Strategic Designs for Online Platforms
Abstract
Platforms are now everywhere in our society. Some platforms share real-time information such that people can refer to many aspects, i.e., transportation, weather, news, etc. For example, online learning platforms can play a significant role in accelerating learning through things like providing more real-time feedback loops. Due to the recent innovation in mobile devices as well as faster networks, live streaming platforms become a new trend. Several usages of live streaming platforms are gaming experience sharing such as Twitch, or shopping experience like Amazon Live. My dissertation studies the strategic designs of different online platforms, especially how information affects users’ strategic behaviors and how it creates different market outcomes. In the first essay, we investigated how different information disclosure rules affect drivers’ behaviors and subsequent effects on riders’ welfare on the ride-sharing platforms. We found that there exists a trade-off between drivers’ utility and buyers’ welfare in terms of drivers’ acceptance rate. Further simulation reveals that the more information a driver can have (i.e. less uncertainty), the lower the overall acceptance rate the platform will have as drivers will screen out many orders less profitable. In this study, we not only use structural models to replicate the whole decision process of drivers but also propose several potential information sharing rules such that the platform may find a better balance point between drivers’ utility and riders’ experience. In the second essay, we investigated how to leverage online influencers with niche audiences to generate more sales for e-commerce retailers. Live stream platforms provide a unique environment for influencers to communicate with audiences and provide real-time feedback. The dedicated and engaged groups of followers from online influencers broaden online traffic for retailers. Despite the fast development of this new opportunity, how to get more audience attention and convert such attention (i.e., online traffic, their followers) into actual sales remains an open question for both influencers and retailers. The economic value of influencers for retailers, especially the temporal perspective and portfolio variation of multiple influencers are understudied. We use the data from a leading live stream shopping platform to quantify such value of influencers at a portfolio level. Our results reconfirm the effectiveness of engaging influencers on e-commerce sales and provide direct empirical evidence that such effects are short-lived. Besides, our results give important managerial insights into how to form a suitable combinatory strategy when selecting multiple influencers for a portfolio. Our multi-level model analysis suggests that different portfolios of influencers induce heterogeneous treatment effects on retailers’ sales performance. In the third essay, we tried to understand the dynamics of how influencers form a collaboration with retailers from a matching perspective. We estimate a two-sided matching model in the influencer-retailer network. Many factors may determine a successful match between retailers and influencers such as the match of their categories, and influencer demographic match. We first quantify the value generated from a matching between influencers and retailers and investigate the determinants of a successful match.
Degree
Ph.D.
Advisors
Zheng, Purdue University.
Subject Area
Commerce-Business|Finance|Multimedia Communications|Web Studies
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