| With the growing pervasiveness of social media and Web 2.0, the volume and complexity of information has become increasingly large. The deluge of user-generated content and the heterogeneous nature of humans make it very hard to identify what information consumers use and how exactly they use it to facilitate their decision-making activities in an online market. In my dissertation, I examine such issues from three perspectives: (i) How do consumers search for, evaluate and purchase products based on information acquired from search engines and social media? (ii) How do today's search mechanisms and social media platforms affect consumers' behaviors? (iii) How can I leverage knowledge created within diverse social media channels to improve the product search mechanism and to increase market efficiency? Moreover, how can I translate my research into real-world applications?;I combine methodologies from econometrics, structural modeling, Bayesian modeling and randomized experiments, together with machine learning techniques. My dissertation builds on a unique dataset containing detailed information on approximately one million online sessions from Travelocity.com from November 2008 through January 2009. To verify my empirical findings, I supplement the archival data analysis with surveys and randomized experiments. This interdisciplinary approach allows me to combine the strengths of IS, economics, marketing and computer science, and to analyze the interplay between search and social media from the economic, technical and social perspectives. |
