Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.

One of the most critical tasks for startups is to validate their business model. Therefore, entrepreneurs try to collect information such as feedback from other actors to assess the validity of their assumptions and make decisions. However, previous work on decisional guidance for business model validation provides no solution for the highly uncertain and complex context of earlystage startups. The purpose of this paper is, thus, to develop design principles for a Hybrid Intelligence decision support system (HI-DSS) that combines the complementary capabilities of human and machine intelligence. We follow a design science research approach to design a prototype artifact and a set of design principles. Our study provides prescriptive knowledge for HI-DSS and contributes to previous work on decision support for business models, the applications of complementary strengths of humans and machines for making decisions, and support systems for extremely uncertain decision-making problems.

Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting "soft" information (information that cannot be quantified) and making predictions in "unknowable risk" situations of extreme uncertainty. In such situations, the machine does not have representative information for a certain outcome. Thereby, humans are still the "gold standard" for assessing "soft" signals and make use intuition. To predict the success of startups, we, thus, combine the complementary capabilities of humans and machines in a Hybrid Intelligence method. To reach our aim, we follow a design science research approach to develop a Hybrid Intelligence method that combines the strength of both machine and collective intelligence to demonstrate its utility for predictions under extreme uncertainty.

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Crowdfunding is now established as a valid alternative to conventional methods of financing for startups. Unfortunately, to date, research has not investigated how backers can be encouraged to support entrepreneurs beyond funding. The aim of this study is to design and evaluate certain design elements for reward-based crowdfunding platforms that can engage backers in co-creational activities for product development. The study uses a design science research (DSR) approach and the theoretical concept of psychological ownership to inform a new design and then experimentally test that design. The results suggest that the derived artifacts positively influence co-creational activities in crowdfunding and that feelings of psychological ownership play an important mediating role. The contribution of this research is threefold. First, this paper extends crowdfunding's application potential from merely a method of financing to a method of value creation with customers for product development. Second, the study advances DSR by applying a new DSR approach that shows whether a design performs as hypothesized by theory. Third, this research allows the exploration of backers' individual behavior as opposed to their collective behavior.

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Current research suggests that crowdfunding not only serves as an alternative source of capital but also as a flexible tool allowing start-ups to systematically integrate a crowd into their innovation processes. However, an adequate understanding of how start-ups can systematically leverage the co-creation potential of their early customers during crowdfunding is still nascent. Against this background, the aim of this research is to conceptualize and examine the concept of co-creation in the context of reward-based crowdfunding. In doing so, we distinguish it from other methods of user integration in the realm of open innovation and discuss how entrepreneurs can leverage reward-based crowdfunding to engage their customers in the development and deployment of their product and service offerings.