Zihong Huang

Zihong Huang

Assistant Professor @ Rawls, Texas Tech University

Working papers

Social Media Impersonation and Verification Badge

Working paper, presented in HICSS 2025

Due to the increasing prevalence of impersonation on social media, the platforms have actively adopted verification badges to combat such an issue. To explore whether providing verification badges to validate creators’ identities is an appropriate approach, we build a game-theoretic model to examine the impact of providing such badges on key stakeholders’ payoffs. Our study reveals that although offering verification badges seems to be good, it can lead to unintended consequences. For example, we find that the platform may not be better off in profitability by providing badges. Specifically, the platform encourages both creators and impersonators to purchase badges when the verification cost is low while discouraging badge application when the preparation cost is high. Surprisingly, providing verification badges can disadvantage genuine creators by enhancing impersonator credibility, making it harder for consumers to distinguish between real and fake profiles, ultimately benefiting impersonators more. Additionally, consumer experiences may sometimes be worse off when the influencers purchase the badge, as verified yet fraudulent accounts can more effectively deceive users, reducing the overall utility of consuming content.

AI-empowered Venture Capital (VC): The Impact of AI Adoption on VC Firms’ Success

Working paper, presented in WeB 2020, CIST 2021

In the venture capital (VC) industry, a new breed of VC firms has emerged to embrace AI-empowered investment strategy instead of relying on human judgments. Although AI has already demonstrated advantages over humans in some domains, it is unclear whether AI can lead to superior performance in the venture capital industry. This research fills this gap by estimating the causal impact of a VC firm’s adoption of AI-empowered investment strategy on its success using matched portfolios of startups from AI-empowered and non-AI-empowered VC firms. We find that AI adoption tends to increase the success of a VC firm in terms of successful exits (e.g. IPO and acquisition) of startups it invests in. In addition, we also find that AI-empowered investment strategy can reduce racial bias but increase gender and local bias. The increase of gender bias is responsible for the superior performance of AI-empowered investment strategy while the reduction of racial bias and the increase of local bias is not.

Budget Induced Strategic Bidding in Multiunit Online Auctions

Working paper, presented in WITS 2019

In this paper, we investigate the role of the budget constraint as a reason for jump bidding in multi-unit ascending online auctions. We theoretically derive the conditions under which jump bidding outperforms the participatory strategy for bidders at their margin. We find that the budget gap and the bid increment jointly influence the bidding strategy for the budget-constrained bidder at his margin. Based on theoretical analysis, we propose a hybrid bidding strategy. Then we use a Discrete Event Simulation model to validate our proposed hybrid strategy can outperform participatory and jump strategies under certain conditions and to examine how the budget gap and the bid increment influence the bidding strategy. We find that as the ratio of the budget gap over bid increment increases, the optimal bidding strategy at margins changes from a pure Participatory strategy to a hybrid one, and finally, becomes a pure Jump strategy.