Browsing by Person "Dahlke, Johannes"

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Artificial intelligence and corporate ideation systems
(2026) Lehmann, Selina L.; Dahlke, Johannes; Pianta, Valentina; Ebersberger, Bernd; Lehmann, Selina L.; University of Hohenheim, Stuttgart, Germany; Dahlke, Johannes; University of Twente, Enschede, The Netherlands; Pianta, Valentina; University of Hohenheim, Stuttgart, Germany; Ebersberger, Bernd; University of Hohenheim, Stuttgart, Germany
Many companies leverage the creativity of their employees to gather ideas for innovations. These ideas are collected, saved, and evaluated via platforms known as corporate ideation systems. Moderated ideation systems (ideation 2.0) emerged as a solution to address the limitations of traditional, rather passive ideation systems (ideation 1.0). In this study, we apply a qualitative mixed‐method approach (literature review, company case studies, expert interviews, and focus group workshops) to examine how artificial intelligence (AI) technology may relieve the remaining pains of stakeholders in collaborative, moderated ideation systems. This leads to a new framework of corporate ideation systems, termed AI‐based ideation systems (ideation 3.0). We identify five major pains suffered by stakeholders in today's moderated ideation systems: creativity pain, content formulation pain, search pain, analytical pain, and administration pain. We find that AI agents act as pain relievers when serving five supporting functions: inspirer, stylist, matchmaker, analyst, and organizer. The interconnected nature of pains means that employing AI agents in certain functions within corporate ideation systems can create positive externalities across the entire system. Practical insights into AI agent implementation and application in corporate ideation systems are provided by six mini‐case studies, which lead to the proposition of two organizational principles: the contextualization of AI usage and the generalization of AI implementation as the requirements for successful ideation 3.0.
Flooding the landscape of knowledge: perspectives on transitions to artificial intelligence in industry
(2024) Dahlke, Johannes; Ebersberger, Bernd
The progress in artificial intelligence (AI) technology is advancing at an unprecedented pace and its applications increasingly impact economic actors and society at large. As the world enters the fourth industrial revolution, the integration of AI technology into industries promises to become a crucial determinant of economic performance and qualitative change within the economy. It also requires to discuss the roles of humans and machines in the process of value creation. Against this backdrop, this doctoral dissertation investigates the current state and dynamics of AI transitions, with a pronounced focus on industrial regimes. It comprises three empirical studies, each depicting different levels of industrial transitions towards AI—moving from a consideration of micro-level technological niches, to meso-level industrial structures, to macro-level landscape trends. This dissertation contributes to our understanding of socio-technical transitions towards AI by showing that sustainable and just transitions towards AI-based industrial regimes require not only consideration of the technological characteristics, but also the sociomaterial context governing its integration, as well as reversely being altered by the diffusion of the technology itself. The work provides further insights for policymakers, practitioners, and researchers as it emphasizes the need for network-based analyses of complex diffusion dynamics within industries, and the need to integrate systemic socio-economic perspectives into extant concepts of responsible AI.