Simulation-based education program on postpartum hemorrhage for nursing students

Purpose This study was conducted to develop a simulation-based postpartum care education program for women with postpartum hemorrhage and to verify the effects of the program on postpartum care. Methods This program was developed according to the ADDIE model of instructional system design, which consists of analysis, design, development, implementation, and evaluation phases. This quasi-experimental study used a non-equivalent control group pre- and post-test design, and data were collected from April 23 to May 4, 2015. To verify the effects of the program, 33 nursing students in the experimental group participated in a simulation program, whereas 31 students in the control group were given a case study. Results The experimental group had statistically significantly higher scores for clinical performance (t=–4.80, p<.001), clinical judgment (t=–4.14, p<.001), and learning satisfaction (t=–10.45, p<.001) than the control group. Conclusion The results of this study indicate that the simulation-based postpartum care education program for women with postpartum hemorrhage was effective for developing students’ competency, implying that a similar program should be integrated into the clinical training component of the maternal nursing curriculum

Can We Trust an AI Agent? Interaction Effects of Its Machine Learning Performance and Digital Character

AI-powered digital characters, AI agents, are expanding their scope of application to various fields. However, research on the key factors influencing consumer attitude is insufficient. This experimental study focuses on machine learning (ML) performance (i.e., the behavioral (intelligence) realism of AI agents), which determines users’ trust. This study further investigates the interaction role of the different forms of digital character (i.e., the form realism of AI agents) in the relationship between ML performance and trust. The findings of this study provide a novel understanding of human-AI interaction, expand academic understanding of AI anthropomorphism, and suggest new research directions for digital humans. The results will also guide business practitioners in developing AI services

Revisiting Executive Pay in Family-Controlled Firms: Family Premium in Large Business Groups

According to the prior literature, family executives of family-controlled firms receive lower compensation than non-family executives. One of the key driving forces behind this is the existence of family members who are not involved in management, but own significant fraction of shares and closely monitor and/or discipline those involved in management. In this paper, we show that this assumption falls apart if family-controlled firm is part of a large business group, where most of the family members take managerial positions but own little equity stakes in member firms. Using 2014 compensation data of 564 executives in 368 family-controlled firms in Korea, we find three key results consistent with our prediction First, family executives are paid more than non-family executives (by 27% more, on average) and this family premium is pronounced in larger business group firms even after controlling for potential selection bias problems. Second, pay to family-executives falls with the influence of outside family members (their aggregate ownership in the firm minus the ownership held by the family executive in the same firm). Third, family premium in large business group firms rises with group size, but falls with family’s cash flow rights. It also rises for group chairs, but falls with the number of board seats the family-executive holds within the group