Mind the Future Gap: Introducing the FOD Framework for Future-Oriented Design

There are many uncertainties and ambiguities in the design of future-oriented artifacts. Societal and environmental developments are unclear; technologies not ready; target users not accessible. Nevertheless, designing future-oriented artifacts provides opportunities to either create radical innovations that present a competitive advantage, or to engage with relevant stakeholders in a speculative way. This paper provides a framework for developing, discussing, and evaluating future-oriented artifacts, which was developed based on literature and conceptual theorizing. It consists of a process model and a morphological box, outlining eight categories of relevance along with several options to choose from. Subsequently, we applied the framework to an existing future design project to illustrate its applicability. The framework spans the space of possible design and evaluation approaches and, hence, provides a guiding schema for researchers and practitioners to discuss the potentials and implications of design concepts for future-oriented artifacts

Designing the Future With the “Delphi Design Sprint”: Introducing a Novel Method for Design Science Research

This paper introduces a novel innovation method that focuses on the development of future-oriented artifacts. The “Delphi Design Sprint” combines two existing methods—the Delphi method and Design Sprints. The development of the method follows an action research approach and was tested and validated in a university-led design project involving a panel of 20 international experts. This paper introduces the method and describes exemplary results of the project’s outcome

Crafting Future Scenarios with the Help of AI: Potentials of a Hybrid Delphi Expert Panel

This paper examines the potential of ChatGPT to enhance the established Delphi method by providing additional AI-infused expertise. We investigated several aspects of the Delphi method independently: the integration of abstract AI-infused expertise perspectives, the generation of an AI “clone” (digital twin) of a human expert, the rating of scenarios through AI, and the capability of AI to iterate future scenarios and to provide qualitative feedback. The findings suggest that AI systems can augment a Delphi panel by providing new perspectives but cannot replace individual human experts and their respective expertise. The insights shall inform other researchers who want to conduct hybrid Delphi studies with AI-infused expertise. In that sense, with this paper, we aim to lay the foundation for a hybrid Delphi study method and suggest actionable recommendations