報告人：李勝利 北京大學(xué)信息管理系
時間： 2025年6月9日（周一）10:00
地點：主樓317
個人簡介：
李勝利老師現(xiàn)任北京大學(xué)信息管理系長聘副教授。研究方向包括信息系統(tǒng)經(jīng)濟(jì)學(xué)、電子商務(wù)、社交媒體分析。主持或參與國家自然科學(xué)基金項目等多項。論文曾發(fā)表于MISQ、POM、JMIS、JAIS、DSS、I&M等期刊。曾獲北京大學(xué)黃廷方/信和青年杰出學(xué)者獎、2011-2015年中國信息經(jīng)濟(jì)學(xué)會理論貢獻(xiàn)獎、2019年中國信息經(jīng)濟(jì)學(xué)優(yōu)秀成果獎、中國信息經(jīng)濟(jì)學(xué)2024創(chuàng)新成果獎等。任中國信息經(jīng)濟(jì)學(xué)會常務(wù)理事，Information Technology & Management期刊副編輯，Industrial Management & Data Systems期刊編委會成員等。
講座簡介：
The wide adoption of data analytics technologies on video streaming platforms enables delivering advertisements to targeted audiences and significantly improves the efficacy and efficiency of advertising. On the one hand, streaming platforms can precisely capture consumers' interests, purchasing habits, and behavioral patterns. On the other hand, this may also raise consumers’ privacy concerns. In this paper, we build an analytical model to investigate the implications of targeted advertising on streaming platforms, considering three different business models, namely free-content-with-ads business model (FBM), subscription business model (SBM), and dual business model (DBM). We characterize the conditions under which the platform should adopt targeted advertising and the corresponding optimal targeting level. Further, the proliferation of targeted advertising may impact the streaming platform’s decision on business models. This study also provides insights into the optimal business models when targeted advertising is implemented.
