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.