Recent years have witnessed an unprecedented explosion in information technology that enables dynamic diffusion of user-generated content in social networks. Online videos, in particular, have changed the landscape of marketing and entertainment, competing with premium content and spurring business innovations. In the present study, we examine how learning and network effects drive the diffusion of online videos. While learning happens through informational externalities, network effects are direct payoff externalities. Using a unique data set from YouTube, we empirically identify learning and network effects separately, and find that both mechanisms have statistically and economically significant effects on video views; furthermore, the mechanism that dominates depends on the video type. Specifically, although learning primarily drives the popularity of quality-oriented content, network effects also make it possible for attention-grabbing content to go viral. Theoretically, we show that, unlike the diffusion of movies, it is the combination of both learning and network effects that generate the multiplier effect for the diffusion of online videos. From a managerial perspective, providers can adopt different strategies to promote their videos accordingly, that is, signaling the quality or featuring the viewer base depending on the video type. Our results also suggest that YouTube can play a much greater role in encouraging the creation of original content by leveraging the multiplier effect. > >
This paper examines the effect of a social network on prediction markets using a controlled laboratory experiment that allows us to identify causal relationships between a social network and the performance of an individual participant, as well as the performance of the prediction market as a whole. Through a randomized experiment, we first confirm the theoretical predictions that participants with more social connections are less likely to invest in information acquisition from outside information sources, but perform significantly better than other participants in prediction markets. We further show that when the cost of information acquisition is low, a social network-embedded prediction market outperforms a nonnetworked prediction market. We find strong support for peer effects in prediction accuracy among participants. These results have direct managerial implications for the business practice of prediction markets and are critical to understanding how to use social networks to improve the performance of prediction markets.
This paper examines the effects of social network structures on prediction market accuracy in the presence of insider information through a randomized laboratory experiment. In the experiment, insider information is operationalized as signals on the state of nature with high precision. Motivated by the literature on insider information in the context of financial markets, we test and confirm two characterizations of insider information in the context of prediction markets: abnormal performance and less diffusion. Experimental results suggest that a more balanced social network structure is crucial to the success of prediction markets, whereas network structures akin to star networks are ill suited to prediction markets. As compared with other network structures, insider information has less positive effects on prediction market accuracy in star networks. We also find that the bias of the public information has a larger negative effect on prediction market accuracy in star networks.