Crowd-Based Ecosystems: Platforms, Participation, and Policy
Permanent URI for this collectionhttps://hdl.handle.net/10125/112503
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Item type: Item , Assessing the Impact of Algorithmic Quantity Regulations on Sharing Platforms: Evidence from Airbnb in Paris(2026-01-06) Tripathi, Shagun; Petropoulos, Georgios; Kyriakou, HarrisIn recent years, several automated caps, or algorithmic quantity regulations (AQRs), have been deployed to police supply conditions in sharing economy platforms. AQRs constitute a paradigm shift in platform regulation, as they enable exhaustive, and low-cost enforcement, thus comprehensively influencing interactions both within and outside the focal platform. However, their actual impact is not known, and has not been studied so far. In this work, we employ a series of difference-in-differences analyses to provide causal evidence on the impact of AQR. We find that the quality of platform offerings was negatively affected after the introduction of an algorithmic quantity regulation - marked by 6% decline in ratings. Additionally, we find that the AQR affected certain platform participants disproportionately. Providers without organic and designated trust building signals, i.e., inexperienced hosts and non-superhosts, bore the cost of the AQR, ending up worse off than their counterparts.Item type: Item , Introduction to the Minitrack on Crowd-Based Ecosystems: Platforms, Participation, and Policy(2026-01-06) Hong, Yili; Chen, Pei-Yu; Huang, Nina; Gu, BinItem type: Item , Information Strategy in Platform Competition(2026-01-06) Li, Haohao; He, Weitao; Liu, LuningDigital platforms face increasingly intense competition, making the development of effective information strategies to gain competitive advantage a critical challenge. We develop a novel competition-based information value framework to examine how competitive dynamics influence the effectiveness of different information cues (factual, emotional, and attractiveness-based) in platform environments. Grounded in the Ability-Motivation-Opportunity (AMO) framework, we theorize that in competitive environments, factual cues negatively impact performance, while emotional and attractiveness cues generate positive effects. As competition intensifies, the influence of factual and emotional cues diminishes, while the impact of attractiveness cues strengthens; conversely, in non-competitive environments, factual cues enhance performance, whereas emotional cues exhibit inhibitory effects. Empirical analysis of 1.4 million projects from online crowdfunding platform supports our hypotheses. This framework advances understanding of how platform competition shapes optimal information strategies while providing practical insights for platform managers and participants to optimize managerial decision-making processes.Item type: Item , Gig Worker Social Referrals on an On-Demand Food Delivery Platform(2026-01-06) Wang, Hai; Sun, Hao; Zhang, PeterSocial referral programs are commonly used by online labor platforms to incentivize labor supply by rewarding existing workers for successful referrals. This study investigates the impact of such programs on gig workers' labor supply in online labor platforms using data from an on-demand food delivery platform in Singapore. In particular, we analyze how gig workers' past labor supply and referral behavior influence the generation and value of social referrals. This research offers insights into the mechanisms that drive labor supply dynamics in the gig economy and highlights the effectiveness of social referral programs for shaping worker behavior and enhancing platform performance.