Crowdsourcing for Innovation: unpacking motivational, knowledge and relational mechanisms of innovative behavior in crowdsourcing platforms

Abstract

The Internet and the advance of communication technologies have brought unprecedented opportunities for harnessing the creative potential of people all over the world. In an attempt to utilize this potential to explore breakthrough new product ideas and find solutions to challenging innovation problems, companies make extensive use of crowdsourcing practices. The main purpose of this dissertation is to contribute to a greater understanding of the dynamics of crowdsourcing by providing a comprehensive investigation of the behavioral factors that influence innovative behavior and the performance of the crowd. In particular, we examine motivational, knowledge and relational mechanisms of crowd engagement, creativity and knowledge-sharing behavior. We demonstrate that crowd members engage in new product ideation and innovative problem solving for different reasons (i.e., intrinsic, extrinsic, prosocial and learning motivation), and monetary rewards impact creativity in different ways, according to individuals’ prosocial motivation. In addition, we find that a crowd member’s performance in solving innovation problems is a consequence of the interplay between his/her expertise and how broadly and deeply he/she searches for solutions. Finally, we show that fear of opportunism by others – the main relational risk attached to disclosing knowledge in crowdsourcing platforms – is not uniform among crowd members, and trust in the owner of the crowdsourcing platform is central in assuaging such fears.