Understanding the social navigation user experience

Abstract

A social navigation system collects data from its users--its community--about what they are doing, their opinions, and their decisions, aggregates this data, and provides the aggregated data--community data--back to individuals so that they can use it to guide behavior and decisions. In this thesis, I document my investigation of the user experience for social navigation systems that employ activity data. I make three contributions in this thesis. First, I synthesize social navigation systems research with research in social influence, advice-taking, and informational cascades to construct hypotheses about the social navigation user experience. These hypotheses posit that community data from a social navigation system exerts informational influence on users, that users egocentrically discount community data, that herding in social navigation systems can be characterized as informational cascades, and that the size and unanimity of the community data correspond to the strength of the community data's influence. The second contribution of this thesis is an experiment that evaluates the hypotheses about the social navigation user experience; this experiment investigated how a social navigation system can support online charitable giving decisions. The experiment's results support the majority of the hypotheses about the social navigation user experience and provide mixed evidence for the other hypotheses. The implications that arise from the experiment's findings compromise the final contribution of this thesis. These implications concern improving the design of social navigation systems and developing a general framework for evaluating the social influence of social navigation systems.