For more information Social Media Influencer Content Strategy in the Digital Era
Abstract: As content creators on social media platforms, influencers dynamically generate and deliver content to audiences. To incentivize audience engagement and enhance their influence, influencers must continuously optimize their content strategies in response to dynamic interactions with audiences. However, existing literature has paid limited attention to modeling how influencer content strategies affect audience behavior while explicitly accounting for such dynamic and interactive features. At the platform level, maximizing the effectiveness of marketing campaigns requires not only selecting the most suitable influencers but also optimally scheduling their promotional content under budget constraints. Quantifying and modeling the joint problem of influencer selection and content scheduling, while incorporating heterogeneous influencer characteristics, remains both theoretically and empirically challenging. To address these gaps, this study examines influencers’ optimal content strategies for maximizing audience influence and develops an integrated framework for influencer selection and content scheduling. Combining machine learning methods, empirical modeling, and operations optimization, this research uncovers how different content strategies shape audience responses and campaign outcomes. The findings extend the literature on the modeling and optimization of influencer content strategies and offer actionable insights for both influencers and platforms regarding content generation and monetization.
Bio of the Presenter: Xingyu Chen is the Associate Dean and Distinguished Professor at the School of Management, Shenzhen University. She holds a Ph.D. from Nanyang Technological University in Singapore and was a visiting scholar at the University of Michigan, Ann Arbor. Her work has been published in top-tier journals, including UTD24 journals such as the Journal of Marketing Research, Marketing Science, and Production and Operations Management; the FT50 journals including Journal of the Academy of Marketing Science; ABS4* journals including Journal of the Association for Information Systems; ABS4 journals including International Journal of Research in Marketing; authoritative journals like Information & Management and Decision Support Systems; and the leading communication journal New Media & Society. Additionally, three of her English case studies are included in the Ivey Business School case library, and five of her Chinese enterprise cases have been recognized as National Top 100 Excellent Management Cases (2019, 2020, 2022, 2023, and 2024). She also received the 11th Shenzhen Philosophy and Social Sciences Outstanding Achievement Award (2023).
Professor Chen has been recognized with several prestigious honors, including being selected for the Ministry of Education's Chang Jiang Young Scholars Program, the Shenzhen University Liyuan Outstanding Young Scholar award, the Shenzhen High-Level Overseas Talent "Peacock Plan," the Shenzhen High-Level Professional Talent (Reserve Level), and the Nanshan District "Leading Talent" in Shenzhen. She has led two National Natural Science Foundation of China (NSFC) General Projects (one ongoing, one completed with an excellent rating) and one NSFC Young Scientist Project (completed with an excellent rating). Her research focuses on social media marketing models and big data marketing.
Professor Chen serves as an Associate Editor for the SSCI Q1 interdisciplinary journal Industrial Management & Data Systems and as an editorial board member for the Journal of Business Research. She also acts as a reviewer for renowned international journals such as Management Science, Information & Management, and Internet Research.
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