Unpredictable robots elicit responsibility attributions.

Do people hold robots responsible for their actions? While Clark and Fischer present a useful framework for interpreting social robots, we argue that they fail to account for people's willingness to assign responsibility to robots in certain contexts, such as when a robot performs actions not predictable by its user or programmer.

Using text and charts to provide social norm feedback to general practices with high overall and high broad-spectrum antibiotic prescribing: a series of national randomised controlled trials.

BACKGROUND: Sending a social norms feedback letter to general practitioners who are high prescribers of antibiotics has been shown to reduce antibiotic prescribing. The 2017-9 Quality Premium for primary care in England sets a target for broad-spectrum prescribing, which should be at or below 10% of total antibiotic prescribing. We tested a social norm feedback letter that targeted broad-spectrum prescribing and the addition of a chart to a text-only letter that targeted overall prescribing. METHODS: We conducted three 2-armed randomised controlled trials, on different groups of practices: Trial A compared a broad-spectrum message and chart to the standard-practice overall prescribing letter (practices whose percentage of broad-spectrum prescribing was above 10% and who had relatively high overall prescribing). Trial C compared a broad-spectrum message and a chart to a no-letter control (practices whose percentage of broad-spectrum prescribing was above 10% and who had relatively moderate overall prescribing). Trial B compared an overall-prescribing message with a chart to the standard practice overall letter (practices whose percentage of broad-spectrum prescribing was below 10% but who had relatively high overall prescribing). Letters were posted to general practitioners, timed to be received on 1 November 2018. The primary outcomes were practices' percentage of broad-spectrum prescribing (trials A and C) and overall antibiotic prescribing (trial B) each month from November 2018 to April 2019 (all weighted by the number and characteristics of patients registered in the practice). RESULTS: We randomly assigned 1909 practices; 58 closed or merged during the trial, leaving 1851 practices: 385 in trial A, 674 in trial C, and 792 in trial B. AR(1) models showed that there were no statistically significant differences in our primary outcome measures: trial A ß = - .199, p = .13; trial C ß = .006, p = .95; trial B ß = - .0021, p = .81. In all three trials, there were statistically significant time trends, showing that overall antibiotic prescribing and total broad-spectrum prescribing were decreasing. CONCLUSION: Our broad-spectrum feedback letters had no effect on broad-spectrum prescribing; adding a bar chart to a text-only letter had no effect on overall antibiotic prescribing. Broad-spectrum and overall prescribing were both decreasing over time. TRIAL REGISTRATION: ClinicalTrials.gov NCT03862794. March 5, 2019.

Blaming automated vehicles in difficult situations.

Automated vehicles (AVs) have made huge strides toward large-scale deployment. Despite this progress, AVs continue to make mistakes, some resulting in death. Although some mistakes are avoidable, others are hard to avoid even by highly skilled drivers. As these mistakes continue to shape attitudes toward AVs, we need to understand whether people differentiate between them. We ask the following two questions. When an AV makes a mistake, does the perceived difficulty or novelty of the situation predict blame attributed to it? How does that blame attribution compare to a human driving a car? Through two studies, we find that the amount of blame people attribute to AVs and human drivers is sensitive to situation difficulty. However, while some situations could be more difficult for AVs and others for human drivers, people blamed AVs more, regardless. Our results provide novel insights in understanding psychological barriers influencing the public's view of AVs.

A Study Protocol for Testing the Effectiveness of User-Generated Content in Reducing Excessive Consumption.

Excessive consumption is on the rise, as is apparent in growing financial debt and global greenhouse gas emissions. Voluntary simplicity, a lifestyle choice of reduced consumption and sustainable consumer behavior, provides a potential solution for excessive consumers. However, voluntary simplicity is unpopular, difficult to adopt, and under researched. The outlined research project will test a method of promoting voluntary simplicity via user-generated content, thus mimicking an existing social media trend (Minimalism) in an empirical research design. The project will test (a) whether the Minimalism trend could benefit consumers interested in reducing their consumption, and (b) whether self-transcendence (i.e., biospheric) and self-enhancement (i.e., egoistic and hedonic) values and goals have a similar impact in promoting voluntary simplicity. A one-week intervention program will test the efficacy of watching user-generated voluntary simplicity videos in reducing non-essential consumption. Each of the two intervention conditions will present participants with similar tutorial videos on consumption reduction (e.g., decluttering, donating), while priming the relevant values and goals (self-transcendence or self-enhancement). These interventions will be compared to a control condition, involving no user-generated content. Participants will undergo baseline and post-intervention evaluations of: voluntary simplicity attitudes and behaviors, buying and shopping behaviors, values and goals in reducing consumption, and life satisfaction. Experience sampling will monitor affective state during the intervention. We provide a detailed stepwise procedure, materials, and equipment necessary for executing this intervention. The outlined research design is expected to contribute to the limited literature on voluntary simplicity, online behavioral change interventions, and the use of social marketing principles in consumer interventions.