Johannes Dahlke


Last Name

Dahlke

First Name

Johannes

ORCID

0000-0002-9268-3632

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2022 - 20258
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Publications 1 - 8 of 8
  • Inter-firm Diffusion of Artificial Intelligence: The Role of Epidemic Effects and Relational Embeddedness
    Item type: Other Conference Item
    Dahlke, Johannes; Beck, Mathias; Kinne, Jan; et al. (2022)
  • Digitalisierung in KMU: Potenzial wird oft nicht ausgeschöpft
    Item type: Journal Article
    Beck, Mathias; Dahlke, Johannes; Wörter, Martin (2023)
    Die Volkswirtschaft
  • Digital technology and innovation performance: the power and perils of environmental complexity
    Item type: Other Conference Item
    Dahlke, Johannes; Beck, Mathias; Wörter, Martin (2024)
  • Agent-Based Modelling and Machine Learning: A New Paradigm for Complexity Economics and Sustainability Transitions? 1
    Item type: Book Chapter
    Bogner, Kristina; Müller, Matthias; Dahlke, Johannes; et al. (2024)
    Routledge International Handbook of Complexity Economics
    Complexity economics and transitions studies face the challenging task of accounting for complexity to explain the dynamics of socio-economic systems against the backdrop of grand societal challenges. A promising method to represent and better understand complex systems through simulation is the practice of agent-based modelling (ABM). However, researchers using ABM face the trade-off between capturing the complexity and the ease of designing and interpreting respective models. Beyond highlighting the relevance of using machine learning (ML) as an enabling mechanism for ABM to address this conflict, we provide concrete examples of how employing machine learning techniques with the objectives to increase the accuracy, understanding, and validity of models, can help to capture or process complexity. While we argue in favor of realizing the potentials of the green and digital twin transition in scientific practice, the resulting size and opaqueness of models require researchers to design, interpret, and communicate ML-driven ABM responsibly.
  • Epidemic effects in the diffusion of emerging digital technologies: evidence from artificial intelligence adoption
    Item type: Journal Article
    Dahlke, Johannes; Beck, Mathias; Kinne, Jan; et al. (2024)
    Research Policy
    The properties of emerging, digital, general-purpose technologies make it hard to observe their adoption by firms and identify the salient determinants of adoption. However, these aspects are critical since the patterns related to early-stage diffusion establish path-dependencies which have implications for the distribution of the technological opportunities and socio-economic returns linked to these technologies. We focus on the case of artificial intelligence (AI) and train a transformer language model to identify firm-level AI adoption using textual data from over 1.1 million websites and constructing a hyperlink network that includes >380,000 firms in Germany, Austria, and Switzerland. We use these data to expand and test epidemic models of inter-firm technology diffusion by integrating the concepts of social capital and network embeddedness. We find that AI adoption is related to three epidemic effect mechanisms: 1) Indirect co-location in industrial and regional hot-spots associated to production of AI knowledge; 2) Direct exposure to sources transmitting deep AI knowledge; 3) Relational embeddedness in the AI knowledge network. The pattern of adoption identified is highly clustered and features a rather closed system of AI adopters which is likely to hinder its broader diffusion. This has implications for policy which should facilitate diffusion beyond localized clusters of expertise. Our findings also point to the need to employ a systemic perspective to investigate the relation between AI adoption and firm performance to identify whether appropriation of the benefits of AI depends on network position and social capital.
  • When is AI adoption contagious? Epidemic effects and relational embeddedness in the inter-firm diffusion of artificial intelligence
    Item type: Other Conference Item
    Dahlke, Johannes; Beck, Mathias; Kinne, Jan; et al. (2022)
  • Digital technology and firm performance: The power and perils of environmental complexity
    Item type: Presentation
    Dahlke, Johannes; Beck, Mathias; Wörter, Martin (2023)
  • Patterns in management research on artificial intelligence: A longitudinal analysis using structural topic modeling
    Item type: Journal Article
    Dahlke, Johannes; Ebersberger, Bernd (2025)
    Journal of Evolutionary Economics
    The field of management research on applied artificial intelligence (AI) is growing rapidly as the technology is maturing in industrial applications. Due to the nascent state of the scientific discourse, it is hard to discern thematic focal points or how well managerial and technical topics are integrated. Based on 10,036 publications over 25 years, we map the topic landscape of AI-related management research, longitudinal patterns of topics, and structural changes of topic networks and research communities. Our model identifies 71 unique topics, indicating a strong but myopic focus on technological capabilities and applications. The lagged response to technological paradigm shifts indicates a double pacing problem. Network structures of thematic research communities reveal increased centralization and interconnections, suggesting the field's role in transferring basic AI research to industrial implementation. However, topics in technology management of AI seem to be separated from recent advances in AI. We propose mechanisms to foster an integrative discourse on applied AI that allows management research to act as a sense-giving institution. This includes focusing on fundamental technological characteristics instead of applications and strengthening the role of journals as discourse intermediaries.
Publications 1 - 8 of 8