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2025 AOM Annual Meeting

OMT

RM

13393: The Algorithmic Apprentice: AI-Powered Research Methods

Saturday, July 26, 2025
12:00 - 16:00 CEST (GMT+2)
Location: Bella Center: Hall C- C1-m3
Session Type: PDW (workshop)
Orientation: Teaching,Practice,International,Research,Diversity
Shareable link: https://cdmcd.co/YKLzJv
This hands-on workshop investigates how artificial intelligence (AI) and machine learning (ML) can transform research in organizational theory, strategy, and international management. Moving beyond mere paper presentations, we will engage participants in exploring how these tools, capable of processing vast datasets, can overcome limitations of conventional methods, leading to novel insights and stronger theoretical foundations. Participants will assess how supervised ML algorithms automate document classification and reveal hidden patterns, using examples such as identifying AI-related patents and discovering non-linear relationships between variables. The workshop will emphasize the power of ML to improve causal inference through advanced matching techniques, and to measure abstract constructs through novel combinations of text mining, factor analysis, facial recognition, and analyses of video and audio. The use of large language models (LLMs) to analyze discourse and generate explanations for social phenomena will also be explored, highlighting AI’s potential to advance qualitative research. Participants will not only learn about the theoretical potential of AI/ML, but will also gain a practical understanding of how these techniques can be applied in their own research. This hands-on approach will focus on skill-building, empowering participants to use AI/ML to enhance data analysis, improve causal inference, measure complex constructs, and uncover hidden patterns. The workshop will address risks such as algorithmic bias and overfitting, stressing the importance of rigorous validation and transparent reporting. Attendees will leave with concrete knowledge of employing these powerful methods, enabling them to integrate AI/ML into their research and contribute to the cutting edge of methodological practices.