Information technology and social media have been a driving force in the economy and have transformed all aspects of business in recent decades. Understanding social networks is necessary to evaluate their impacts and examine key business issues involving information and technological innovations. The dissertation contains three chapters exploring those issues. In the first chapter, I propose an optimal procurement mechanism for mobile data offloading, covering both technological and business aspects. The unprecedented growth of cellular traffic driven by web surfing, video streaming, and cloud-based services is creating challenges for cellular service providers to fulfill the unmet demand. My present work contributes to the existing literature by developing an analytical model, which considers the unique challenge of integrating the longer range cellular resource and shorter range WiFi hotspots. In the second chapter, I examine the effect of a social network on prediction markets using a controlled laboratory experiment. In prediction markets, people place bets on events that they think are most likely to happen, thus revealing in a sense the nature of their private information. Through a randomized experiment, I show that when the cost of information acquisition is low, a social-network-embedded prediction market outperforms a non-networked prediction market. The third chapter studies different forms of social learning in the context of location-based networks: observational learning and the saliency effect. In recent years, the location-sensing mobile devices offer geographic location capabilities to share users' information about their locations with their friends. In our context, observational learning corresponds to the fact that "check-ins" made by friends help users learn the quality information of a venue; the saliency effect refers to that check-ins lead some of the uninformed consumers to discover a new venue. / text