Study on incentive mechanisms of smes crowdsourcing contest innovation.

Abstract

Dealing with insufficient resources is a common challenge yet practical reality for many project managers working within SMEs. With the rise of Web 2.0, crowdsourcing contest innovation (CCI) it is now possible for project managers to use online platforms as a way to collaborate with external agents to fill this resource gap and thus improve innovation. This research uses agent-based modelling to prognosticate the efficacy of crowdsourcing contest innovation with a particular focus on the project manager ‘seeker’ within an SME initiating competitive crowdsourced contest teams made up of individual ‘solver’ participants. The contribution of knowledge will benefit the open innovation community to better understand the main motivational incentives to obtain maximum productivity of a team with limited project management resources. In pursuit of this, the social exchange theory is challenged, this thesis explores the motivation factors that influence solvers to participate in SMEs CCI from the perspectives of benefit perception and cost perception. The results found that non-material factors such as knowledge acquisition and sharing, reputation can stimulate solvers to participate in SMEs CCI more than material (physical money) rewards. Meanwhile, risks such as intellectual property risks and waste of resources are significant participation obstacles. Based on this, the principal- agent theory is used to design the models of team collaboration material incentive mechanism, dynamic reputation incentive mechanism and knowledge sharing incentive mechanism, and the performance of each incentive mechanism is analysed. At last, according to the principles of sample selection, Zbj.com, the China’s most successful crowdsourcing platform of which the main clients are SMEs, is chosen as the research object, and the effectiveness of the incentive mechanisms designed in this thesis is verified. It is found that the material and non-material incentives have been partially applied on the platform, and the explicit, implicit and synergistic effects of incentives are preliminarily achieved. According to the research results, it is suggested that the guarantee measures of the incentive mechanisms should be further developed, such as optimising pricing services and refining task allocation rules.