【主講】姜正瑞，南京大學商學院營銷與電子商務系教授

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

【時間】2021/12/15周三 10:00-12:00

【地點】騰訊會議：221-686-697  https://meeting.tencent.com/dm/AMlQmZV7lYDw

【語言】中文

【主辦】管理科學與工程系

【簡曆】姜正瑞，南京大學商學院營銷與電子商務系教授，二級教授，博士生導師。在2019年加入南大之前，任美國愛荷華州立大學商學院信息系統與商業分析教授和托米講席教授。主要研究領域是商務智能與大數據分析，研究特色是将管理學研究與計算機科學研究有效融合，在商業數據分析、機器學習、決策支持和科技創新擴散等方向做出了重要貢獻，大多數研究成果發表在國際頂級期刊上（如Management Science, MIS Quarterly, Information Systems Research, IEEE Transactions on Knowledge and Data Engineering）。現為信息系統領域國際頂級期刊Information Systems Research的副主編，曾任另一頂刊MIS Quarterly的副主編，并分别于2016和2021年獲得這兩本刊物的年度最佳副主編獎。另外現在還擔任運營管理領域頂刊Production and Operations Management的高級編輯。主持過信息系統領域多個國際和地區性的學術會議。作為項目主持人曾收到國家自然科學基金和其他機構的資助，在北美、中國和非洲從事過科研和知識傳播的工作。2019年被南京市委市政府授予“南京市高層次舉薦人才（A類）”的榮譽稱号，2021年獲得江蘇省“雙創”傑出人才稱号。

【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.