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- 저자 : Qiuju Yin (검색결과 2 건)
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The Impact of Public Health Emergency Events on Health Behaviors : Evidence from the COVID-19
Chenxi Guo,Qiuju Yin,Wanxin Qiao 한국경영정보학회 2021 한국경영정보학회 학술대회논문집 Vol.2021 No.11
The emergence of public health events has a devastating impact on the health of people. However, it is still unclear whether the outbreak of public health emergency event would influence the people’s health behaviors in online health communities. In this study, we collect the data of users’ physical activities and content-generation activities from a large online health community in China from January 2019 to April 2021, in which period COVID-19 had outbroken in China. Through adopting an interrupted time-series strategy, we find that users expend greater effort on improving the physical activity level as compared with the time before the COVID-19. The burst of COVID-19 makes people pay more attention to their health and improve their health awareness. Furthermore, we observe that after the COVID-19, users are more likely to share contents on the community. Our results have implications for public health and online community management.
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Seoyoun Lee,Younghoon Chang,Haejung Yun,Qiuju Yin 한국경영정보학회 2023 한국경영정보학회 학술대회논문집 Vol.2023 No.11
This study aims to investigate generative AI (GAI) services, which are leading innovations in various domains and significantly affecting education with a range of positive and negative effects. Specifically, this study seeks to empirically examine the academic performance of graduate students who regularly use generative AI services such as ChatGPT (Generative Pre-trained Transformer). To establish the research framework and hypotheses, this study incorporates concepts from social cognitive theory and the concept of dependency. Also, this study introduces graduate students' personal and socio-environmental factors alongside the dependency variables of habitual and addictive behavior of GAI and their impact on learning and task performance. This study uses a structural methodology in order to test the hypotheses and a mixed approach that includes Artificial Neural Networks (ANN) to predict the most critical variables. We plan to collect data from 500 graduate students who actively utilize generative AI services to achieve this. Based on the outcomes of this research, this study tries to offer academic and practical implications for educational institutions and administration.
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