommunication network tracing, a government app, and the Apple and Google Bluetooth exposure notification system) in four nationally representative Taiwanese samples. Using Bayesian methods, we found a high acceptance of all three tracking technologies, with acceptance increasing with the inclusion of additional privacy measures. Modeling revealed that acceptance increased with the perceived technology benefits, trust in the providers' intent, data security and privacy measures, the level of ongoing control, and one's level of education. Acceptance decreased with data sensitivity perceptions and a perceived low policy compliance by others among the general public. We consider the policy implications of these results for Taiwan during the COVID-19 pandemic and in the future.

Privacy Personas for IoT-Based Health Research: A Privacy Calculus Approach

The reliance on data donation from citizens as a driver for research, known as citizen science, has accelerated during the Sars-Cov-2 pandemic. An important enabler of this is Internet of Things (IoT) devices, such as mobile phones and wearable devices, that allow continuous data collection and convenient sharing. However, potentially sensitive health data raises privacy and security concerns for citizens, which research institutions and industries must consider. In e-commerce or social network studies of citizen science, a privacy calculus related to user perceptions is commonly developed, capturing the information disclosure intent of the participants. In this study, we develop a privacy calculus model adapted for IoT-based health research using citizen science for user engagement and data collection. Based on an online survey with 85 participants, we make use of the privacy calculus to analyse the respondents' perceptions. The emerging privacy personas are clustered and compared with previous research, resulting in three distinct personas which can be used by designers and technologists who are responsible for developing suitable forms of data collection. These are the 1) Citizen Science Optimist, the 2) Selective Data Donor, and the 3) Health Data Controller. Together with our privacy calculus for citizen science based digital health research, the three privacy personas are the main contributions of this study.

Health Privacy Information Self-Disclosure in Online Health Community

The scarcity of medical resources is a fundamental problem worldwide; the development of information technology and the Internet has given birth to online health care, which has alleviated the above problem. The survival and sustainable development of the online health community requires users to continuously disclose their health and privacy. Therefore, it is a great practical significance to find out the factors and mechanisms that promote users' self-disclosure in the online health community. From the perspective of individual and situation interaction, this study constructed influencing factors model of health privacy information self-disclosure. Finally, we collected 264 valid samples from the online health community through online and offline questionnaire surveys and then use the SPSS20.0 and AMOS21.0 to conduct exploratory factor analysis, confirmatory factor analysis, scale reliability and validity analysis, and structural equation model analysis. The main findings are as follows: trust in websites and trust in doctors reduce the privacy concern. The privacy trade-off will not occur when trust is enough to offset the privacy concerns caused by personalized services, reciprocity norms, and other factors. Second, reciprocity norms are inevitably compulsive, which will increase privacy concerns. However, based on voluntariness, reciprocity norms can enhance user trust. Third, service quality caused by personalized services not only enhance the social rewards of users but also eliminate the privacy concern. Fourth, users' health privacy attention and information sensitivity are too high to decrease the influence of user' privacy concerns on personal health privacy information disclosure. The conclusions of this paper will help us to supplement privacy calculus theory and the application scope of the attention-based view. The proposed strategy of this article can be used to stimulate the information contribution behavior of users and improve the medical service capabilities in online health community.

Young Adults View Smartphone Tracking Technologies for COVID-19 as Acceptable: The Case of Taiwan

Taiwan has been successful in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic; however, without a vaccine the threat of a second outbreak remains. Young adults who show few to no symptoms when infected have been identified in many countries as driving the virus' spread through unidentifiable community transmission. Mobile tracking technologies register nearby contacts of a user and notifies them if one later tests positive to the virus, potentially solving this issue; however, the effectiveness of these technologies depends on their acceptance by the public. The current study assessed attitudes towards three tracking technologies (telecommunication network tracking, a government app, and Apple and Google's Bluetooth exposure notification system) among four samples of young Taiwanese adults (aged 25 years or younger). Using Bayesian methods, we find high acceptance for all three tracking technologies (>75%), with acceptance for each technology surpassing 90% if additional privacy measures were included. We consider the policy implications of these results for Taiwan and similar cultures.

Out of Control - Privacy Calculus and the Effect of Perceived Control and Moral Considerations on the Usage of IoT Healthcare Devices

People are increasingly applying Internet of Things (IoT) devices that help them improve their fitness and provide information about their state of health. Although the acceptance of healthcare devices is increasing throughout the general population, IoT gadgets are reliant on sensitive user data in order to provide full functioning and customized operation. More than in other areas of IoT, healthcare applications pose a challenge to individual privacy. In this study, we examine whether actual and perceived control of collected data affects the willingness to use an IoT healthcare device. We further measure actual behavior as a result of a risk-benefit trade-off within the framework of privacy calculus theory. Our experiment with N = 209 participants demonstrates that while actual control does not affect the willingness to use IoT in healthcare, people have a higher intention to use an IoT healthcare device when they perceive to be in control of their data. Furthermore, we found that, prior to their decision, individuals weigh perceived risks and anticipated benefits of information disclosure, which demonstrates the potential to apply the privacy calculus in the context of IoT healthcare technology. Finally, users' moral considerations of IoT in healthcare are discussed.

Unveiling consumer's privacy paradox behaviour in an economic exchange

Privacy paradox is of great interest to IS researchers and firms gathering personal information. It has been studied from social, behavioural, and economic perspectives independently. However, prior research has not examined the degrees of influence these perspectives contribute to the privacy paradox problem. We combine both economic and behavioural perspectives in our study of the privacy paradox with a price valuation of personal information through an economic experiment combined with a behavioural study on privacy paradox. Our goal is to reveal more insights on the privacy paradox through economic valuation on personal information. Results indicate that general privacy concerns or individual disclosure concerns do not have a significant influence on the price valuation of personal information. Instead, prior disclosure behaviour in specific scenario, like with healthcare providers or social networks, is a better indicator of consumer price valuations.