Applying LLMs and GenAI in Innovation Economics – Potentials and Pitfalls

Background

Large Language Models (LLMs) and Generative AI are revolutionizing how we understand innovation economics and measure the economic impacts of technological change. In innovation studies and innovation economics, these tools facilitate novel approaches to causal identification, enable scalable analysis of previously intractable economic questions, and open new frontiers in understanding how AI technologies reshape market dynamics, productivity patterns, and policy outcomes including within specific sectors such as healthcare, energy, science, education, and public policy.

However, innovation economics has yet to fully harness the potential of emerging AI-driven LLM methods for addressing core economic questions about innovation performance, R&D patterns, and technological diffusion. With efforts to integrate these techniques, there is a growing need for researchers to exchange best practices and discuss both the potential and methodological limitations inherent to these novel approaches.

Workshop Announcement & Committees

We are pleased to announce an upcoming two-day workshop hosted by AAU Business School (DK) in collaboration with representatives from the University of Strasbourg (FR), Copenhagen Business School (CBS), University of Bremen (DE), and UNU-MERIT (NL) that jointly contribute to the PhD and Research network "Economics of Innovation, AI & Data Science applications". The 6th workshop of the network will provide a platform for sharing and discussing research that integrates LLMs and Generative AI within innovation economics, emphasizing methodological rigor and policy relevance in the analysis of AI's economic impacts.

Call for Submissions

We seek submissions that use LLMs and GenAI in research methods of innovation economics, for example, combining AI techniques with econometric analysis, causal identification strategies, or policy evaluation frameworks. Applications can also refer to text analysis of innovation documents, AI-enhanced difference-in-differences analysis, innovation measurement and metrics, natural experiments and counterfactuals using LLM-processed data, automated survey analysis, historical innovation analysis, or utilising novel unstructured data sources to construct datasets. Submissions should demonstrate how these tools advance our understanding of innovation economics exemplifying application potential and discussing inherent limitations.

Submissions might apply, explore, or advance LLMs and GenAI techniques in the context of:

We particularly welcome submissions that demonstrate how LLMs and GenAI can advance the economic research frontier by overcoming existing limitations in traditional econometric techniques. Contributions exploring how these techniques enhance our understanding of AI-driven economic transformations across specific sectors are especially welcome.

Submission Guidelines

Submissions should highlight both the economic potential and the methodological limitations that emerge when using LLMs and GenAI techniques in innovation economics research. This should address data quality, temporal knowledge boundaries, causal identification challenges, economic validation, reliability for policy applications, ethical considerations, as well as reproducibility of economic findings.

Format Requirements:

• Extended abstract: Maximum 2,000 words
• Reference list: Maximum 500 words
• One figure or table illustrating key findings (optional)
• Please combine everything into a single PDF document for upload.

Important Dates

Contact Information

For any inquiries, please contact the organizing committee: