A highly successful strategy to thrive innovation in private and public organizations is the integration of consumers and external stakeholders into the innovation process. Eighty five percent of the 100 most valued brands already use digital platforms such as OpenIdea, Crowdspring, HyveCrowd or propretiary platforms such as LEGO ideas.
Although these digital platforms are widely used, our analogous human behavior still hinders them from unleashing their full innovation potential. In this project we focus on three vexing behavioral barriers to human creativity endemic to digital platforms that have been long established by prior research. First, we tend to suffer from cognitive fixation, having difficulty detaching from previously seen common concepts and ideas. Second, once we have generated ideas, we are constrained by our own ideas, particularly if others tell us that they are sufficient. Third, we tend to suffer from idea selection bias, rejecting highly original ideas because we do not believe they are feasible. These three barriers significantly reduce the creativity and the number of ideas we can generate on digital innovation platforms.
For instance, using transformer or generative adversarial neural networks, AI approaches can better explore solution spaces without cognitive limitations, offering inspirational input to humans such that they may overcome cognitive fixation. The technological advancements in the domain of AI are promising in improving the performance of digital innovation platforms, yet their application in human creativity remains underresearched.
The goal of this project is to complement behavioral science research on human creativity with recent developments in AI with the goal of overcoming the three aforementioned barriers to human creativity. We investigate a “hybrid creativity” approach, in which humans and machines interact in novel ways that can improve joint creative performance. Therefore, our main research question states “How can we best use AI to improve human creativity?”. We will conduct three related sub-projects guided by this research question, all aiming at publication in top-tier scientific journals in the fields of marketing and management.
Conceptually, we draw an behavioral theories related to the three barriers such as SIAM-theory, geneplore model of creativity, self-efficacy theory, and distraction-conflict theory. Based on these theories we argue that AI approaches can both help and harm individual creative performance on digital innovation platforms. Understanding the mechanisms of how AI can help us will allow us to design meaningful interventions that aim at improving human creative performance on platforms. The deliverables of this research project are novel mechanisms and actionable interventions that should be considered when AI is intended to increase individual creative performance and publicly available exemplary AI tools that are implementable and testable on innovation platforms.