【主讲】姜正瑞，南京大学商公司营销与电子商务系教授

【主题】Social Influence in the Concurrent Diffusion of Information and Behaviors in Online Social Networks

【时间】2021/12/15周三 10:00-12:00

【地点】腾讯会议：221-686-697  https://meeting.tencent.com/dm/AMlQmZV7lYDw

【语言】中文

【主办】管理科学与工程系

【简历】姜正瑞，南京大学商公司营销与电子商务系教授，二级教授，博士生导师。在2019年加入南大之前，任美国爱荷华州立大学商公司信息系统与商业分析教授和托米讲席教授。主要研究领域是商务智能与大数据分析，研究特色是将管理学研究与计算机科学研究有效融合，在商业数据分析、机器学习、决策支持和科技创新扩散等方向做出了重要贡献，大多数研究成果发表在国际顶级期刊上（如Management Science, MIS Quarterly, Information Systems Research, IEEE Transactions on Knowledge and Data Engineering）。现为信息系统领域国际顶级期刊Information Systems Research的副主编，曾任另一顶刊MIS Quarterly的副主编，并分别于2016和2021年获得这两本刊物的年度最佳副主编奖。另外现在还担任运营管理领域顶刊Production and Operations Management的高级编辑。主持过信息系统领域多个国际和地区性的学术会议。作为项目主持人曾收到国家自然科学基金和其他机构的资助，在北美、中国和非洲从事过科研和知识传播的工作。2019年被南京市委市政府授予“南京市高层次举荐人才（A类）”的荣誉称号，2021年获得江苏省“双创”杰出人才称号。

【摘要】The emergence of online social networks has greatly facilitated the diffusions of information and behaviors. While the two diffusion processes are often intertwined, “talking the talk” does not necessarily mean “walking the talk”—those who share information about an action may not actually take part in the action. This study aims to understand whether the diffusion of information and behaviors are similar, and whether social influence plays an equally important role in these processes. Integrating text mining, social network analyses, and survival analysis, this research examines the concurrent spread of information and behaviors related to the Ice Bucket Challenge event on Twitter. We show that the two processes follow different patterns. Unilateral social influence contributes to the diffusion of information, but not to the diffusion of behaviors; bilateral influence conveyed via the communication process is a significant and positive predictor of both diffusion of behaviors and information. Based on the Bass diffusion theory, we find that the influence from bilateral social connections is a more significant predictor than the influence from unilateral social connections. In addition, when jointly modeling the two adoption behaviors, the prediction accuracy of behavior adoptions is significantly improved. These results have important implications for applying theories of social influence, social networks, and contagion to better understand individuals’ behaviors in passing information and taking actions in a social context.