To specify the peer effects that affect influencers’ sponsored content strategies, the current research addresses three questions: how influencers respond to peers, what mechanisms drive these effects, and the implications for social media platforms. By using a linear-in-means model and data from a leading Chinese social media platform, the authors address the issues of endogenous peer group formation, correlated unobservables, and simultaneity in decision-making and thereby offer evidence of strong peer effects on the quantity of sponsored content but not its quality. These effects are driven by two mechanisms: a social learning motive, such that following influencers emulate leading influencers, and a competition motive among following influencers within peer groups. No evidence of competition motive among leading influencers or defensive strategies by leading influencers arises. Moreover, peer effects increase influencers’ spending on in-feed advertising services, leading to greater platform revenues, without affecting the pricing of sponsored content. This dynamic may reduce influencers’ profitability, because their rising costs are not offset by higher prices. These findings emphasize the need for balanced strategies that prioritize both platform growth and influencer sustainability. By revealing how peer effects influence competition and revenue generation, this study provides valuable insights for optimizing content volume, quality, and financial outcomes for social media platforms and influencers.

This paper investigates the impact of curation algorithms through the lens of stock market dynamics. By innovatively incorporating the dynamic interactions between social media platforms, investors, and stock markets, we construct the Social-Media-augmented Artificial Stock marKet (SMASK) model under the agent-based computational framework. Our findings reveal that curation algorithms, by promoting polarized and emotionally charged content, exacerbate behavioral biases among retail investors, leading to worsened stock market quality and investor wealth levels. Moreover, through our experiment on the debated topic of algorithmic regulation, we find limiting the intensity of these algorithms may reduce unnecessary trading behaviors, mitigates investor biases, and enhances overall market quality. This study provides new insights into the dual role of curation algorithms in both business ethics and public interest, offering a quantitative approach to understanding their broader social and economic impact.

This study investigates how digital transformation affects public sentiment toward firms on social media platforms in China. Using 2008-2022 data on Chinese listed companies and multivariate regression analysis, this paper identifies that digital transformation boosts positive online comments and sentiment. This relationship is mediated by gains in total factor productivity from digital initiatives. Moreover, concurrent green transformation positively moderates the effect, amplifying the impact of digital moves on online positive sentiment. Heterogeneous results reveal that the digital transformation effect on online positive sentiment is greater for state-owned, high-tech, and large companies. To our knowledge, this is the pioneering study to examine the linkage between corporate digital transformation and online public sentiment. The findings reveal whether, how, and when digital transformation shape more favorable public sentiment and online buzz. Companies can leverage digitalization, productivity improvements, and green development to foster positive perceptions and enhance their online reputation.