European Journal of Information Systems

Neurophysiological Foundations and Effects of Contemporary Digital Technologies

Introduction and Motivation
The advent of increasingly powerful artificial intelligence (AI) systems (e.g., large language models (LLMs) such as ChatGPT, diffusion models, graph neural networks, or vision transformers used in domains such as healthcare, manufacturing, software development or finance), together with other contemporary digital technologies (e.g., autonomous cars, immersive worlds, and neuroadaptive interfaces), has fundamentally reshaped human interaction with information systems. As these technologies become embedded in work, consumption, private life, and society in general, the ways in which they affect human cognition, emotion, and social interaction remain poorly understood.

A growing body of research highlights that neurophysiological methods can provide unique insights into these phenomena across a broad range of IS contexts—whether AI-driven or not. NeuroIS—the interdisciplinary field that integrates neuroscience and psychophysiological approaches with information systems (Dimoka et al. 2012; Riedl et al. 2010; Riedl & Léger 2016)—has been at the forefront of this movement. Originating at the International Conference on Information Systems (ICIS) in Montréal in 2007, NeuroIS celebrates its 20-year anniversary in 2027. This milestone provides an opportune moment to reflect on the progress achieved, highlight challenges, and chart new directions for NeuroIS research in an era of rapid technological change—incorporating AI, but also extending beyond it.

Over the past two decades, NeuroIS research has established the potential of neurophysiological methods such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), eye tracking, and various other neurophysiological measures (e.g., electrodermal activity, heart rate variability) to enrich IS theory (e.g., Vance et al. 2018). These approaches have been shown to improve prediction accuracy of different IS phenomena (e.g., trust in online environments, technostress, cybersecurity behaviors), uncover hidden cognitive and affective mechanisms, and open new methodological horizons (Dimoka et al. 2011; Pfeiffer et al. 2020; Riedl et al. 2014). A particularly important trajectory in this research stream has been the integration of neurophysiological and behavioral levels of analysis: by linking neural responses to observable IS-related behaviors and self-report measures, scholars have developed more complete theories of user cognition, emotion, and decision-making. Papers published in the European Journal of Information Systems have also contributed significantly to the advancement of knowledge in this field (e.g., Anderson et al. 2026; Lutz et al. 2023).

In an era of pervasive digitalization—including, but going beyond, AI—this integration becomes even more essential. Understanding not only what individuals do when interacting with advanced digital systems (including AI), but also how their brains and bodies adapt, allows researchers to explain emergent behaviors such as overreliance, automation bias, algorithm aversion, new forms of collaborative cognition, or “cognitive debt,” that is, the cognitive costs that accumulate when people rely heavily on digital assistants for task execution. Moreover, neurophysiological measures enable the study of phenomena that may remain inaccessible to self-reports or behavioral observation alone, such as unconscious biases, implicit trust, or neural markers of attention and memory.

Beyond neurophysiology, this special issue also welcomes work that integrates genetic perspectives into IS research. Prior publications have demonstrated the promise of genetics for explaining individual differences in IS-related behaviors (Brown & Sias 2023; Browne & Walden 2020). Combining genetic and neurophysiological approaches may provide a powerful multi-level framework to understand how contemporary digital technologies—including AI, but also other IS artifacts and sociotechnical arrangements—affect humans at both biological and behavioral levels.

This special issue therefore seeks high-quality, theory-driven, and methodologically rigorous research that examines the neurophysiological (and related biological) foundations, mechanisms, and effects of contemporary IS technologies and sociotechnical systems—incorporating AI as an important case, but not limiting the scope to AI—while recognizing the need to connect neural-level insights to observable IS-related behavior and self-report measures. We invite submissions from information systems scholars, neuroscientists, psychologists, computer scientists, geneticists, and other disciplines to advance understanding of how contemporary IS reshapes human cognition, affect, decision-making, and social interaction.

Importantly, this special issue encourages contributions across the full range of IS research traditions. Neurophysiological and genetic insights may advance design-oriented research, for example by informing new principles for human-centered and neuroadaptive system design (e.g., vom Brocke et al. 2020). They may also enrich IS economics research, by offering a biological basis for understanding productivity, well-being, or value creation in digitally mediated contexts. Likewise, studies grounded in organizational behavior, strategy, or societal perspectives are highly relevant if they integrate or reflect on neurophysiological evidence. By welcoming diverse approaches, this special issue aims to bridge disciplinary and methodological boundaries and stimulate broad engagement within and beyond the IS community. Furthermore, the application potential of research findings is emphasized, highlighting the importance of ecological validity in NeuroIS research (Balapour & Riedl 2025).

In line with EJIS’ tradition of intellectual openness, this special issue explicitly welcomes visionary, provocative, and contrarian contributions that challenge taken-for-granted assumptions, problematize dominant narratives (including AI-centric ones), or open up unconventional research directions. While studies may employ approaches that are intentionally contrarian (Nandhakumar 2010), this is not a requirement.

Illustrative Research Questions
The following questions illustrate possible directions. They are intentionally broad and include AI-focused topics as well as other contemporary NeuroIS issues beyond AI; submissions need not be limited to these themes.

Cognitive and Neural Processes

• How does sustained interaction with AI-enabled systems (e.g., LLMs) and other advanced digital tools (e.g., automation and decision support) reshape neural networks associated with language, reasoning, and memory?
• Does reliance on AI systems and other forms of automation/digital decision support alter attentional control, working memory, metacognition, or problem-solving processes at the neurophysiological and behavioral levels?
• What are the implications of digitally augmented cognition for neuroplasticity, skill acquisition, and expertise development over time?
• How can integrated neurophysiological and behavioral measures provide a fuller picture of technology-mediated cognitive processes—when users interact with AI, recommender systems, immersive environments, or adaptive interfaces?

Affective and Emotional Responses

• How do individuals’ brains respond to empathetic or anthropomorphic cues exhibited by conversational AI and other interactive systems (e.g., social robots or avatars)?
• What neurophysiological correlates underlie trust, reliance, distrust, or skepticism toward AI systems as well as toward other algorithmic or platform-based systems (e.g., recommenders, rankings, or automated security warnings)?
• How does emotional regulation change when decision-making is mediated by AI support, algorithmic decision aids, or digital nudges?
• What are the neurophysiological mechanisms of stress, fatigue, or overload in contexts of AI-assisted work as well as other digitally intensified work settings (e.g., algorithmic management, hyperconnectivity, and platform work)?

Social and Group Dynamics

• How does technological mediation in team collaboration (including AI copilots and other collaboration platforms) influence the neurophysiological foundations of social coordination, empathy, and shared attention?
• What neural mechanisms underlie shifts in authority, leadership, and influence when AI becomes a co-decision-maker, or when platforms/algorithms shape visibility, reputation, and coordination in groups?
• How do cultural differences modulate neurophysiological responses to AI and other digitally mediated collaboration settings (e.g., remote work platforms or videoconferencing)?
• How do gender, age, or personality differences modulate neurophysiological responses to IS artifacts, and what does this imply for inclusive design and governance?

Behavioral, Design, and Societal Implications

• How might AI-driven systems and other algorithmic/persuasive designs reinforce or mitigate cognitive biases, and what neurophysiological evidence supports (or refutes) these mechanisms?
• What neural signatures accompany ethical dilemmas and moral decision-making in AI-mediated contexts as well as in broader digital governance settings (e.g., privacy, surveillance, ranking/recommendation, and cybersecurity)?
• How does long-term use of AI and other digitally intensive work systems shape neurophysiological well-being, stress, or mental health at the workplace?
• How can neurophysiological insights inform the design of AI systems and other digital systems (e.g., adaptive interfaces or security warnings) that are more intuitive and aligned with human cognitive limits?
• What are the neurophysiological underpinnings of productivity, efficiency, or value creation in digitally mediated economic interactions (including AI-mediated, platform-mediated, and automation-mediated contexts)?
• How can genetic and neurophysiological approaches together explain individual differences in the adoption and use of digital systems?
  Methodological Advances
• Which neurophysiological tools (e.g., fMRI, EEG, fNIRS, eye tracking, galvanic skin response, heart rate variability) are best suited for investigating contemporary IS phenomena (including AI-related ones), and how do trade-offs depend on context and ecological validity?
• How can hybrid approaches combining neurophysiology with computational methods (e.g., machine learning, natural language processing, agent-based modeling) enrich IS theory development across AI and non-AI contexts (e.g., platform governance, digital nudging, and adaptive interfaces)?
• How can multi-level research designs integrate genetic, neurophysiological, behavioral, self-report, and organizational data to address IS phenomena more holistically, including unintended consequences and counterintuitive effects of contemporary digitalization?

Reference Disciplines
We encourage authors to draw on and contribute to insights from cognitive neuroscience, neuropsychology, neuroeconomics, consumer neuroscience, affective computing, neuroergonomics, and genetics, alongside (Neuro)IS research. The aim is to foster dialogue across disciplinary boundaries and to establish novel theoretical and methodological pathways.

Relevance and Contribution
We particularly welcome submissions that (i) offer distinctive NeuroIS contributions, (ii) connect neurobiological evidence to consequential IS phenomena, and (iii) are willing to be bold (e.g., by challenging established assumptions or presenting counterintuitive findings). Specifically, we welcome submissions that:

• advance or develop new IS theories grounded in neurophysiological or genetic evidence;
• offer actionable implications for the design, governance, and responsible use of AI systems in organizations and society;
• provide methodological innovations that strengthen the rigor and reproducibility of biological and neurophysiological IS research;
• explicitly take into account that NeuroIS research is ideally characterized by a high degree of ecological validity;
• reflect on the 20-year trajectory of NeuroIS and its future potential in the age of AI.

Associate Editors
Bonnie B. Anderson, Brigham Young University, USA
Dinko Bačić, Loyola University Chicago, USA
Colin Conrad, Dalhousie University, Canada
Verena Dorner, Vienna University of Economics and Business, Austria
Nadine R. Gier-Reinartz, Heinrich-Heine-University Düsseldorf
Milena Head, McMaster University, Canada
Alan R. Hevner, University of South Florida, USA
Qiqi Jiang, Copenhagen Business School, Denmark
Marion Korosec-Serfaty, University of Québec in Montréal, Canada
Alexander Maedche, Karlsruhe Institute of Technology, Germany
Gernot R. Mueller-Putz, Graz University of Technology, Austria
Pierre-Majorique Léger, HEC Montréal, Canada
Mario Nadj, University of Duisburg-Essen, Germany
Fiona Nah, Singapore Management University, Singapore
Adriane Randolph, Kennesaw State University, USA
Ofir Turel, University of Melbourne, Australia
Eric A. Walden, Texas Tech University, USA
Peter Walla, Sigmund Freud Private University Vienna, Austria
Dezhi Wu, University of South Carolina, USA

References
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