No abstract

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.

Purpose Past research demonstrated that novel IT-based business models generate tremendous returns for innovators. However, the risks associated with these innovations remain under-explored. This paper aims to address this critical gap analyzing risks and offering important insights particularly for practitioners. Design/methodology/approach The authors adopted an exploratory multiple-case study research design. It draws on 22 semi-structured interviews with managers from leading energy utilities, as well as leading providers of virtual power plants technology within the German energy industry. Findings The research reveals that main risks in new digital business models in the energy sector are associated with three forms of interdependence between innovation actors: the regulatory, the technological and the collaborative. To deal with these interdependencies, the authors propose an original multi-step risk management framework. This framework considers the outreach as a critical dimension for risk assessment and offers a new risk response matrix to draw individual and collective mitigation activities for specific types of risks. Practical implications This paper offers a framework for the management of interdependence risks that are fundamental for business model innovations based on IT. Thus, it is applicable in companies both inside the energy sector and beyond. Originality/value This paper analyzes an important digital business model innovation that has not yet been explored in management literature – the virtual power plant (VPP). It is based on original and current empirical work and proposes a novel risk management framework for business organizations.

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.