Investigating Citizens' Acceptance of Contact Tracing Apps: Quantitative Study of the Role of Trust and Privacy.

BACKGROUND: The COVID-19 pandemic accelerated the need to understand citizen acceptance of health surveillance technologies such as contact tracing (CT) apps. Indeed, the success of these apps required widespread public acceptance and the alleviation of concerns about privacy, surveillance, and trust. OBJECTIVE: This study aims to examine the factors that foster a sense of trust and a perception of privacy in CT apps. Our study also investigates how trust and perceived privacy influence citizens' willingness to adopt, disclose personal data, and continue to use these apps. METHODS: Drawing on privacy calculus and procedural fairness theories, we developed a model of the antecedents and behavioral intentions related to trust and privacy perceptions. We used structural equation modeling to test our hypotheses on a data set collected at 2 time points (before and after the launch of a national CT app). The sample consisted of 405 Irish residents. RESULTS: Trust in CT apps was positively influenced by propensity to trust technology (ß=.074; P=.006), perceived need for surveillance (ß=.119; P<.001), and perceptions of government motives (ß=.671; P<.001) and negatively influenced by perceived invasion (ß=-.224; P<.001). Perceived privacy was positively influenced by trust (ß=.466; P<.001) and perceived control (ß=.451; P<.001) and negatively influenced by perceived invasion (ß=-.165; P<.001). Prelaunch intentions toward adoption were influenced by trust (ß=.590; P<.001) and perceived privacy (ß=.247; P<.001). Prelaunch intentions to disclose personal data to the app were also influenced by trust (ß=.215; P<.001) and perceived privacy (ß=.208; P<.001) as well as adoption intentions before the launch (ß=.550; P<.001). However, postlaunch intentions to use the app were directly influenced by prelaunch intentions (ß=.530; P<.001), but trust and perceived privacy only had an indirect influence. Finally, with regard to intentions to disclose after the launch, use intentions after the launch (ß=.665; P<.001) and trust (ß=.215; P<.001) had a direct influence, but perceived privacy only had an indirect influence. The proposed model explained 74.4% of variance in trust, 91% of variance in perceived privacy, 66.6% of variance in prelaunch adoption intentions, 45.9% of variance in postlaunch use intentions, and 83.9% and 79.4% of variance in willingness to disclose before the launch and after the launch, respectively. CONCLUSIONS: Positive perceptions of trust and privacy can be fostered through clear communication regarding the need and motives for CT apps, the level of control citizens maintain, and measures to limit invasive data practice. By engendering these positive beliefs before launch and reinforcing them after launch, citizens may be more likely to accept and use CT apps. These insights are important for the launch of future apps and technologies that require mass acceptance and information disclosure.

#Putkids1st: Health Professionals Using Social Media for Public Policy Advocacy-From Collective Action to Connective Action.

This study examines public policy advocacy by pediatricians and other health professionals in the hashtag community: #putkids1st. The study explores 4321 tweets that feature the hashtag, generated by 1231 unique users largely drawn from the American Association of Pediatricians and its members. The data are used to explore the structural dynamics of the hashtag community, the role of homophily, and to test a source-message framework to predict and recommendations to help improve engagement and retransmission of professional health advocacy messages.

Corrigendum to "A dataset for accounting, finance and economics research on US data breaches" [Data in Brief 35 (2021) 1-6/ 106924].

[This corrects the article DOI: 10.1016/j.dib.2021.106924.].

A dataset for accounting, finance and economics research on US data breaches.

This data article describes a dataset of data breaches in US listed firms over a ten-year period. Data breaches represent major events that pose serious challenges to organisations. The number of incidents has been on the increase over the last decade and this has attracted the interest of the media, consumers and regulators. While there is a well-established literature on cybersecurity in Computer Science and Information Systems journals, studies exploring the economic and business impacts of data breaches represent a relatively recent phenomenon. There is a nascent but fast-growing literature in accounting, finance and economics that focuses on the financial impacts of data breaches and this dataset provides a useful resource for future studies in this space. By providing data on the company identifier, the type of breach, the dates of breach disclosure, and relates these dates to the company's fiscal year, the dataset can be merged quickly with existing accounting and finance datasets. The dataset includes data on 506 incidents over a ten-year period thereby enabling cross-sectional and longitudinal analyses.

Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter.

Over 2.8 million people die each year from being overweight or obese, a largely preventable disease. Social media has fundamentally changed the way we communicate, collaborate, consume, and create content. The ease with which content can be shared has resulted in a rapid increase in the number of individuals or organisations that seek to influence opinion and the volume of content that they generate. The nutrition and diet domain is not immune to this phenomenon. Unfortunately, from a public health perspective, many of these 'influencers' may be poorly qualified in order to provide nutritional or dietary guidance, and advice given may be without accepted scientific evidence and contrary to public health policy. In this preliminary study, we analyse the 'healthy diet' discourse on Twitter. While using a multi-component analytical approach, we analyse more than 1.2 million English language tweets over a 16-month period in order to identify and characterise the influential actors and discover topics of interest in the discourse. Our analysis suggests that the discourse is dominated by non-health professionals. There is widespread use of bots that pollute the discourse and seek to create a false equivalence on the efficacy of a particular nutritional strategy or diet. Topic modelling suggests a significant focus on diet, nutrition, exercise, weight, disease, and quality of life. Public health policy makers and professional nutritionists need to consider what interventions can be taken in order to counteract the influence of non-professional and bad actors on social media.

Data set for automatic detection of online misogynistic speech.

The data set is composed of 2285 definitions posted on the Urban Dictionary platform from 1999 to May 2016. The data was classified as misogynistic and non-misogynistic by three independent researchers with domain knowledge. The data set is available in public repository in a table containing two columns: the text-based definition from Urban Dictionary and its respective classification (1 for misogynistic and 0 for non-misogynistic).