An empirical analysis of American Passenger's willingness to fly in commercial airplanes after vaccination against COVID-19.

Although several studies have explored the effects of the pandemic on aviation, little remains known about whether members of the public are willing to fly again after they have been vaccinated. The current study uses the Health Belief Model (HBM) to fill this missing gap by manipulating the following variables: 1) whether or not the participant is vaccinated; 2) whether or not airlines require that all passengers and crew receive vaccinations; 3) length of flight; 4) destination; and 5) the number of passengers. The data from 678 participants revealed that willingness to fly is much higher if the participants themselves have been vaccinated, if the airlines require all passengers to be vaccinated, if the flight is short, if the destination is domestic, and if the number of passengers is low. These findings did not appear to differ as a function of flying business versus pleasure. We discuss the practical implications of these data as airlines struggle to bring back their customer base.

Building a predictive model of U.S. patient willingness to undergo robotic surgery.

Prior research regarding robotic surgery (RS) has largely focused on the engineering or medical aspects of these tools. A few studies have examined consumer opinions toward, or willingness to use, robotic surgeons; however, no study to date has examined what type of person would be willing to undergo RS. Across two studies, the current research fills this gap by building both a descriptive and predictive regression model used to predict what type of user would be willing to undergo RS. To build the descriptive model, 1324 potential patients were asked a series of questions about demographics, attitudes, opinions, and personalities. Results indicate that perceived value, familiarity, wariness of new technologies, fear of surgery, openness, anger, fear, and happiness are all significant predictors of willingness to undergo RS. A regression equation was developed and then used to predict scores in a second study with 1335 potential patients. The scores from both studies were compared for model fit. Several methods were used to validate the regression model, including correlational analyses, a t test, and calculation of the cross-validity coefficient. All three stringent tests showed strong model fit, explaining 62% of the variance in the model. These findings have both practical and theoretical values to the field and can be used to identify early adopters of this advanced medical technology.

A qualitative analysis of social and emotional perspectives of airline passengers during the COVID-19 pandemic.

Background: Fear of illness, economic damage, and stigma have had a devastating impact on the travel industry and have caused a significant reduction in both business and leisure travel. This study examines passengers' social and emotional perspectives during and after the COVID-19 pandemic, building on a prior quantitative study that identified factors that predict a person's willingness to fly during the COVID-19 pandemic. Methods: This study used a qualitative method with a phenomenological perspective and hermeneutic design. Fifteen adults from the United States participated in a personal interview designed to capture demographics, individual safety measures, feelings, and concerns involving air travel during the pandemic. Personal interview transcripts were then inspected by the researchers using a constant comparison method. Results: The personal experiences of participants were dominated by projections of trust issues and emotional heuristics, protective behaviors, and fear of confrontations with others, and a fear of the unknown. These themes emerged even in participants who continued to fly during the pandemic. Conclusion: Insights into travelers' emotions, trust, and fears may help airlines and other segments of the travel industry to develop targeted messaging that supports the trust and safety issues confronted by frequent travelers.

Factors that predict passengers willingness to fly during and after the COVID-19 pandemic.

BACKGROUND: Prior research has examined consumer willingness to fly in a variety of situations, including during disease outbreaks. However, to date, no study that we know of has identified what type of person is willing to fly during the COVID-19 pandemic. METHODS: Six hundred and thirty-two participants from the United States were asked to complete a survey designed to capture demographics, personality measures, emotional states and travel purposes. The data were collected in two stages in order to both develop a descriptive regression equation and a predictive model. RESULTS: Regression equations were created for both business and pleasure travel, and the following predictors were significant for both scenarios: perceived threat from COVID-19, agreeableness, affect, and fear. These models accounted for 66-67% of the variance in willingness to fly. CONCLUSION: Airlines and governments could use these findings to help control the message to potential passengers on actions being taken to provide a safe flying experience, such as mask wearing policies and aircraft disinfectant procedures.

Autopilots in the Operating Room: Safe Use of Automated Medical Technology.

Automated medical technology is becoming an integral part of routine anesthetic practice. Automated technologies can improve patient safety, but may create new workflows with potentially surprising adverse consequences and cognitive errors that must be addressed before these technologies are adopted into clinical practice. Industries such as aviation and nuclear power have developed techniques to mitigate the unintended consequences of automation, including automation bias, skill loss, and system failures. In order to maximize the benefits of automated technology, clinicians should receive training in human-system interaction including topics such as vigilance, management of system failures, and maintaining manual skills. Medical device manufacturers now evaluate usability of equipment using the principles of human performance and should be encouraged to develop comprehensive training materials that describe possible system failures. Additional research in human-system interaction can improve the ways in which automated medical devices communicate with clinicians. These steps will ensure that medical practitioners can effectively use these new devices while being ready to assume manual control when necessary and prepare us for a future that includes automated health care.

Patient Perceptions of New Robotic Technologies in Clinical Restorative Dentistry.

Patient perception research has failed to focus on burgeoning technology within the dental field. Specifically, researchers have yet to focus on developing robotic technologies which are being utilized by dentists to help perform certain procedures with added precision and route mapping. The current study attempts to fill the gap created by a lack of intersection between dental technology research, consumer perceptions research, and automation research. The current research takes a two-study approach, with study 1 investigating if gender or price of the procedure affects willingness to undergo a robotic dental procedure. Study 2 further investigates this phenomenon, identifying ten separate types of dental procedures (ranging from minimally invasive to extremely invasive) to further understand consumer perceptions. Results indicate that females are less willing to undergo a robotic dental procedure, and that participants in general are more willing to undergo a procedure if it is half price than if it full price. Willingness is influenced by gender and the type of procedure being performed - participants were less willing to undergo invasive procedures such as gum surgery than noninvasive procedures such as teeth whitening. These results, and their theoretical and practical significance are discussed.