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Previous research is inconclusive regarding the effects of paper and web surveys on response burdens. We conducted an establishment survey with random assignment to paper and web modes to examine this issue. We compare how the actual and perceived response burdens differ when respondents complete a survey in the paper mode, in the web mode and when they are allowed to choose between the two modes. Our results show that in the web mode, respondents have a lower estimated time to complete the questionnaire, while we do not find differences between paper and the web on the perceived response time and perceived burden. Even though the response burden in the web mode is lower, our study finds no evidence of an increased response burden when moving an establishment survey from paper to the web.

PurposeDigital trace data provide new opportunities to study how individuals act and interact with others online. One advantage of this type of data is that it measures behavior in a less obtrusive way than surveys, potentially reducing measurement error. However, it is well documented that in observational studies, participants' awareness of being observed can change their behavior, especially when the behavior is considered sensitive. Very little is known regarding this effect in the online realm. Against this background, we studied whether people change their online behavior because digital trace data are being collected.Design/methodology/approachWe analyzed data from a sample of 1,959 members of a German online panel who had consented to the collection of digital trace data about their online browsing and/or mobile app usage. To identify reactivity, we studied change over time in five types of sensitive online behavior.FindingsWe found that the frequency and duration with which individuals engage in sensitive behaviors online gradually increases during the first couple of days after the installation of a tracker, mainly individuals who extensively engage in sensitive behavior show this pattern of increase after installation and this change in behavior is limited to certain types of sensitive online behavior.Originality/valueThere is an increased interest in the use of digital trace data in the social sciences and our study is one of the first methodological contributions measuring reactivity in digital trace data measurement.

The COVID-19 pandemic has spotlighted the importance of high-quality data for empirical health research and evidence-based political decision-making. To leverage the full potential of these data, a better understanding of the determinants and conditions under which people are willing to share their health data is critical. Building on the privacy theory of contextual integrity, the privacy calculus, and previous findings regarding different data types and recipients, we argue that established social norms shape the acceptance of novel practices of data collection and use. To investigate the willingness to share health data, we conducted a preregistered vignette experiment. The scenarios experimentally varied the vignette dimensions by data type, recipient, and research purpose. While some findings contradict our hypotheses, the results indicate that all three dimensions affected respondents’ data sharing decisions. Additional analyses suggest that institutional and social trust, privacy concerns, technical affinity, altruism, age, and device ownership influence the willingness to share health data.

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making (ADM) may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM.

BACKGROUND The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs. One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19. Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention. OBJECTIVE The objective of this study is to investigate the user acceptability of a contact-tracing app in five countries hit by the pandemic. METHODS We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections. We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States. We measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries. RESULTS We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption. CONCLUSIONS Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.