This dissertation presents three essays that focus on various aspects pertaining to the management of online platforms, defined as "digital services that facilitate interactions between two or more distinct, but interdependent sets of users (whether firms or individuals) who interact through the service via the Internet" (OECD, 2019). The interactions benefit both the users and the platform. Managing online platforms involves developing strategies for one or more of three value adding functions: (a) lowering search costs for the parties connecting through the platform, (b) providing a technology infrastructure that facilitates transactions at scale by sharing both demand and supply side costs; and (c) locating other audiences or consumers for the output that results from the transaction. The platform manager must manage these value adding functions. Thus, one important management task is to recognize potential asymmetries in the economic and/or psychological motivations of the transacting parties connected through the platform. In this dissertation, I empirically examine these issues in greater detail. The first essay, "Incentivizing User-Generated Content—A Double-Edged Sword: Evidence from Field Data and a Controlled Experiment," addresses the conundrum faced by online platform managers interested in crowdsourcing user-generated content (UGC) in prosocial contexts. The dilemma stems from the fact that offering monetary incentives to stimulate UGC contributions also has a damping effect on peer approval, which is an important source of non-monetary recognition valued by UGC contributors in prosocial contexts. The second essay, "Matching and Making in Matchmaking Platforms: A Structural Analysis," examines matchmaking platforms, focusing specifically on the problem of misaligned incentives between the platform and the agents. Based on data from the Ultimate Fighting Championship (UFC) on fighter characteristics, and pay-per-view revenues associated with specific bouts, we identify the potential for conflicts of interest and examine strategies that may be used to mitigate such problems. The third essay, "Matching and Making in Matching Markets: A Managerial Decision Calculus," extends the empirical model and analytical work to a class of commonly encountered one-sided matching market problems. It provides the conceptual outline of a decision calculus that allows managers to explore the revenue and profitability implications of adaptive changes to the tier structures and matching algorithms. / Doctor of Philosophy / The 21st century has witnessed the rise of the platform economy. Consumers routinely interact with online platforms ways in their day to day activities. For instance, they interact with platforms such as Quora, StackOverflow, Uber, and Airbnb to name only a few. Such platforms address a variety of needs starting from providing users with answers to a variety of questions to matching them with a range of service providers (e.g., for travel and dining needs). However, the rapid growth of the platform economy has created a knowledge gap for both consumers and platforms. The three essays in this dissertation attempt to contribute to the literature in this area. The first essay, "Incentivizing User-Generated Content—A Double-Edged Sword: Evidence from Field Data and a Controlled Experiment," examines how crowdsourcing contests influence the quantity and quality of user-generated content (UGC). Analyzing data from the popular question and answer website Quora, we find that offering monetary incentives to stimulate UGC contributions increases contributions but also has a simultaneous damping effect on peer endorsement, which is an important source of non-monetary recognition for UGC contributors in prosocial contexts. The second essay, "Matching and Making in Matchmaking Platforms: A Structural Analysis," examines matchmaking platforms, focusing on the problem of misaligned incentives between the platform and the agents. Based on data from the Ultimate Fighting Championship (UFC) on fighter characteristics, and pay-per-view revenues associated with specific bouts, we identify the potential for conflicts of interest and examine strategies that may be used to mitigate such problems. The third essay, "Matching and Making in Matching Markets: A Managerial Decision Calculus," extends the empirical model and analytical work to a class of commonly encountered one-sided matching market problems. It provides the conceptual outline of a decision calculus that allows managers to explore the revenue and profitability implications of adaptive changes to the tier structures and matching algorithms.

To overcome constrained resources, firms can actively seek innovative opportunities from the external world. This innovation approach, called open innovation (Chesbrough 2003; Hippel 2005; Terwiesch and Ulrich 2009; Terwiesch and Xu 2008), is receiving more and more attention. Facilitated by the global Internet and emerging forms of information technology, it has become very easy for companies to generate large numbers of innovative solutions through the use of online open innovation contests or crowdsourcing contests (Archak and Sundararajan 2009; Terwiesch and Ulrich 2009; Terwiesch and Xu 2008; Yang et al. 2009).For an innovation project to succeed, it is necessary to generate not only a large number of good ideas or solutions, but also to identify those that are "exceptional" (Terwiesch and Ulrich 2009). This dissertation contains three studies that aim to improve our understanding of how best to use contests as a tool to aggregate external resources (collective intelligence) in the generation and evaluation of solutions. The first study views an innovation contest from the innovation seeker's perspective and provides insights on how to improve contest performance. The second study views an innovation contest from the innovation solver's perspective examining the characteristics and strategies of winners and solvers. Finally, in the third study, a new approach to the solution evaluation process is introduced, which is referred to as open evaluation. In this approach, a prediction market is used as an aggregation mechanism to coordinate the crowd in the evaluation of proposed solutions. These three studies make a number of contributions to the literature, addressing core issues in the area of online innovation contests. The analyses, which leverage large-scale empirical data, produce a number of profound results, which can help people to understand how best to use and design innovation contests in an online environment, for idea generation. Further, these studies present a variety of managerial implications associated with the aggregation of individual effort (collective intelligence) to evaluate the ideas that are generated by an innovation contest. We hope that our studies can help open innovation pioneers, such as Google, to systematically generate and identify exceptionally good ideas at much lower costs. By utilizing our findings, we expect that more firms will be able to adopt an open innovation strategy, both systematically and easily. / Business Administration/Management Information Systems