Analysis of Design Artifacts in Platform-Based Markets
thesisposted on 31.07.2020, 20:24 by Vandith Pamuru Subramanya Rama
Digitization has led to emergence of many platforms-based markets. In this dissertation I focus on three different design problems in these markets. The first essay relates to augmented-reality platforms. Pok\'emon Go, an augmented-reality technology-based game, garnered tremendous public interest upon release with an average of 20 million active daily users. The game combines geo-spatial elements with gamification practices to incentivize user movement in the physical world. This work examines the potential externalities that such incentives may have on associated businesses. Particularly, we study the impact of Pok\'emon Go on local restaurants using online reviews as a proxy for consumer engagement and perception. We treat the release of Pok\'emon Go as a natural experiment and study the post-release impact on the associated restaurants. We find that restaurants located near an in-game artifact do indeed observe a higher level of consumer engagement and a more positive consumer perception as compared with those that have no in-game artifacts nearby. In addition, we find that the heterogeneous characteristics of the restaurants moderate the effect significantly. To the best of our knowledge, this study is the first to examine the economic implications of augmented-reality applications. Thereby, our research lays the foundations for how augmented-reality games affect consumer economic behavior. This work also builds insights into the potential value of such associations for business owners and policymakers.
The second essay focuses on the platform design problem in sponsored seaerch ad-market.Recent advances in technology have reduced frictions in various markets. In this research, we specifically investigate the role of frictions in determining the efficiency and bidding behavior in a generalized second price auction (GSP) -- the most preferred mechanism for sponsored search advertisements. First, we simulate computational agents in the GSP setting and obtain predictions for the metrics of interest. Second, we test these predictions by conducting a human-subject experiment. We find that, contrary to the theoretical prediction, the lower-valued advertisers (who do not win the auction) substantially overbid. Moreover, we find that the presence of market frictions moderates this phenomenon and results in higher allocative efficiency. These results have implications for policymakers and auction platform managers in designing incentives for more efficient auctions.
The third essay is about user-generated content platforms. These platform utilize various gamification strategies to incentivize user contributions. One of the most popular strategy is to provide platform sponsorships like a special status. Previous literature has extensively studied the impact of having these sponsorships user contributions. We specifically focus on the impact of losing such elite status. Once their contributions to the platform reduce in volume, elite users lose status. Using a unique empirical strategy we show that users continue to contribute high quality reviews, even though they lose their status. We utilize NLP to extract various review characteristics including sentiment and topics. Using an empirical strategy, we find that losing status does not modify the topic of the reviews written by the users, on average.