A Boon or a Bane? An Examination of Social Communication in Social Trading

with Mingwen Yang, Matthias Pelster, and Yong Tan

Major Revision at Information Systems Research

Social trading is an emerging market in the sharing economy, allowing investors to observe the trading history of other investors and to automatically follow their investment strategies through so-called “mirror trading” or “copy trading”. A copy trading service enables novice investors (copiers) to delegate their trading activities and allows experienced investors (leaders) to earn commission fees by sharing their trading knowledge.  In this study, we use a separable temporal exponential random graph model (STERGM) to analyze the link formation and dissolution of a large social trading network. In contrast to traditional social networks, social trading networks are characterized by a rapid dissolution of links, thereby increasing the importance of studying network dissolution. We investigate how social communication, along with financial performance and demographics, affects dynamic network evolution and address the existing dependence among copier-leader links. Our results show that social communication, financial performance, and demographic factors are important determinants of link formation. However, once a link is formed, copiers mainly focus on financial performance and communication but not on demographic factors. Thus, the determinants of link formation and dissolution are asymmetric. Different types of social communication, such as posts and comments, have different implications for link formation and dissolution. Our findings provide important implications for both investors and social trading platforms.

Flow of the Game: A Hidden Markov Model of Player Game-play and Reward Ads Watching Behavior in Online Mobile Games

with Stephanie Lee, and Yong Tan

Major Revision at Information Systems Research

With the rapid development of mobile technology and consumers’ increasing demand for portability, the mobile gaming market has experienced huge growth. To ensure the success of mobile games, game publishers need to keep their players engaged and, at the same time, seek monetization strategies that do not interrupt players’ engagement. Reward ads, a relatively new and increasingly popular in-app advertising monetization model in which players can voluntarily watch an ad in exchange for a reward within the game, can help publishers generate revenue without breaking the flow of the game. This paper builds a Hidden Markov Model to examine how players’ intrinsic motivation factors and reward ads affect mobile game players' dynamic engagement state evolution. Using detailed tapstream data from a mobile game app, we find that player's engagement transition is a complex process, and players in high, medium, or low engagement states respond differently to different factors. First, watching reward ads can help players to transition to a higher engagement level, and the magnitude of the positive effect diminishes as players transition to higher engagement states. Second, there is an inverted-U shape between the fluctuation of the perceived difficulty and the propensity of moving to or staying at a higher engagement level. Third, the perceived difficulty is helpful for players who are in a low or high engagement state, and a feeling of achievement has a positive effect on players in all states. Finally, players in a low engagement level are more likely to watch reward ads compared with players in a medium engagement level, and players are more likely to watch reward ads when games become difficult. The findings enhance the understanding of players' engagement and motivations to play mobile games and provide design guidance for mobile game publishers.

Actions Speak Louder than Words: Imputing Users’ Reputation from Transaction History

with Hossein Ghasemkhani, Yong Tan, and Arvind Tripathi

Under 2nd round review at Production and Operations Management

The choice of market mechanism is a key for success for any online marketplace. In recent years, as P2P lending has seen phenomenal growth, leading P2P lending platforms have used various market mechanisms, and in some cases, even switched from one mechanism to another, chasing higher market share and overall growth. While Prosper.com, a leading P2P lending platform has switched from auction lending model to fixed price lending model, recent studies show that overall social welfare was higher with the auction lending model. However, the success of auction lending model hinges on accuracy of lenders’ assessment of credit risk of the borrowers. Building on extant literature and in support of the auction lending model to increase the social welfare, we design an artifact to dynamically estimate borrowers’ reputation to help the lenders and improve the allocative efficiency in P2P lending markets. We posit that borrowers’ reputation built on transactional data, readily available on P2P lending platforms, represents the collective perception of lenders about the borrowers. We propose a dynamic latent class model of reputation and use the latent instrumental variable approach to deal with endogeneity. We test our artifact using real-world P2P lending data. We show that accounting for reputation improves the explanatory power of the model and provides a way to empirically model the evolution and impact of reputation in online platforms where repeated transactions are performed.

Product Positioning and The Value of Learning from Competitors

with Yingfei Wang, Zhijie Lin, and Yong Tan

Research in Progress

Product variety is an important strategy for professional firms to attract customers and respond to competition. However, individual service providers in sharing economy are usually regarded as non-professional suppliers and may ignore or underestimate the value of paying attention to competitor information. In this study, we model kitchen providers’ dish variety strategies (i.e., low and high variation) in different competition environments (i.e., low and high competition), leveraging a longitudinal data from a peer-to-peer food sharing platform. We incorporate providers’ learning behavior by allowing their choice-making utility to be dynamically updated through the learning process. In addition, we explicitly model kitchen providers’ propensity to pay attention to competitor information and investigate the value of learning from competitors. Thirdly, in the Bayesian signal generating process, we propose a novel heterogeneous network representation approach to measure the competitive market structure between kitchens. Compared to traditionally defined geographic-proximity-based competition, the network embedding one not only provides a more granular measure of kitchen characteristics, but also incorporates both supply-side and demand-side information, which provides more accurate demand forecasts.