This study aims to develop a research model to investigate how the structure and mechanisms of innovation crowdsourcing influence knowledge management and innovation performance, based on the perspectives of open innovation theory and the knowledge-based view (KBV) of the firm.
The research model and associated hypotheses were tested using partial least squares structural equation modelling (PLS-SEM), based on a dataset from the Microsoft Power BI community of business intelligence (BI) and analytics tools.
The results show that both organisational and technical mechanisms of the community positively influence the community structure. The community structure has a positive impact on knowledge acquisition, knowledge transformation and the size and diversity of crowd participation. The mechanisms of innovation crowdsourcing and knowledge transformation in turn have a strong influence on innovation performance.
This study is among the first to provide analytical insights into the mechanisms of innovation crowdsourcing and their underlying impact on innovation performance in the context of BI and analytics tools that exhibit a multiplicity and complexity of functions and capabilities. It therefore provides strategic guidance on how to effectively stimulate crowd intelligence and maximise the collaborative and synergistic effectiveness of innovation crowdsourcing communities, focusing on knowledge management practices and user innovation behaviour and performance.
Daradkeh, M. and Atalla, S. (2022): Innovation Crowdsourcing Mechanisms and Innovation Performance: An Empirical Study of a Business Intelligence Community. World Journal of Entrepreneurship, Management and Sustainable Development, Vol. 18, No. 5, pp. 633–651.
