The Influence of IT Architectures on Profit Concentration in Digital Platforms
Dr Qiao Dandan, Associate Professor, School of Computing
Abstract:
This thesis investigates the impact of information technology architectures on profit concentration in digital platforms, focusing on two critical contexts: online illegal commercial markets and decentralized autonomous organizations (DAOs). While digital technologies aim to democratize access and distribute profits equitably, evidence suggests they can inadvertently exacerbate market concentration. By exploring the mechanisms underlying these phenomena, my thesis provides theoretical and empirical insights into the design of fairer and more sustainable IT-driven systems.
The first study examines the role of search intermediaries in online illegal transaction markets, such as Darknet, where the introduction of a cross-website search engine significantly reduced information retrieval costs. Using a Difference-in-Differences approach, the study reveals that reduced search costs disproportionately benefit dominant vendors, intensifying market concentration. This is primarily attributed to trust considerations, as customers in these high-risk markets prioritize vendor reliability over pricing. Mechanism analyses confirm that top vendors leverage their trustworthiness and ability to scale up operations, further consolidating their market dominance. These findings challenge traditional notions that reduced search costs lead to more distributed market structures especially in a multi-vendor context, emphasizing the importance of trust in shaping concentration outcomes in unique market environments.
The second study shifts focus to DAOs, exploring the dynamic effects of token-based incentives on users’ coalition behaviors. Unlike traditional social media platforms, where reputation is platform-controlled, DAOs rely on community consensus, which can lead to power concentration if token incentives encourage coalition formation. Using data from Steemit, a blockchain-based social media platform, my second study employs a Hidden Markov Model to capture the dynamic coalition intentions of users. Results show that token incentives have dual effects: they suppress coalition motivations among low-motivation users but amplify these behaviors among highly motivated individuals. Furthermore, role-specific incentives, such as those for content creators or curators, exhibit varied impacts, with curator incentives encouraging coalition across all motivational states. These insights underscore the complexity of designing token-based incentive systems that balance community engagement with more reasonable power distribution.
Together, my thesis contributes to the literature on technology-driven market concentration by offering nuanced, context-specific analyses. They provide actionable recommendations for designing IT architectures and incentive mechanisms that promote fairness and mitigate the risks of profit concentration, thus advancing the broader goals of digital platform governance and sustainability.