Diener, K.; Piller, F.; Pollok, P.
Intermediaries are an inherent part of value creation in open innovation, especially crowdsourcing. They connect organizations seeking external input or solutions for an innovation-related problem (seekers) with potential solution providers (solvers). To bridge between the innovation problem and external knowledge sources, intermediaries deploy different search strategies and offer complementary services. Learn more.
Boussioux, L; Lane, J. N.; Zhang, M.; Jacimovic, V.; Lakhani, K. R.
This study investigates the capability of generative artificial intelligence (AI) in creating innovative business solutions compared to human crowdsourcing methods. We initiated a crowdsourcing challenge focused on sustainable, circular economy business opportunities. The challenge attracted a diverse range of solvers from a myriad of countries and industries. Simultaneously, we employed GPT-4 to generate AI solutions using three different prompt levels, each calibrated to simulate distinct human crowd and expert personas. Learn more.
Fayard, A. L.
With the growing complexity of social and environmental issues, there has been a blossoming of hackathons and open innovation challenges. This push to accelerate innovation embraces a perspective of time as clock time—conceived as objective, linear, measurable, and therefore, rather easy to compress. Such a view of time conflicts with the emergent nature of idea generation and the indeterminate process that leads to social impact, which both rely on event time.
Preißner, S; Raasch, C.; Schweisfurth, T.
This study investigates the sources of disruptive innovation. The disruptive innovation literature suggests that these do not originate from existing customers, in contrast to what is predicted by the user innovation literature. We compile a unique content-analytical dataset based on 60 innovations identified as disruptive by the disruptive innovation literature. Using multinomial and binomial regression, we find that 43% of the sample disruptive innovations were originally developed by users. Learn more.
Li, X.; Zheng, H.; Chen, J.; Zong, Y.; Yu, L.
At a time when artificial intelligence is widely used in all walks of life, the way users interact with the digital world also needs to incorporate intelligent elements to reduce the cost of connectivity. This cost can be quantified through "experience metrics", which reveal the problems users encounter when using the interface (UI), and make targeted optimization. With AI, deep learning and prediction of user behavior can be achieved to anticipate and address potential barriers to use in UI design. Learn more.
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