Building the Next Generation of NeuroIS Scholars: Lessons Learned, Challenges Overcome, and Future Directions for the Field

Pierre-Majorique Léger (), Bonnie Brinton Anderson (), Randall K. Minas (), Gernot R. Müller-Putz (), Adriane B. Randolph () and René Riedl ()
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Pierre-Majorique Léger: HEC Montréal, Tech3Lab
Bonnie Brinton Anderson: Brigham Young University, Marriott School of Business
Randall K. Minas: University of Hawai’i, HINTLab, Shidler College of Business
Gernot R. Müller-Putz: Graz University of Technology, Institute of Neural Engineering
Adriane B. Randolph: Kennesaw State University, BrainLab
René Riedl: University of Applied Sciences Upper Austria, Digital Business Institute, School of Business and Management

A chapter in Information Systems and Neuroscience, 2025, pp 327-332 from Springer

Abstract: Abstract Over the past two decades, the NeuroIS community has evolved from self-training in neuroscientific methods to systematically integrating these techniques into PhD education. This transition has fostered a second generation of researchers with structured training in neurophysiological tools, ensuring deeper methodological expertise within the field. However, despite these advances, the number of proficient NeuroIS scholars remains limited, and the broader IS discipline still lacks widespread understanding of these methods and related research approaches. This panel will reflect on key success factors in building the NeuroIS community and explore strategies for expanding training efforts beyond mentorship-based models. Additionally, the discussion will address how AI-driven advancements are transforming neuroscientific analysis, requiring both early-career and senior researchers to continuously upskill. Panelists will examine how the IS community can leverage AI innovations to enhance neurophysiological research, strengthen methodological adoption, and position NeuroIS at the forefront of interdisciplinary scientific progress.

Keywords: NeuroIS training; Curriculum; Functional brain research; Neurophysiological measurement; EEG; fMRI; fNIRS; Physiological measurement; Artificial intelligence (AI) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-00815-2_30

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