P2P lending and Natural Disasters: Is Altruistic Behavior conditional?

In the aftermath of natural disasters, borrowers seeking credit from traditional sources, such as banks, may encounter higher interest rates than in pre-disaster periods. We examine the lending behavior of peer-to-peer (P2P) platforms in the wake of disasters. Since P2P platforms involve individual investors in making lending decisions, they accommodate both profit motives and empathetic responses. Empirical evidence from psychology suggests that empathy can lead to prosocial behavior. Consistent with this assertion, we find that loans affected by natural disasters have lower interest rates than comparable loans not issued in the wake of disasters. Our study finds that lenders are involved in a prosocial behavior and are ready to give away some part of their interest income to help the communities in need

Factors Affecting the Extent of Patients’ Electronic Medical Record Use: An Empirical Study Focusing on System and Patient Characteristics

BackgroundPatients’ access to and use of electronic medical records (EMRs) places greater information in their hands, which helps them better comanage their health, leading to better clinical outcomes. Despite numerous benefits that promote health and well-being, patients’ acceptance and use of EMRs remains low. We study the impact of predictors that affect the use of EMR by patients to understand better the underlying causal factors for the lower use of EMR. ObjectiveThis study aims to examine the critical system (eg, performance expectancy and effort expectancy) and patient characteristics (eg, health condition, issue involvement, preventive health behaviors, and caregiving status) that influence the extent of patients’ EMR use. MethodsWe used secondary data collected by Health Information National Trends Survey 5 cycle 3 and performed survey data analysis using structural equation modeling technique to test our hypotheses. Structural equation modeling is a technique commonly used to measure and analyze the relationships of observed and latent variables. We also addressed common method bias to understand if there was any systematic effect on the observed correlation between the measures for the predictor and predicted variables. ResultsThe statistically significant drivers of the extent of EMR use were performance expectancy (β=.253; P<.001), perceived behavior control (β=.236; P<.001), health knowledge (β=–.071; P=.007), caregiving status (β=.059; P=.013), issue involvement (β=.356; P<.001), chronic conditions (β=.071; P=.016), and preventive health behavior (β=.076; P=.005). The model accounted for 32.9% of the variance in the extent of EMR use. ConclusionsThe study found that health characteristics, such as chronic conditions and patient disposition (eg, preventive health behavior and issue involvement), directly affect the extent of EMR use. The study also revealed that issue involvement mediates the impact of preventive health behaviors and the presence of chronic conditions on the extent of patients’ EMR use

Design Science Approach to developing using Chatbot for Sexually Transmitted Diseases

Sexually transmitted diseases remain a significant public health issue with substantial cost and societal implications. Despite these grave concerns, individuals suffering from these disorders do not usually seek care early due to the STDs\u27 taboo. In the article, we try to address such individuals\u27 care needs by developing a chatbot application using a design science research approach. The application offers a much richer experience to the users by leveraging decision tree algorithms to develop context sensitivity using the attributes of the individuals seeking information and the nature of the information sought. Further, using Google’s dialog flow and custom-built web interfaces, we build and integrate our chatbot. Subsequently, we evaluated the chatbot using simulation techniques. We explore the potential of this chatbot in providing a context-specific dialogue with patients. As future work, we intend to develop direct conversational agents with more robust conversational coherence capabilities and interconnect conversational flow with the user while maintaining solid contextual grounds