ABSTRACT
Cryptocurrency is an attempt to create an alternative to centralized financial systems using blockchain technology. However, our understanding of the psychological mechanisms that drive cryptocurrency adoption is limited. This study examines the role of basic human values in three stages of cryptocurrency adoption-awareness, intention to buy, and ownership-using the Theory of Planned Behavior (TPB). Logistic regression analysis was conducted on a quota sample of 714 German adults, and the results showed that openness-to-change values increased the likelihood of cryptocurrency awareness, while self-enhancement values increased the likelihood of intention to buy and ownership. These findings were consistent even after controlling for demographic characteristics, attitudinal beliefs, and perceived behavioral control, which are important factors in the TPB. The results suggest that basic human values may influence an individual's decision to adopt cryptocurrency, but the transition from awareness to ownership may be influenced by socio-economic opportunities available to interested individuals.
ABSTRACT
Introduction: While economics often interprets individual intertemporal choice preferences through the rationality assumption of utility maximization, the reality is that as emotional beings, individuals' preferences for intertemporal behavior are much more diverse and inconsistent. Prior research has predominantly focused on positive or negative emotions based on prospect theory, such as anxiety, anger, disgust, and depression. However, there has been relatively little research on how sadness affects individuals' preferences for immediate and future rewards. Methods: In this study, 170 college students are recruited as participants, and their emotions are primed with a video before engaging in an intertemporal task. Covariance analysis and logit regression model are established to examine the main and interactive effects of sadness on individuals' immediate reward preferences. Results: The findings reveal that sadness led individuals to prefer smaller immediate rewards, demonstrating a more myopic behavioral pattern, but didn't affect time discount rate. As the reward baseline increases, sadness's impact on immediate reward preferences is more pronounced, exacerbating individuals' myopic behavior. Discussion: In conclusion, these findings underscore the importance of considering emotional states in economic decision-making models and suggest avenues for future research to explore the complex dynamics of emotions and intertemporal choices.
ABSTRACT
The specificity of course of acute period of craniocerebral injury and organization of medical care support are the factors determining outcomes for this category of patients. The purpose of the study is to investigate changes in predictors of course of acute period course of craniocerebral injury under implementation of stream model of medical care organization. The sampling included 150 patients with moderate and severe craniocerebral injury based on data obtained in 2013 and 2019, respectively. The clinical characteristics of patients (gender, age, level of consciousness, alcoholic intoxication, type of injury) and the characteristics of organization of medical care (mode of admission, qualification of hospital physician, time prior to surgery intervention) were evaluated. The selection of predictors significant for prognosis of outcomes of acute period of craniocerebral injury was implemented on the basis of logit-regression analysis and artificial neural network technology. The sampling of patients was divided into groups on the basis of outcomes of acute period of craniocerebral injury. The groups with relatively favorable and unfavorable course of acute period of craniocerebral injury were identified. It is demonstrated that prior to implementation of stream model of medical care provision, the most significant factors determining outcomes of the acute period of craniocerebral injury were characteristics of organization of medical care of these patients. After implementation of stream model the leading predictors became clinical characteristics of patients.