NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis
Abstract
:1. Introduction
2. Theoretical Background
2.1. The Origins and Development of NeuroIS
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- Multimodal Data Fusion: The utilization of EEG-fMRI has increased by 40% since 2022, providing enhanced temporal precision (<1 ms of EEG) and spatial resolution (3–5 mm of fMRI). The integration of hybrid eye tracking and neuroimaging now achieves synchronization under 10 ms, which is crucial for examining visual attention in information systems. This convergence aids researchers in comprehending the cognitive and emotional mechanisms underlying system usage. FMRI enables real-time visualization of brain activity during user interactions, uncovering decision-making processes and responses to interface designs.EEG facilitates the capture of immediate neural reactions to stimuli, thereby assisting in the evaluation of user engagement and cognitive load [14,15]. Eye-tracking technology reveals patterns of visual attention, indicating which areas of the interface attract focus and how users navigate digital content [16]. Additionally, the incorporation of physiological measures such as skin conductance and heart rate variability provides a holistic perspective on user experience, capturing emotional responses that behavioral metrics may overlook [17].
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- Mobile Neurotechnology: The adoption of wearable EEG technology has experienced a 25% annual growth from 2022 to 2025, facilitating ecologically valid studies of human–computer interaction [18]. Advances in fMRI technology have achieved isotropic resolution of 0.55 mm for mapping prefrontal cortex activation during decision-making tasks [19].
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- Real-Time Applications: Neurofeedback systems that utilize EEG alpha and beta power modulation have demonstrated a 30% enhancement in user interface adaptation speed [6].
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- Biometric Authentication Systems: EEG-based identification now achieves an accuracy rate of 94% through the use of steady-state visual evoked potentials (SSVEP) [20].
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- Neuro-adaptive Interfaces: Closed-loop systems that adjust user interface complexity based on prefrontal fMRI activation have shown a 15% improvement in task performance [21].
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- Multi-Omics Integration: Pilot studies are investigating the integration of EEG biomarkers with proteomic data to predict stress levels in remote workers [22].
2.2. Applications of NeuroIS
- Informing IT Design and IS StudiesThe existing neuroscience literature can inform the design of IT tools and IS studies, even in the absence of direct application of neuroscience methodologies.
- Bridging IT Tools and BehaviorBrain activity and other physiological responses may serve as a connection between IT tools and IT-related behaviors, thereby introducing a biological perspective to the analysis.
- Elucidating Theoretical MechanismsNeuroscience and psychophysiological methodologies can elucidate the theoretical mechanisms through which IT tools affect behavior.
- Enhancing IT Tool EvaluationMeasurements of brain activity and other biological responses can enhance the evaluation of IT tools.
- Assessing Challenging ConstructsNeuroscience techniques allow for the assessment of constructs that are challenging to measure through self-report methods, such as automaticity in IT usage.
- Predicting Significant OutcomesBiological states and processes may serve as more reliable predictors of significant outcomes, such as user health, compared to self-reported measures.
- Understanding IT’s Impact on Brain FunctionNeuroscience methods can facilitate an understanding of whether and how the usage of IT tools impacts brain function.
- Developing Adaptive SystemsBiological states and processes can be utilized in real-time to develop adaptive systems that enhance outcomes related to health, well-being, satisfaction, and productivity.
- Creating Biofeedback SystemsReal-time data concerning a user’s biological state, such as stress levels, can be leveraged to create biofeedback systems, potentially yielding positive effects on health and performance.
- Revolutionizing Human–Computer InteractionMetrics of brain function may replace conventional input devices (e.g., mouse or keyboard) in human–computer interaction, potentially enhancing enjoyment and productivity across various contexts, including video gaming and enterprise systems.
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- User Experience Research: This field employs neuroimaging methodologies, including functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), to examine users’ cognitive and emotional reactions to diverse interfaces and design components. The integration of NeuroIS in user experience research facilitates the development and enhancement of more user-centric and effective systems [23].
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- Neuromarketing: Combining neuroimaging and psychological methodologies, neuromarketing seeks to comprehend consumers’ responses to marketing stimuli, encompassing advertising, product design, and branding. This approach yields valuable insights into consumer preferences and decision-making processes [24].
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- Information Security: NeuroIS investigates the neural correlates associated with security threats and decision-making processes to devise more effective security measures and policies. Through this lens, NeuroIS aids in designing systems that are less vulnerable to human error and manipulation [25,26,27,28,29].
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- Healthcare Systems: The application of neuroimaging and physiological monitoring is being explored to enhance the design of healthcare systems, including electronic health records and medical devices, ultimately improving patient safety and user experience [30].
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- Education and Training: Principles derived from neuroscience are being utilized to develop more effective e-learning systems and training programs, aimed at optimizing the delivery of educational content and improving learning outcomes [31].
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- Numerous aspects of information systems (IS) research are closely connected to human information processing and decision-making behavior. Insights gained from neuropsychology and cognitive neuroscience can substantially contribute to the exploration of various IS phenomena [7]. This study endeavors to categorize these topics through a systematic bibliometric analysis, encompassing 256 articles published over the preceding 17 years (2007–2024), and aims to deliver comprehensive statistical insights into the research landscape and trends surrounding “NeuroIS”.
3. Materials and Methods: Bibliometric Analysis
4. Results and Findings
4.1. Published Documents on NeuroIS
- Pioneering Phase (2011–2013)This phase is characterized by a low annual article count and a lack of discernible trends. The pioneering phase established the foundational framework for NeuroIS research. During this period, foundational research was conducted, laying the groundwork for future advancements in the field.
- Growth Phase (2014–2018)This phase marks a period of steady growth, with an increasing number of publications each year. Researchers began to explore broader applications of NeuroIS, leading to the establishment of key themes and methodologies. The growth phase expanded the scope and depth of the field, with increasing contributions from diverse disciplines.
- Acceleration Phase (2019–2024)This phase is marked by a sudden, exponential surge in growth observed each year. While signs of growth are evident in the initial period (2019–2021), the full potential of this phase has not yet been realized. The acceleration phase represents a period of rapid advancement, though challenges remain in fully harnessing its potential.Challenges faced during this period, such as methodological complexities and interdisciplinary integration, necessitate further exploration through innovative approaches [2,40,41]. These phases highlight the evolving nature of NeuroIS research, reflecting both its growing significance and the need for continued innovation to address emerging challenges.
4.2. The Analysis of the Collaboration Network of NeuroIS
4.2.1. Prominent Countries
4.2.2. Prominent Institution
4.2.3. Prominent Authors
4.3. The Co-Citation Network of NeuroIS
4.3.1. Document Co-Citation Network
4.3.2. Author Co-Citation Network
4.3.3. Journal Co-Citation Network
4.4. Emerging Trends in NeuroIS and Future Research Directions
4.4.1. References with Citation Bursts
4.4.2. Co-Occurrence Keywords Analysis
5. Research Discussion
6. Conclusions: Research Limitations and Future Research Trends in NeuroIS
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- Integration with AI and Machine Learning: As AI technologies continue to advance, it is anticipated that an increasing amount of research will focus on the ways in which neurophysiological data can enhance AI decision-making processes, particularly in terms of comprehending user preferences and refining user interface designs [73].
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- Integration of NeuroIS and Generative AI: The integration of NeuroIS and Generative AI represents a transformative frontier in information systems research. This interdisciplinary synergy leverages neuroscience principles and advanced AI technologies to enhance our understanding of human–computer interactions and develop innovative solutions for complex challenges.
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- Focus on Well-Being and Ethics: In light of the rapidly evolving capabilities of neurotechnology, future research is likely to increasingly confront ethical considerations and the implications of technology on mental health and well-being [74].
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- Augmented User Experiences: Ongoing investigations into augmented reality (AR) and virtual reality (VR) are expected to become central, as they analyze the effects of immersive technologies on cognitive and emotional responses [75].
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- Personalization and User-centric Design: Future studies may take a more in-depth approach to personalized computing experiences, tailoring technologies to accommodate diverse cognitive styles and preferences, thereby enhancing user satisfaction and efficiency [76].
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- Longitudinal Studies: There is a potential for an increase in longitudinal studies aimed at evaluating the long-term effects of technology on cognitive functions and decision-making, thus providing more comprehensive data for in-depth analyses [77].
Funding
Conflicts of Interest
References
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Criteria | Description |
---|---|
Database | Scopus |
Field | Title, keywords, abstracts |
Years | January 2007 to January 2024 |
Search string | NeuroIS |
Type of publication | Journal articles, book chapters, conference paper, conference review, review |
Language | English |
Rank | Country by Documents | Documents | Rank | Country by Citation | Citation |
---|---|---|---|---|---|
1 | United States | 87 | 1 | United States | 2849 |
2 | Germany | 72 | 2 | Germany | 1257 |
3 | Austria | 52 | 3 | Austria | 1176 |
4 | Canada | 40 | 4 | Canada | 1093 |
5 | Australia | 33 | 5 | Australia | 584 |
6 | China | 12 | 6 | Liechtenstein | 541 |
7 | Singapore | 6 | 7 | Hong Kong | 215 |
8 | Liechtenstein | 5 | 8 | Taiwan | 151 |
9 | Switzerland | 4 | 9 | China | 129 |
10 | Taiwan | 4 | 10 | Sweden | 120 |
Rank | Institutions | Documents | Citations | Country |
---|---|---|---|---|
1 | Karlsruher Institut für Technologie | 33 | 374 | Germany |
2 | Johannes Kepler University Linz | 32 | 919 | Austria |
3 | HEC Montréal | 28 | 566 | Canada |
4 | the University of Newcastle | 25 | 269 | Australia |
5 | University of Applied Sciences | 24 | 369 | Austria |
6 | Brigham Young University | 18 | 387 | United States |
7 | Temple University | 16 | 1250 | United States |
8 | Kennesaw State University | 11 | 22 | United States |
9 | Texas Tech University | 11 | 118 | United States |
10 | Technische Universitat Graz | 10 | 534 | Austria |
11 | Indiana University Bloomington | 10 | 650 | United States |
Rank | Author | Document | Citations | Institutions | Country |
---|---|---|---|---|---|
1 | Riedl, R. | 40 | 589 | University of Applied Sciences Upper Austria | Austria |
2 | Léger, P.M. | 24 | 151 | HEC Montréal | Canada |
3 | Adam, M.T.P. | 18 | 69 | the University of Newcastle, Australia | Australia |
4 | Weinhardt, C. | 15 | 10 | Karlsruher Institut für Technologie | Germany |
5 | Davis, F.D. | 12 | 279 | Rawls College of Business | United States |
6 | Vance, A. | 12 | 96 | Virginia Tech, Pamplin College of Business | United States |
7 | Dimoka, A. | 11 | 1090 | C. T. Bauer College of Business | United States |
8 | Walla, P. | 11 | 27 | Sigmund Freud Private Universitat Wien | Austria |
9 | Anderson, B.B. | 10 | 129 | Brigham Young University | United States |
10 | Lutz, B. | 6 | 18 | Universitat Freiburg | Germany |
Cluster ID | Size | Silhouette Score | Mean (Cite Year) | Label (LSI) | Label (LLR) | Label (MI) |
---|---|---|---|---|---|---|
0 | 39 | 0.823 | 2011 | technostress | electroencephalography (EEG) (5.43, 0.05) | IS use (0.9) |
1 | 31 | 0.936 | 2013 | security warnings | informational social influence (3.27, 0.1) | informational social influence (0.62) |
2 | 29 | 0.912 | 2015 | human–computer interaction | user experience (7.28, 0.01) | longitudinal experimental design (0.49) |
3 | 24 | 0.955 | 2017 | flow experience | customer experience (10.23, 0.005) | face reader (0.19) |
4 | 20 | 0.976 | 2013 | brain–computer interfaces | flow theory (4.82, 0.05) | flow theory (0.22) |
8 | 13 | 0.949 | 2016 | systems design | information systems (4.81, 0.05) | cholinergic receptor nicotinic alpha 4 (0.35) |
11 | 9 | 0.961 | 2018 | information processing | information processing (13.81, 0.001) | taxonomy (0.06) |
12 | 8 | 0.989 | 2010 | biofeedback; decision-making processes games | IT artifacts (7.27, 0.01) | NeuroIS (0.06) |
14 | 4 | 1 | 2014 | electronic network of practice | information filtering (8.3, 0.005) | NeuroIS (0.09) |
Rank | Citation Counts | Cluster ID | Title and Reference |
---|---|---|---|
1 | 16 | 1 | Dimoka, A., Davis, F. D., Gupta, A., Pavlou, P. A., Banker, R. D., Dennis, A. R., … & Weber, B. (2012). On the use of neurophysiological tools in IS research: Developing a research. [46] |
2 | 14 | 0 | Dimoka, A. (2010). What does the brain tell us about trust and distrust? Evidence from a functional neuroimaging study. Mis Quarterly, 373–396. [47] |
3 | 14 | 0 | Dimoka, A., Pavlou, P. A., & Davis, F. D. (2011). Research commentary—NeuroIS: The potential of cognitive neuroscience for information systems research. Information Systems Research, 22(4), 687–702. [1] |
4 | 13 | 2 | Riedl, R., & Léger, P. M. (2016). Fundamentals of neuroIS. Studies in neuroscience, psychology and behavioral economics, 127. [4] |
5 | 12 | 0 | Riedl, R., Hubert, M., & Kenning, P. (2010). Are there neural gender differences in online trust? An fMRI study on the perceived trustworthiness of eBay offers. MIS quarterly, 397–428. [48] |
6 | 12 | 0 | Riedl, R., Randolph, A. B., Brocke, J.V., Léger, P. M., & Dimoka, A. (2010). The potential of neuroscience for human-computer interaction research [49] |
7 | 10 | 1 | Dimoka, A. (2012). How to conduct a functional magnetic resonance (fMRI) study in social science research. MIS quarterly, 811–840. [50] |
8 | 10 | 0 | Brocke, J. V., Riedl, R., & Léger, P. M. (2013). Application strategies for neuroscience in information systems design science research. Journal of Computer Information Systems, 53(3), 1–13. [51] |
9 | 9 | 2 | Riedl, R., Mohr, P. N., Kenning, P. H., Davis, F. D., & Heekeren, H. R. (2014). Trusting humans and avatars: A brain imaging study based on evolution theory. Journal of Management Information Systems, 30(4), 83–114. [52] |
10 | 7 | 1 | De Guinea, A. O., Titah, R., & Léger, P. M. (2014). Measure for measure: A two study multi-trait multi-method investigation of construct validity in IS research. Computers in Human Behavior, 29(3), 833–844. [53] |
Rank | Author | Citations |
---|---|---|
1 | Riedl, R. | 781 |
2 | Davis, F.D. | 517 |
3 | Dimoka, A. | 494 |
4 | Pavlou, P.A. | 330 |
5 | Leger, P.M. | 301 |
6 | Benbasat, I. | 293 |
7 | Broke, J.V. | 257 |
8 | Dennis, A.R. | 247 |
9 | Gefen, D. | 228 |
10 | Kenning, P. | 188 |
Rank | Journal | Count | Centrality | Year |
---|---|---|---|---|
1 | MIS Quarterly | 86 | 0.02 | 2011 |
2 | Neuroimage | 52 | 0.06 | 2008 |
3 | PLOS ONE | 43 | 0.01 | 2017 |
4 | Journal of the Association for Information Systems | 36 | 0.01 | 2008 |
5 | Communications of the Association for Information Systems | 32 | 0.00 | 2012 |
6 | Science | 29 | 0.04 | 2008 |
7 | Computers in Human Behavior | 27 | 0.00 | 2017 |
8 | Nature | 26 | 0.00 | 2011 |
9 | Studies in Neuroscience, Psychology and Behavioral Economics | 24 | 0.00 | 2018 |
10 | Annual Review of Psychology | 19 | 0.01 | 2008 |
Keywords | Year | Frequencies | Centrality |
---|---|---|---|
NeuroIS | 2008 | 94 | 0.55 |
Information systems | 2008 | 55 | 0.23 |
Electroencephalography | 2010 | 30 | 0.11 |
Information use | 2014 | 27 | 0.03 |
Behavioral research | 2010 | 21 | 0.10 |
Decision-making | 2013 | 17 | 0.10 |
Neurophysiology | 2012 | 15 | 0.05 |
FMRI | 2008 | 15 | 0.08 |
Human–computer interaction | 2011 | 14 | 0.07 |
Eye tracing | 2016 | 13 | 0.11 |
Functional neuroimaging | 2017 | 12 | 0.02 |
Brain | 2010 | 9 | 0.02 |
Neuroscience | 2010 | 9 | 0.01 |
Cognitive neuroscience | 2010 | 8 | 0.05 |
Hear rate | 2012 | 7 | 0.01 |
Machine learning | 2016 | 7 | 0.03 |
Cognitive load | 2008 | 7 | 0.01 |
Electronic commerce | 2014 | 5 | 0.02 |
Laboratory experiments | 2010 | 5 | 0.01 |
Security of data | 2015 | 5 | 0.02 |
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Entezarian, N.; Bagheri, R.; Rezazadeh, J.; Ayoade, J. NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis. Metrics 2025, 2, 4. https://doi.org/10.3390/metrics2010004
Entezarian N, Bagheri R, Rezazadeh J, Ayoade J. NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis. Metrics. 2025; 2(1):4. https://doi.org/10.3390/metrics2010004
Chicago/Turabian StyleEntezarian, Nahid, Rouhollah Bagheri, Javad Rezazadeh, and John Ayoade. 2025. "NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis" Metrics 2, no. 1: 4. https://doi.org/10.3390/metrics2010004
APA StyleEntezarian, N., Bagheri, R., Rezazadeh, J., & Ayoade, J. (2025). NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis. Metrics, 2(1), 4. https://doi.org/10.3390/metrics2010004