To Help without Expectation: Investigating Social Exchanges on a Mobile Crowdsourcing Platform for a Smart City

Drawing from the social exchange theory, this study investigates social exchanges on a mobile crowdsourcing platform (MCP) for a smart city. Specifically, we investigate how users' perceptions of features on a MCP influence their helping behaviors. Preliminary analysis shows that there are three main categories of features (i.e. information, positive emotions and ease of use) and two types of social exchange structures (i.e. reciprocal and productive). Results from regression analyses indicate that positive emotions and ease of use have effects on reciprocal exchange structure while information features have effects on productive exchange structure.