Wearing masks against 2019 coronavirus (COVID-19) is beneficial in suppressing pandemic spread, not through preventing the wearer from being infected but by preventing the wearer from infecting others. Despite not providing much protection, the custom of wearing masks has prevailed in East Asia from the early stages of the pandemic, especially in Japan, to such an extent that it caused a shortfall in supply. Why do many Japanese people wear masks during the COVID-19 pandemic, even though masks are unlikely to prevent them from getting infected? We examined six possible psychological reasons for wearing masks: three involved expectations about the risk of infection and three involved other driving psychological forces. The results of our nationwide survey revealed that people conformed to societal norms in wearing masks and felt relief from anxiety when wearing masks. However, risk reduction expectations did not affect mask usage. The social psychological motivations successfully explained much about mask usage. Our findings suggest that policymakers responsible for public health should consider social motivations when implementing public strategies to combat the COVID-19 pandemic.

Artificial intelligence (AI) can provide many benefits in healthcare, including rapid and effective treatment options. However, previous research on human–computer interactions has demonstrated that people are reluctant to accept AI. This study compared individuals’ trust in AI with their trust in doctors regarding medical treatment and explored whether people trust an AI system that understands and suggests their desired treatments. We conducted an online experiment (N = 415) adopting a scenario method. The participants were asked to imagine receiving a medical examination and being prescribed medicine by a physician or an AI system. Participants trusted the AI system less than a doctor, even when the AI system learned and suggested their desired treatment. This finding confirms that people are reluctant to trust AI even if it performs at the level of a human doctor, thereby strengthening the existent literature that shows people prefer human doctors.

This article empirically examines the effectiveness of earthquake early warning (EEW) in Japan based on experiences of residents who received warnings before earthquake shaking occurred. In Study 1, a survey (N = 299) was conducted to investigate residents’ experiences of, and reactions to, an EEW issued in Gunma and neighboring regions on June 17, 2018. The main results were as follows. (1) People's primary reactions to the EEW were mental, not physical, and thus motionless. Most residents stayed still, not for safety reasons, but because they were focusing on mentally bracing themselves. (2) Residents perceived the EEW to be effective because it enabled them to mentally prepare, rather than take physical protective actions, before strong shaking arrived. (3) In future, residents anticipate that on receipt of an EEW they would undertake mental preparation as opposed to physical protective actions. In Study 2, a survey (N = 450) was conducted on another EEW issued for an earthquake offshore of Chiba Prefecture on July 7, 2018. Results were in line with those of Study 1, suggesting that the findings described above are robust. Finally, given people's lack of impetus to undertake protective action on receipt of an EEW, this article discusses ways to enhance such actions.

Earthquake early warning (EEW) can be used to detect earthquakes and provide advanced notification of strong shaking, allowing pre-emptive actions to be taken that not only benefit infrastructure but reduce injuries and fatalities. Currently Aotearoa New Zealand does not have a nationwide EEW system, so a survey of the public was undertaken to understand whether EEW was considered useful and acceptable by the public, as well as perceptions of how and when such warnings should be communicated, before making an investment in such technology. We surveyed the public’s perspectives (N = 3084) on the usefulness of EEW, preferred system attributes, and what people anticipated doing on receipt of a warning. We found strong support for EEW, for the purposes of being able to undertake actions to protect oneself and others (e.g. family, friends, and pets), and to mentally prepare for shaking. In terms of system attributes, respondents expressed a desire for being warned at a threshold of shaking intensity MM5–6. They suggested a preference for receiving a warning via mobile phone, supported by other channels. In addition to being warned about impending shaking, respondents wanted to receive messages that alerted them to other attributes of the earthquake (including the possibility of additional hazards such as tsunami), and what actions to take. People’s anticipated actions on receipt of a warning varied depending on the time available from the warning to arrival of shaking. People were more likely to undertake quicker and easier actions for shorter timeframes of <10 s (e.g., stop, mentally prepare, take protective action), and more likely to move to a nearby safe area, help others, look for more information, or take safety actions as timeframes increased. Given the public endorsement for EEW, information from this survey can be used to guide future development in Aotearoa New Zealand and internationally with respect to system attributes, sources, channels and messages, in ways that promote effective action.

Autonomous cars (ACs) controlled by artificial intelligence are expected to play a significant role in transportation in the near future. This study investigated determinants of trust in ACs. Trust in ACs influences different variables, including the intention to adopt AC technology. Several studies on risk perception have verified that shared value determines trust in risk managers. Previous research has confirmed the effect of value similarity on trust in artificial intelligence. We focused on moral beliefs, specifically utilitarianism (belief in promoting a greater good) and deontology (belief in condemning deliberate harm), and tested the effects of shared moral beliefs on trust in ACs. We conducted three experiments (N = 128, 71, and 196, for each), adopting a thought experiment similar to the well-known trolley problem. We manipulated shared moral beliefs (shared vs. unshared) and driver (AC vs. human), providing participants with different moral dilemma scenarios. Trust in ACs was measured through a questionnaire. The results of Experiment 1 showed that shared utilitarian belief strongly influenced trust in ACs. In Experiment 2 and Experiment 3, however, we did not find statistical evidence that shared deontological belief had an effect on trust in ACs. The results of the three experiments suggest that the effect of shared moral beliefs on trust varies depending on the values that ACs share with humans. To promote AC implementation, policymakers and developers need to understand which values are shared between ACs and humans to enhance trust in ACs.