Advancements in big data analytics offer new avenues for the analysis and deciphering of suspicious activities on the internet. One promising new technology to increase the identification of terrorism threats is based on probabilistic computing. The technology promises to provide more efficient problem solutions in encryption and cybersecurity. Probabilistic computing technologies use large amounts of data, though, which raises potential privacy concerns. A study ( N = 1,023) was conducted to survey public support for using probabilistic computing technologies to increase counterterrorism efforts. Overall, strong support was found for the use of publicly available personal information (e.g., personal websites). Regarding private personal information (e.g., online conversations), respondents perceived it to be more appropriate to use information from out-group members (non-American citizens) than from in-group members (American citizens). In line with a social-identity account, this form of in-group favoritism was strongest among respondents displaying a combination of strong national identities and strong privacy concerns.
An increasing number of residential homes are equipped with smart assistants such as Cortana, Alexa, and Siri. Adoption rates and the frequency of the usage of smart assistants vary across users and residential homes. Building on the theory of uses and gratifications (UGT) and the unified theory of acceptance and use of technology 2 (UTAUT2), the objective of this paper was to examine whether the intended use of a digital assistant would moderate the effects of performance expectancy and hedonic motivation on its adoption. Two experiments (N = 345 and N = 351) tested the hypothesis that, for utilitarian purposes, devices with high performance appraisal are preferred, whereas for entertainment purposes, devices with high hedonic appraisal are preferred. The experiments manipulated the performance expectancy and hedonic motivation towards several digital assistants by varying how the assistants were introduced. Participants were asked which assistant they would choose for a variety of utilitarian and entertainment purposes. As expected, the experiments supported the proposed matching hypothesis, revealing that the devices that were high in performance appraisal were preferred for utilitarian tasks, whereas the devices high in hedonic appraisal were preferred for entertainment needs. These results suggest that a device’s introduction can change people’s perceptions of the device and subsequently their decision to use it.
Terrorist threats and attacks provide major risks and sources of public crises in the 21st century. New probabilistic computing technologies possess the capability of increasing the success of identifying terrorist threats and solving cybersecurity and encryption problems more efficiently. However, to identify terrorist threats, these technologies would require the use of a large amount of personal data, which cause potential concerns for privacy. We offer a social-identity explanation of public support for the detection of terrorist threats through the use of online personal data. A survey study ( N = 1,204) revealed strong support for the provided social-identity explanation of public perceptions. As expected, respondents displayed in-group favoritism by more strongly supporting the use of private personal information from out-group members (non-U.S. citizens) than from in-group members (U.S. citizens). The observed in-group favoritism was most pronounced when respondents had both a strong national identity and a strong sense of general privacy concern. The observed differences were independent of respondents’ political orientation and age.
There exist today many forms of anti-scientific beliefs, from extreme views like the QAnon conspiracies, to misconceptions about vaccines and cancer treatment. The COVID-19 pandemic has presented to us a situation in which the public is being asked by medical experts and politicians alike to trust in science and follow after various health recommendations like wearing masks or getting vaccinated against the virus. We used an anti-science belief scale [Morgan et al., 2018] to assess how preexisting beliefs that run counter to the scientific narrative predict behaviors during the pandemic. We found that people who were more accepting of those anti-scientific positions trusted medical information and experts less and engaged less in recommended health behaviors, while simultaneously showing a more favorable view of Trump's actions as President during the pandemic.
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