NeuroIS: A Systematic Review of NeuroIS Through Bibliometric Analysis

Abstract

This study aims to provide a comprehensive knowledge mapping and extensive analysis of NeuroIS research, elucidating global trends and directions within this field from January 2007 to January 2024. A visual analysis of 256 research articles sourced from the Scopus database is conducted. The knowledge mapping, utilizing CiteSpace (CiteSpace 3.6 R1) and VOSviewer (VOSviewer 1.6.19), illustrates the current research landscape, encompassing collaboration networks, co-citation networks, references exhibiting citation bursts, and keyword analysis. The findings highlight the United States and Germany as leading nations in the exploration of NeuroIS, with the Karlsruher Institut für Technologie in Germany identified as a prominent institution in this domain. René Riedl, Pierre-Majorique Léger, Marc T. P. Adam, and Christof Weinhardt emerge as the most prolific authors in the field. Noteworthy themes that have garnered attention in recent years include customer experience, information systems, and information processing. Document analysis reveals that the study by Dimoka et al. in 2012 is the most cited work, providing a comprehensive overview of global NeuroIS research. Analysis of the document co-citation network identifies electroencephalography (EEG) in the context of technostress, the social impact of information in security alerts, and user experience in human–computer interaction as key areas of focus. René Riedl is recognized as the most cited researcher, while MIS Quarterly is distinguished as the leading journal in this field. Twelve NeuroIS papers exhibit high citation counts, with significant activity noted in 2021 and 2022. The timeline delineates the evolution of topics such as neuroscience, fMRI, cognitive neuroscience, social media, trust, eye tracking, and human–computer interaction. This study pioneers the examination of the current research status of NeuroIS through bibliometric analysis and the latest available data. It advocates for enhanced collaborations among scholars and institutions to improve information systems management and foster the development of NeuroIS. The study underscores the importance of ongoing research and cooperation in NeuroIS to deepen our understanding of how neuroscience can inform information systems design and management, thereby enhancing human–technology interaction. By identifying key trends, influential authors, and prominent themes, this analysis lays the groundwork for further exploration and innovation in this interdisciplinary domain. As technology continues to advance and our reliance on information systems intensifies, the insights derived from NeuroIS research can provide valuable perspectives on enhancing user experiences, optimizing information processing, and applying neuroscientific principles to develop more effective IT artifacts. Through sustained collaboration and knowledge sharing, the NeuroIS community can drive progress and shape the future of information systems management in an increasingly dynamic digital landscape.

1. Introduction

The application of cognitive neuroscience within information management represents an emerging research methodology in the domain of information systems, referred to as the neural information system [1]. This innovative approach merges principles and techniques from cognitive neuroscience with conventional information management practices, thereby enhancing our comprehension of the mechanisms by which the human brain processes and utilizes information. By harnessing insights derived from cognitive neuroscience, researchers and practitioners can formulate more effective information management strategies, systems, and technologies. This interdisciplinary methodology has the potential to revolutionize the design, implementation, and utilization of information systems, resulting in advancements in decision-making, human–computer interaction, and knowledge management [2,3].

NeuroIS is designed to accomplish two primary objectives. First, it seeks to enhance the theoretical understanding of the design, development, utilization, and impact of information and communication technologies on users from a neurological perspective. Second, it aims to contribute to the design and development of IT systems that positively influence practical outcome variables, including health, well-being, satisfaction, adoption, and productivity [4,5]. Generally, the overarching goal of numerous NeuroIS studies is to attain a deeper understanding of human behavior within the context of information systems [6].

Research topics explored in NeuroIS encompass conceptual and empirical studies, along with theoretical and design science research. A prior review [1,5,7] suggests that the field can be categorized into four distinct areas: (1) cognitive processes related to learning and comprehension through experience and contemplation; (2) emotional processes associated with the experience of both negative and positive emotions; (3) social processes involving interactions among individuals; and (4) decision-making processes that entail selecting an option from the array of available possibilities.

The diverse range of measurement tools utilized in the field of NeuroIS can be classified into three primary categories: (1) tools for assessing the central nervous system, such as MRI, fMRI, fNIRS, and EEG; (2) tools for evaluating the peripheral nervous system, including EKG, galvanometer, and EMG; and (3) tools for measuring the hormonal system to assess levels of cortisol, adrenaline, and oxytocin.

Furthermore, it is anticipated that quantitative and molecular genetics will play a significant role in future NeuroIS research [2,5]. Despite substantial advancements in the NeuroIS field over the past decade, it has not yet achieved widespread recognition within information systems journals, as evidenced by recent reviews of the discipline [2,5]. While certain studies have delineated specific trends within this domain, such as the work by Zhao and Siau [8], which focused exclusively on various neurophysiological tools in neuroscience, they provided a comprehensive evaluation of the strengths and limitations of these tools, particularly in the context of information systems research, without addressing other aspects of NeuroIS. Similarly, Zhu et al. [9] conducted a focused analysis of journals dedicated to neuromarketing within the marketing sector from 2010 to 2021. Additionally, Lin et al. [10] examined 99 published articles in neuroscience and analyzed citation data from 19 information systems journals operating within the SCI/SSCI framework with the aim of identifying the most productive countries, universities, authors, journals, and publications in NeuroIS from 2010 to 2021. However, this study relied exclusively on ABS 3-star journals in the field of information systems as its primary data source, overlooking the broader spectrum of NeuroIS journals that extend beyond 3-star classifications.

NeuroIS research is also present in journals such as the Journal of Electronic Commerce, E-Commerce Research, Frontiers in Neuroscience, the Journal of Advertising Research, the European Journal of Marketing, the Journal of Consumer Psychology, the Journal of Interactive Marketing, and the Journal of Consumer Research, among others, within the domains of electronic commerce and marketing. Furthermore, the “Notes in Information Systems and Organization”, compiled during the NeuroIS Retreat and NeuroIS Society conferences, contain several issues relevant to NeuroIS that were not included in this review and are identified as a research gap in the current study.

Despite the significant contributions made in the field, it is imperative to emphasize the lack of a comprehensive analysis that synthesizes the extensive body of research in neuroscience and information systems, as evidenced by various journals and conference proceedings. This notable deficiency in the literature is particularly concerning, as it obstructs our understanding of how these two disciplines could potentially inform and enhance one another, thereby resulting in missed opportunities for interdisciplinary advancement. The objective of this paper is to address this gap and provide valuable insights into the intersection of neuroscience and information systems, thereby enriching our collective knowledge and understanding.

Consequently, the present study seeks to fill this lacuna in the literature through the application of bibliometric analysis utilizing CiteSpace 3.6 R1 and VOSviewer 1.6.19. Researchers employ bibliometric analysis for various purposes, including the identification of emerging trends in article and journal performance, collaboration patterns, and research constituents, as well as the examination of the intellectual structure within a specific domain of the existing literature [11,12]. The data central to bibliometric analysis are typically extensive and objective in nature (e.g., citations, publications, occurrences of keywords and topics), although its interpretations often require both objective (e.g., performance analysis) and subjective (e.g., thematic analysis) evaluations established through informed methodologies and procedures. In essence, bibliometric analysis serves as a valuable tool for deciphering and mapping the cumulative scientific knowledge and evolutionary nuances of well-established fields, making sense of large volumes of unstructured data in rigorous ways.

Well-executed bibliometric studies thus form robust foundations for advancing a field in innovative and meaningful ways, enabling and empowering scholars to (1) gain a comprehensive overview, (2) identify knowledge gaps, (3) generate novel ideas for further investigation, and (4) position their intended contributions to the field [11].

This study represents a pioneering effort to conduct a bibliometric analysis of “NeuroIS”, aiming to provide a comprehensive overview of the field’s development and to identify key topics. Furthermore, the study identifies leading scholars, institutions, and significant articles within this area, offering invaluable insights for researchers seeking collaboration opportunities, relevant journals, and influential research papers.

The subsequent sections of the paper are structured as follows: a discussion of the theoretical background, an introduction to the bibliometric methods, and an examination of the data sources. Following this, the paper presents the results of the bibliometric analysis, including co-occurring and co-citation analyses. It also highlights emerging trends in “NeuroIS” and provides future research directions. Finally, the study discusses the implications and limitations of the findings.

2. Theoretical Background

2.1. The Origins and Development of NeuroIS

NeuroIS (neuro-information syste) represents an interdisciplinary domain within the field of information systems, leveraging neuroscience and neurophysiological methodologies to attain a comprehensive understanding of the development, adoption, and impact of information and communication technologies. The integration of cognitive neuroscience approaches into information systems research was first proposed at the 2007 International Conference on Information Systems (ICIS), and the term “NeuroIS” was introduced by Dimoka et al. [13]. Since 2009, an annual academic conference has been established to showcase research and development initiatives at the intersection of information systems and neurobiology, with the objective of promoting the advancement of the NeuroIS field.

NeuroIS aspires to fulfill two primary objectives. Firstly, it aims to enhance theoretical insights into the design, development, utilization, and effects of information and communication technologies. Secondly, it seeks to contribute to the design and development of information technology systems that positively influence practical outcome variables such as health, well-being, satisfaction, adoption, and productivity.

The topics addressed in NeuroIS research encompass both conceptual and empirical studies, along with theoretical and design science research. This field incorporates a diverse array of neuroscience and neurophysiological tools, including techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), hormone assessments, skin conductance and heart rate monitoring, eye tracking, and facial electromyography. Furthermore, it is anticipated that quantitative and molecular genetics will play a significant role in future NeuroIS research [4].

Recent developments in NeuroIS research have identified the following three notable trends in the application of neurophysiological techniques:

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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].

Emerging frontiers in NeuroIS research include the following:

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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].

The anticipated progression of quantitative and molecular genetics within NeuroIS research may further enrich the field, enabling scholars to examine the biological foundations of individual differences in technology usage. This avenue of inquiry could lead to the development of personalized information systems tailored to specific neurological and genetic profiles, thereby enhancing user experience and effectiveness.

As NeuroIS continues to advance, it is imperative for researchers to embrace interdisciplinary approaches, drawing insights from psychology, neuroscience, computer science, and information systems. This collaborative spirit will foster innovations that not only advance theoretical understanding but also inform the design of more effective and intuitive technologies, ultimately bridging the divide between human cognition and digital interactions.

These advancements establish NeuroIS as a crucial field for thoroughly examining the neurocognitive mechanisms that govern interactions with technology, as its methodological rigor increasingly adheres to the established standards of clinical neuroscience.

2.2. Applications of NeuroIS

NeuroIS encompasses a wide array of applications across diverse domains and emphasizes various research areas, including technology adoption, users’ cognitive workload, website design, virtual environments, and the role of emotions in human–computer interaction, as well as IT security and other pertinent topics. The contributions of neuroscience to IS research can be summarized as follows [4]:

• Informing IT Design and IS Studies
  The 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 Behavior
  Brain 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 Mechanisms
  Neuroscience and psychophysiological methodologies can elucidate the theoretical mechanisms through which IT tools affect behavior.
• Enhancing IT Tool Evaluation
  Measurements of brain activity and other biological responses can enhance the evaluation of IT tools.
• Assessing Challenging Constructs
  Neuroscience techniques allow for the assessment of constructs that are challenging to measure through self-report methods, such as automaticity in IT usage.
• Predicting Significant Outcomes
  Biological 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 Function
  Neuroscience methods can facilitate an understanding of whether and how the usage of IT tools impacts brain function.
• Developing Adaptive Systems
  Biological 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 Systems
  Real-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 Interaction
  Metrics 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.

Based on these contributions, several key research areas within NeuroIS include the following:

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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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Financial Decision-Making: The examination of the neural mechanisms that underlie financial decision-making is conducted to advance superior decision support systems and tools for investors and financial professionals [32,33].

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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

The initial phase of the bibliometric analysis involves the selection of an appropriate database. For this purpose, the Scopus database has been selected due to its extensive coverage of the scientific literature, encompassing journals, conference proceedings, books, and various other scholarly publications, in addition to its sophisticated search and analytical capabilities. Scopus offers a variety of bibliometric indicators, including citation counts, h-index, and co-citation networks, establishing itself as a widely utilized and reputable resource for bibliometric analysis. Moreover, the database facilitates the straightforward tracking of research trends and collaboration patterns across diverse countries, institutions, and disciplines. Scopus also implements stringent data validation procedures to ensure the accuracy and completeness of bibliographic information [34].

Subsequently, data are extracted from the selected database and subjected to a rigorous filtering process. The specific query employed to retrieve data from the Scopus database is delineated in

Table 1. The criteria utilized for the screening process of the articles.

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

. According to the data acquired from Scopus, the inaugural article on “NeuroIS” was published in 2007. The parameters for the query’s publication date were established from January 2007 to January 2024, with the data downloaded on 12 January 2024. A total of 256 scholarly articles were compiled based on these inclusion criteria.

Figure 1 illustrates the flowchart detailing the data collection process. A four-stage data search methodology was adhered to, including identification, screening, eligibility, and inclusion [35]. The exact keyword search is presented below.

TITLE-ABS-KEY (“NeuroIS”) AND PUBYEAR > 2007 AND PUBYEAR < 2025 AND (EXCLUDE (DOCTYPE, “tb”) OR EXCLUDE (DOCTYPE, “ed”)) AND (LIMIT-TO (LANGUAGE, “English”)).

The final stage consists of determining the methods for bibliometric analysis. In this study, VOSviewer software [36]. was employed for visualizing results, while CiteSpace [37]. was utilized for science mapping. All bibliographic data were extracted from the Scopus database in both .csv and .ris file formats. Excel files (.csv) and portable network chart files (.png) were selected for the analysis.

Following the installation of CiteSpace, the .ris file extension was imported directly from Scopus. The data were meticulously prepared for analysis using the “CiteSpace Data Processing Utilities” interface of the program. In NeuroIS-oriented research, VOSviewer was employed to offer extensive details and network visualizations. VOSviewer software, used for the creation and visualization of bibliometric network maps, was deployed to examine the international keyword map [38,39].

The procedures for bibliometric analysis are concisely summarized in three steps: data preparation, a detailed examination of the bibliometric analysis specifics, and analysis of the findings derived from the analysis.

In this study, both the co-occurrence network and co-citation network were analyzed using VOSviewer and CiteSpace, respectively. Furthermore, a keyword analysis will disclose emerging trends and potential future research directions.

4. Results and Findings

4.1. Published Documents on NeuroIS

As illustrated in Figure 2, an analysis of NeuroIS publications sourced from the Scopus database reveals varied trends over the past 17 years (2007–2024). The volume of articles exhibited a steady increase from 2008 to 2011, with annual increments of 1, 3, 5, and 10 articles, respectively. However, a decline was observed in 2012 and 2013, with counts dropping to 8 and 3 articles, respectively. This was followed by a notable surge in 2014 and 2015, reaching 18 and 24 articles, respectively. A decrease to 9 articles was recorded in 2016, but a significant rise to 33 articles occurred in 2017.

The year 2018 saw a decline to 16 articles, while 2019 experienced a rebound to 20 articles. In 2020, there was a substantial increase to 39 articles, followed by a decrease to 31 in 2021. The number of articles rose again to 37 in 2022, yet a decline to 12 was noted in 2023, before escalating to 26 in 2024. This fluctuation in publication counts reflects the evolving interest and research dynamics within the NeuroIS discipline. Scholars appear to be responding to emerging trends and the broader sociotechnical context that influences their research agendas.

The years following 2017, particularly 2020 and 2022, indicate a revitalized interest in NeuroIS, likely spurred by technological advancements and neurocognitive studies that hold considerable relevance in contemporary information systems. Furthermore, the significant drop in publications during 2023 may suggest a temporary redirection of focus or challenges faced by researchers in adapting to new circumstances or publication demands.

Insights gleaned from the continuous analysis of publication trends afford stakeholders the opportunity to evaluate the maturation of the field and identify potential areas for further exploration. As the volume of research in NeuroIS expands, it becomes increasingly essential for researchers, practitioners, and educators to align their efforts to foster collaboration and interdisciplinary strategies that can further enhance this dynamic and evolving domain.

In summary, the data derived from the Scopus database provide a comprehensive overview of the publication landscape within NeuroIS, underscoring not only the growing interest over the years but also the cyclical nature of research focus within this innovative field.

According to findings from the Scopus database, the developmental timeline of NeuroIS research can be delineated into the following three distinct phases:

• 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

The analysis of the collaboration network within the NeuroIS domain illuminates the global landscape of research endeavors in this specialized field. A thorough investigation into the interconnectedness of countries through collaborative publications allows us to extract valuable insights into the distribution of knowledge and expertise.

The participation of diverse contributors from multiple nations highlights the international breadth of NeuroIS research, presenting a rich convergence of ideas and perspectives aimed at advancing the discipline.

Employing VOSviewer to assess the collaboration network within NeuroIS reveals several significant findings. VOSviewer enables the construction of collaboration networks by analyzing co-authorship patterns found in academic publications. This methodology incorporates data extraction, which aggregates author affiliations, publication counts, and citation linkages, alongside clustering, which organizes authors and institutions into clusters based on the frequency of co-authored works. In this context, the size of the nodes indicates the level of collaboration, while color coding reflects thematic or institutional affiliations.

Notably, this study identifies at least four countries that have contributed by publishing three articles each, while six countries have published over ten articles. The density visualization of the country network, as illustrated in Figure 3 generated by VOSviewer, reveals the interconnectedness of these nations, with the most substantial grouping comprising five clusters.

The findings indicate that a fully developed collaboration network among countries in the NeuroIS field has yet to emerge. Figure 3 offers a visual representation of these collaborative efforts, where larger circles and font sizes signify greater significance. A review of the collaboration map in Figure 3 indicates that the primary nodes of cooperation within NeuroIS are located in five key countries: the United States, Germany, Austria, Australia, and Canada. The United States leads in publications with 87 documents and 2849 citations, followed by Germany with 72 documents (1257 citations), Austria with 52 documents (1176 citations), Canada with 40 documents (1093 citations), and Australia with 33 documents (584 citations).

As we navigate the complex web of collaborations, it becomes clear that while certain countries have emerged as pivotal players in advancing research, there remains substantial room for further growth and diversification of the collaborative network. The dynamic interplay among nations within the NeuroIS realm not only facilitates knowledge exchange but also lays the groundwork for innovative breakthroughs and advancements in this evolving field. The potential for increased synergy among researchers from diverse backgrounds cannot be overstated, as interdisciplinary collaboration may yield novel methodologies and applications that address intricate challenges in neuroinformatics and information systems.

In addition, as

Table 2. Prominent country by documents and citations ranked.

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

shows, the existing collaboration patterns indicate several intriguing trends. For example, the frequent partnerships between the United States and Germany reflect a robust exchange of ideas and research methodologies that can foster shared innovations. Similarly, countries such as Austria and Canada, despite their relatively limited publication outputs, play crucial roles in forming strategic alliances that enhance the quality and scope of NeuroIS research. The interdependence among these nations fosters an ecosystem that encourages diverse perspectives while reinforcing resilience against the challenges that may arise from isolated research efforts.

The analysis also highlights the underrepresentation of several regions, particularly those in the Global South, pointing to a clear area for development. Increasing participation from these regions could not only enrich the NeuroIS research landscape but also introduce unique cultural and intellectual perspectives often overlooked. Initiatives aimed at fostering awareness and collaboration could be pivotal in bridging this gap. Conferences, workshops, and collaborative platforms specifically designed for emerging nations would promote broader involvement and could lead to a more equitable distribution of research output.

Moreover, as technology continues to advance—particularly concerning computational capabilities and data analytics—the opportunities for NeuroIS research to intersect with fields such as artificial intelligence and machine learning are substantial. This convergence opens new avenues for groundbreaking research, enabling experts to harness data’s power in unprecedented ways. As researchers from various countries begin to leverage these technological advancements, it is imperative that they engage with a diverse range of collaborators to cultivate a multidimensional approach to problem-solving.

In conclusion, the examination of the collaboration network within NeuroIS not only elucidates the current landscape of international research but also underscores the significance of strategic partnerships. Fostering collaboration among a diverse array of contributors will be essential for the sustained advancement of knowledge and innovation in this domain. The collective endeavors of nations, unified by a shared objective, have the potential to unveil new avenues for progress, ultimately enriching the scientific comprehension of brain–computer interactions and their implications in information systems.

4.2.2. Prominent Institution

In the collaboration network comprising 160 organizations, a total of 83 institutions have contributed a singular article. Additionally, 25 institutions have published two articles each, while 14 institutions have each contributed three articles.

Furthermore, 8 institutions have published four articles each, and another 8 institutions have published five articles each. Moreover, 22 institutions have exceeded the publication of five articles. The visualization of the collaboration network among these organizations, illustrated in Figure 4 and generated by VOSviewer, presents a density visualization of the interconnected 160 institutions.

Notably, the Karlsruher Institut für Technologie in Germany stands out as the preeminent institution in the domain of “NeuroIS”, with 33 publications. It is closely followed by Johannes Kepler University Linz (32 publications), HEC Montréal business school (28 publications), the University of Newcastle (25 publications), the University of Applied Sciences Upper Austria, School of Management (24 publications), Brigham Young University (18 publications), Temple University (16 publications), Kennesaw State University (11 publications), Texas Tech University (11 publications), and Technische Universitat Graz and Indiana University Bloomington (10 publications).

It is evident that a majority of the institutions are based in the United States, corroborating the findings of the analysis concerning the collaboration networks of countries and regions, which reveals that 42 institutions have formed a cohesive collaboration network across six clusters.

Additionally, other institutions also sustain their respective collaboration networks. In summary, the global collaboration among institutions constitutes a medium-scale collaborative network; however, inter-country collaborations among these institutions require further strengthening, as indicated in Figure 4.

Table 3. Top 11 institutions as per the number of publications.

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

details the top 11 institutions with the highest publication counts.

The research outputs from these leading institutions highlight their substantial contributions to the field of NeuroIS, effectively shaping the landscape of collaborative research initiatives. The diverse expertise and perspectives contributed by institutions worldwide facilitate a rich tapestry of research outcomes, thereby driving advancements and insights within NeuroIS and beyond.

4.2.3. Prominent Authors

In the author collaboration network, a total of 237 authors have each made contributions to the publication of at least one article, with 12 authors having published a minimum of two articles. The density visualization of the collaboration network is illustrated in Figure 5, which highlights seven prominent clusters.

Within these, the largest interconnected group of scholars comprises eight individuals. Notably, the red cluster in Figure 5 features leading authors Adam, M.T.P., with 18 publications, and Weinhardt, C., with 15 publications, representing Australia and Germany, respectively. These distinguished authors have forged a close-knit collaboration network with their colleagues, including Toreini, P., Knierim, M.T., Lux, E., and Maedche, A., who are affiliated with the Karlsruhe Institute of Technology in Germany. Furthermore, two authors are particularly notable for their substantial publication output: Riedl, R., with 40 publications, from the University of Applied Sciences Upper Austria, and Léger, P.M., with 24 publications, from HEC Montréal in Canada.

Each of these scholars has developed their own unique collaboration network, positioning them at the forefront of global efforts to advance NeuroIS techniques and methodologies in information systems management through the lens of neuroscience. In addition, as

Table 4. Top 10 authors based on the number of publications.

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

shows, Davis, F.D., with 12 publications, from the Rawls College of Business, and Dimoka, A., with 11 publications, from the C. T. Bauer College of Business in the United States, also emerge as significant figures in this field. The domain of NeuroIS has witnessed the establishment of a substantial collaboration network among researchers worldwide.

However, there remains a pressing need to enhance collaborations between researchers across various countries and institutions. It is noteworthy that collaborations predominantly occur among scholars affiliated with academic institutions in Europe and North America. As the field continues to progress, fostering a global network of collaborative scholars will be essential in propelling advancements and shaping the future of NeuroIS research.

4.3. The Co-Citation Network of NeuroIS

4.3.1. Document Co-Citation Network

The analysis of the document co-citation network has uncovered significant insights into the principal research topics within the discipline. By investigating the clusters and their corresponding labels generated by various algorithms, researchers acquire a comprehensive understanding of the interconnected themes and emerging trends in the literature. The elevated silhouette scores reflect the robustness and coherence of these clusters, indicating a clear delineation of research themes. This systematic approach to examining the document co-citation network not only assists in identifying pivotal areas of inquiry but also provides direction for future research endeavors and collaborative opportunities within the academic community [42].

Figure 6, generated using CiteSpace 6.3, illustrates the document co-citation network, which comprises 256 cited references and 239 co-citation links spanning the years 2007 to 2024, exhibiting a network density of 0.0335. We employed keyword terms and a log-likelihood ratio (LLR) weighting algorithm for cluster labeling. Notably, all silhouette scores exceed 0.8, indicating a high level of cluster quality [43].

Table 5. The 9 largest clusters in the field of NeuroIS.

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)

presents eight significant clusters identified within the document co-citation network. This network facilitates the identification of critical research topics in the field and aids researchers in selecting suitable research trajectories.

Cluster #0, the largest, contains 39 members and boasts a silhouette value of 0.823. It is designated as “electroencephalography (EEG)” by LLR, “technostress” by LSI, and “IS use” by MI. The second largest cluster (#1) consists of 31 members and a silhouette value of 0.936, identified as “informational social influence” by both LLR and MI and “security warnings” by LSI. The third largest cluster (#2) comprises 29 members with a silhouette value of 0.912, labeled as “user experience” by LLR, “human-computer interaction” by LSI, and “longitudinal experimental design” by MI. The fourth largest cluster (#3) contains 24 members, presenting a silhouette value of 0.955, and is labeled as “customer experience” by LLR, “flow experience” by LSI, and “face reader” by MI.

Cluster #4, the fifth largest, encompasses 20 members with a silhouette value of 0.976, designated as “flow theory” by both LLR and MI and “brain-computer interfaces” by LSI. The sixth largest cluster (#8) consists of 13 members and a silhouette value of 0.949, labeled as “information systems” by LLR, “system design” by LSI, and “cholinergic receptor nicotinic alpha 4” by MI. The seventh largest cluster (#11) has nine members with a silhouette value of 0.961, identified as “information processing” by both LLR and LSI and “taxonomy” by MI. Finally, the eighth largest cluster (#12) comprises eight members with an impressive silhouette value of 0.989, labeled as “IT artifacts” by LLR, “biofeedback” by LSI, and “NeuroIS” by MI. The ninth cluster (#14), although smaller, contains four members and achieves a silhouette value of 1, labeled as “information filtering” by LLR, “electronic network of practice” by LSI, and “NeuroIS” (0.02) by MI.

Electroencephalography (EEG), user experience, and IT artifacts are intricately connected to informational social influence, warranting significant attention. The integration of EEG technology with IT artifacts has unlocked promising opportunities for enhancing user experience and gaining insights into how individuals engage with technology. This convergence presents a fertile ground for investigating the impact of social dynamics on information processing and decision-making.

Cluster #11, titled “information processing”, is the most recent cluster, with a mean citation year of 2018, representing a substantial research opportunity for future scholars. Advances in information processing have contributed to improved decision-making and human behavior [44].

A substantial body of literature connects information processing to EEG measurements through time-frequency methods, alongside research examining the role of emotion in moral decision-making [45]. The most cited articles are detailed in

Table 6. Top 10 most cited papers with co-citation frequency.

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]

. Notably, the article addressing the application of neurophysiological tools has garnered significant citations. The paper titled “The use of neurophysiological tools in research: developing a research agenda for NeuroIS” by Dimoka A. in 2012 [46] olds the highest citation count. This article underscores the importance of integrating neurophysiological tools into research within the NeuroIS domain, highlighting the increasing interest in employing such tools to enhance understanding and formulate a research agenda. This focus on utilizing neurophysiological methodologies signifies a transition towards more precise and nuanced investigations in the field of NeuroIS.

4.3.2. Author Co-Citation Network

Author co-citation networks serve to identify key contributors within a specific academic domain. CiteSpace generally concentrates on the first author for this analysis [43], employing author abbreviations by default.

However, this methodology may result in multiple authors sharing identical names, particularly among Chinese scholars. Consequently, this study utilizes VOSviewer to examine the network of author co-citation.

The criterion for the minimum number of citations for an author is established at 20, resulting in a total of 152 authors from a dataset of 17,242 that meet this threshold. Figure 7, generated using VOSviewer, illustrates the author co-citation network comprising 213 authors from the years 2007 to 2024.

Table 7. Top 10 most cited authors with co-citation frequency.

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

presents the top 10 most frequently cited authors, based on co-citation frequency.

The results of the author’s co-citation network reveal a robust co-citation relationship within this research field. René Riedl emerges as the most cited author with 781 citations, followed by Fred D. Davis with 517 citations, and Angelika Dimoka with 494 citations, consistent with the findings from the co-citation analysis. These researchers have profoundly impacted the field through their contributions, as reflected in their elevated co-citation frequencies. The interconnectedness of these authors within the network underscores the collaborative essence of research in this domain and the shared intellectual contributions that have shaped scholarly discourse over time. Further exploration of the co-citation network may yield additional insights into the evolution of ideas and the dissemination of knowledge within the academic community.

4.3.3. Journal Co-Citation Network

In order to underscore the contributions of various journals to the NeuroIS field over the past 17 years, a journal co-citation network was constructed using CiteSpace, as illustrated in Figure 8. The results indicate a robust co-citation relationship among the journals examined. A total of 178 journals and 1556 co-citation links were identified, producing a network density of 0.0988. The top 10 most frequently cited journals, determined by co-citation frequency, are detailed in

Table 8. Top 10 most cited journals with co-citation frequency.

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

.

Notably, the journal MIS Quarterly emerges as the most referenced journal in the NeuroIS domain, with 86 citations.

As a leading journal in the field of information systems research, MIS Quarterly holds a significant position. Following it is Neuroimage, with 52 citations, and the journal PLOS ONE, which has garnered 43 citations. These preeminent journals substantially influence the development of the NeuroIS field and are highly regarded within the academic community. The co-citation network offers critical insights into the prominent contributors and interrelationships within this area of research, emphasizing the interconnected nature of NeuroIS scholarship.

4.4. Emerging Trends in NeuroIS and Future Research Directions

4.4.1. References with Citation Bursts

A citation burst indicates a significant increase in citations over a short period, thus highlighting emerging research hotspots within a particular field [43].

As illustrated in Figure 9 and Figure 10, twelve articles related to NeuroIS have exhibited citation bursts. Notably, one article commenced its citation burst in 2021, while another experienced increased citations in 2022. Riedl R. [2], in the article titled “Consumer-Grade EEG Instruments: Insights on the Measurement Quality Based on a Literature Review and Implications for NeuroIS Research”, observed a citation burst beginning in 2021. The rising interest in consumer-grade EEG instruments is attributed to their affordability, portability, and user-friendliness, with applications spanning brain–computer interfaces, experimental research, signal processing, and clinical studies. Similarly, Brocke J.V. [40], in the article “Advancing a NeuroIS Research Agenda with Four Areas of Societal Contributions”, experienced a citation burst in 2022. This work reflects on past achievements and future prospects, emphasizing four critical areas for significant societal contributions in forthcoming information systems (IS) research: IS design, IS use, emotion research, and neuro-adaptive systems.

These articles are evidently influential within the NeuroIS domain, fostering interest and dialogue among researchers. The observed citation bursts underscore their impact on contemporary research trends and the direction of future inquiries in this field. It is recommended that researchers engage with these articles to gain valuable insights and perspectives on the convergence of neuroscience and information systems.

4.4.2. Co-Occurrence Keywords Analysis

Keyword analysis serves as a valuable tool for identifying research hotspots within a specific domain, assisting scholars in understanding the evolutionary trajectory of the field and guiding future research directions for their peers [57].

This analytical approach uncovers patterns, connections, and trends within a research area by examining the co-occurrence of specific keywords in the academic literature. Through the analysis of keyword co-occurrence, researchers can derive insights into the principal themes, topics, and relationships present within a body of work [58].

Figure 11, Figure 12 and Figure 13 generated using CiteSpace 6.3 R1 and VOSviewer, illustrate the keyword co-occurrence network, with keywords related to NeuroIS being consolidated. Both CiteSpace and VOSviewer produce comparable results for keyword co-occurrence networks.

Table 9. Significant keywords by year.

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

presents key terms by year, emphasizing those that have appeared at least five times. Notably, the majority of keywords are associated with NeuroIS.

In Figure 11, the keyword co-occurrence network is represented using CiteSpace. The visualization indicates that the most frequent keyword is “NeuroIS”, with a frequency of 92. Other significant keywords include “Information Systems” (55), “electroencephalography” (30), “Information use” (27), “behavioral research” (21), “Decision making” (17), “Neurophysiology” (15), “FMRI” (15), “Human Computer Interaction” (14), “Eye tracing” (13), “Functional neuroimaging” (12), “Brain” (9), “Neuroscience” (9), “Cognitive Neuroscience” (8), and “Cognitive load” (7).

Figure 12 depicts the keyword co-occurrence network as generated by VOSviewer. In this visualization, each node represents a keyword, with the size of the node indicating the frequency of the keyword’s occurrence. Links between nodes signify co-occurrences of keywords, with the thickness of the link reflecting the frequency of these co-occurrences. Larger nodes represent higher keyword occurrences, while thicker links denote more frequent co-occurrences. Each color denotes a thematic cluster, where nodes and links within the cluster illuminate the scope of topics and their interrelationships [59].

Among these nodes, the seven most prominent clusters, determined by the number of elements they encompass, include “NeuroIS” (cluster 1), “Information Systems” (cluster 2), “electroencephalography” (cluster 3), “FMRI” (cluster 4), “Neurophysiology” (cluster 5), “Human Computer Interaction” (cluster 6), “behavioral research” (cluster 7), “Eye tracing” (cluster 8), “Brain” (cluster 9), “Neuroscience” (cluster 10), “Decision making” (cluster 11), “Cognitive Neuroscience” (cluster 12), “Machine learning” (cluster 13), and “Electronic commerce” (cluster 14).

The prominence of specific clusters, such as “NeuroIS” and “Information Systems”, underscores the central role these topics play in shaping discourse within the field. The inclusion of terms like “electroencephalography”, “FMRI”, and “Neurophysiology” highlights the interdisciplinary nature of NeuroIS, drawing insights from neuroscience and psychology. Understanding the interconnections among these keywords can assist researchers in identifying trends, gaps, and potential areas for further exploration at the intersection of neuroscience and information systems. This interconnectedness emphasizes the necessity for collaboration across disciplines and encourages deeper inquiries into how cognitive processes may influence user interactions with information systems. By investigating the correlation between brain activity and decision-making or behavioral responses in digital environments, researchers can develop more robust models that inform system design and user experience. For example, integrating findings from neuromarketing can enhance our understanding of user engagement, revealing potential pathways for optimizing interfaces and tailoring content to align with cognitive preferences [60].

Furthermore, exploring the implications of neurofeedback and brain–computer interfaces may lead to innovative applications within information systems, promoting enhanced usability and personalized experiences [61]. As technological landscapes continue to evolve, the convergence of neuroscience and information systems presents a fertile area for research that can address real-world challenges and improve the effectiveness of human–computer interaction [62]. The identification of emergent themes such as “NeuroIS”, “Cognitive load”, and “behavioral research” further illuminates the dynamic interplay between human factors and technological advancements. Investigating these themes can empower scholars and practitioners to mitigate barriers and enhance accessibility within information systems, ensuring that these tools cater to a diverse range of users.

In conclusion, as researchers advance their exploration of the NeuroIS domain, it is imperative that they cultivate interdisciplinary collaborations, disseminate knowledge, and adopt innovative methodologies. This collaborative endeavor not only enhances the rigor of academic discourse but also possesses the capacity to revolutionize practical applications, ultimately narrowing the divide between human cognition and the rapidly evolving information technologies within an increasingly interconnected world.

To further investigate the intricacies inherent within each cluster, the Layout Timeline View feature of CiteSpace was utilized to analyze the temporal patterns of keyword distribution.

Figure 13 depicts the existence of seven clusters, reflecting the trends identified in the same figure. The temporal perspective provided by the timeline view offers a detailed comprehension of the evolution of various topics that serve as focal points for rigorous inquiry. While Figure 13 presents a multitude of keywords, the structural integrity of the knowledge graph remains intact, with minimal overlap among individual terms. This observation highlights the diverse nature of research topics and the rapid evolutionary pace characterizing fields such as neuroscience, fMRI, cognitive neuroscience, social media, trust, eye tracking, and human–computer interaction.

The Layout Timeline View function indicates that from 2007 to 2010, research in NeuroIS was relatively limited, with a predominant emphasis on cognitive neuroscience. Information systems and human–computer interaction continue to be central areas of investigation.

A significant research milestone emerged in 2014, characterized by extensive exploration and application of relevant technologies, particularly fMRI. The widespread adoption of fMRI has directed research efforts toward various trajectories on a large scale. Noteworthy studies have examined issues such as habituation to security warnings [63], the development of trust between individuals and their avatars [64,65,66,67], and the increasing importance of social media and trust as emerging research domains. The introduction of eye-tracking technology in 2017 has considerably expanded the scope of related technologies and methodological approaches.

The integration of eye tracking has enabled experimental designs that explore nuanced aspects within neuroscience, including cognitive congruence and memory [68,69], encompassing but not limited to working memory. Grounded in these foundational principles, the trajectory of research in this domain continues to evolve.

The visual representation of these clusters serves as a vital tool for identifying key trends and shifts in research focus over time. By examining the progression of keywords within each cluster, researchers can attain a nuanced understanding of the dynamic landscape characterizing these interdisciplinary domains. This thorough analysis emphasizes the intricate interplay between diverse research spheres and underscores the multifaceted nature of contemporary scientific exploration.

5. Research Discussion

The integration of cognitive neuroscience into information management represents a significant advancement in the research landscape of information systems, commonly referred to as the neural information system (NeuroIS). This emerging field has garnered substantial interest from designers, practitioners, and scholars. This research employs CiteSpace and VOSviewer to address a critical gap in the existing literature by offering a comprehensive and detailed analysis of the current research landscape pertaining to NeuroIS. The implications of this study can be evaluated from both historical and prospective perspectives.

Historically, the research delineates the evolution of the field into three distinct phases: the pioneering phase (2011–2013), the growth phase (2014–2018), and the acceleration phase (2019–2024). This classification aids scholars in comprehending the present research environment and recognizing emerging trends. Additionally, this study provides an extensive overview of collaborative networks among countries, institutions, and researchers, underscoring the necessity for enhanced collaboration, particularly on an international scale. The analysis reveals a well-established collaborative network among nations, with the United States and Germany prominently featured. The Karlsruher Institut für Technologie in Germany is identified as a leading institution in this domain. However, there remains significant potential for improvement in inter-institutional collaboration. Strengthened collaborative efforts among institutions and researchers will be vital for advancing the interdisciplinary nature of NeuroIS and addressing the evolving challenges in information management.

Among relevant authors, René Riedl, Pierre-Majorique Léger, Marc T. P. Adam, and Christof Weinhardt are recognized as the most prolific contributors, highlighting the need for further scholarly collaboration. Through cluster analysis utilizing CiteSpace, it was observed that topics such as customer experience, information systems, and information processing are emerging areas that have catalyzed the increased adoption of NeuroIS, presenting novel challenges in information systems design. The study by Dimoka et al. [50], entitled “The Use of Neurophysiological Tools in IS Research”, is the most cited work, providing a comprehensive overview of global NeuroIS research. In examining document citation networks, the electroencephalography (EEG) cluster within the technostress domain, the social impact of the information cluster in the security alerts domain, and the user experience cluster in the human–computer interaction domain were identified as the predominant clusters.

René Riedl also emerges as the most cited scholar within the co-citation network. In the co-citation network of journals, MIS Quarterly is recognized as the most influential, demonstrating significant publication output in this field. The twelve NeuroIS articles exhibit citation bursts, with one article commencing its burst in 2021 and another receiving citations in 2022. Scholars from various disciplines have explored the intersection of information systems and neuroscience.

The temporal perspective provided by the timeline view offers a meticulous understanding of the evolution of topics such as neuroscience, fMRI, cognitive neuroscience, social media, trust, eye tracking, and human–computer interaction as focal points of inquiry. Looking ahead, future research in the field of NeuroIS is anticipated to delve into the practical implications of cognitive neuroscience in information management. By further investigating the application of neural information systems in decision-making processes, user experience design, and knowledge sharing, scholars aim to enhance the efficiency and effectiveness of information systems. As the field continues to evolve, it is expected that NeuroIS will play a pivotal role in shaping the future of information systems and technology, ultimately leading to innovative solutions and advancements in the digital era.

Furthermore, this study serves as a valuable resource for future researchers. The knowledge mapping of “NeuroIS” presented herein can enhance research efficacy and streamline the learning process for scholars, both academic and practitioner, with an interest in this domain. By identifying key contributors and highly cited articles, this study facilitates improved reading efficiency and enables scholars to pinpoint authoritative figures for further exploration.

6. Conclusions: Research Limitations and Future Research Trends in NeuroIS

The significance of bibliometric findings within the NeuroIS domain is substantial for several reasons. Firstly, it delineates the research landscape of NeuroIS. Bibliometric analysis identifies the most influential papers, authors, and journals, thereby providing new researchers with a framework to navigate the field and recognize key contributions [70]. Secondly, it elucidates trends in NeuroIS. By pinpointing growth patterns and emerging topics, bibliometric studies can highlight areas of increasing interest. For example, a rise in research focused on emotional responses to user interfaces may indicate a shift in future research priorities [71]. Thirdly, it uncovers collaboration networks within NeuroIS. Bibliometric analyses often examine co-authorship and collaboration networks, revealing prominent researchers and institutions. Such collaboration fosters innovation and creates opportunities for interdisciplinary research. Furthermore, understanding these trends equips organizations and funding agencies to identify salient topics where research investments may yield substantial returns, thereby enhancing strategic planning and resource allocation.

Citation metrics enable researchers to assess the impact of specific studies and the overall relevance of NeuroIS research within the broader domains of information systems and neuroscience [72]. A critical examination of the evolution of NeuroIS research underscores its interdisciplinary nature. Initially a niche area, NeuroIS has rapidly expanded due to collaboration among researchers from psychology, neuroscience, computer science, and information systems, enriching the field. This evolution has deepened the understanding of how to design technology that aligns with human cognitive processes. The advent of advanced neuroimaging technologies, such as fMRI and EEG, has facilitated experimental research in NeuroIS. These tools allow researchers to observe real-time brain activity yielding insights into user behavior that were previously inaccessible. This advancement not only enhances theoretical understanding but also contributes to practical applications in the design of information technology and user experience.

The identification of emerging themes within NeuroIS is noteworthy. Early studies predominantly focused on basic cognitive functions associated with technology use; however, the scope has expanded to encompass complex topics such as emotions, social interactions in virtual environments, and user trust in AI systems. This evolution illustrates the field’s adaptability to contemporary technological advancements. Real-world applications of NeuroIS are increasing, with research increasingly oriented towards practical implementations in business contexts, such as optimizing user interfaces, enhancing decision-making processes, and understanding user interactions. The translation of research findings into business practices represents a significant trend that bolsters the relevance of NeuroIS. As the field continues to grow, there is an escalating need for critical reflection on ethical concerns, including data privacy and the implications of neuroscience for behavior modification. Researchers are beginning to address these issues, indicating a shift towards more responsible research practices.

In conclusion, bibliometric analyses provide invaluable insights into the current state and future trajectory of NeuroIS research. Through a critical reflection on its evolution, the field has demonstrated considerable potential to inform the design and implementation of technologies that cater to human cognitive and emotional needs. The future of NeuroIS appears bright and promising, with innovative contributions to research and practical applications across various domains.

This study addresses a notable gap in bibliometric research in NeuroIS, but it also has limitations. It is important to note that the scope was confined to the English literature. To enhance the depth and breadth of analysis, future researchers are encouraged to incorporate works in languages beyond English. Moreover, it is crucial to acknowledge that bibliometric analysis alone may not capture the intricacies of research logic and variable relationships. Tools such as CiteSpace and VOSviewer primarily analyze terms derived from titles and abstracts, which can overlook contextual subtleties present in full texts. VOSviewer treats singular and plural forms or variant keyword expressions as distinct entities, thereby fragmenting co-occurrence networks. Additionally, VOSviewer does not provide native support for time-sliced visualizations, which limits its capacity to analyze evolving trends. While CiteSpace offers burst detection capabilities, its time-zone views necessitate manual interpretation and may risk oversimplifying temporal patterns.

Furthermore, VOSviewer is limited in its capacity to integrate data from multiple databases and is confined to the representation of network graphs, lacking capabilities for geospatial or tree-map visualizations. The cluster labels generated by CiteSpace (e.g., LLR/LSI algorithms) may result in arbitrary themes without the validation of domain experts. To address these shortcomings, future research could explore the integration of CiteSpace’s burst detection with VOSviewer’s network resolution, complemented by bibliometric methods and manual content analysis to validate clusters and topics. Consequently, subsequent investigations could utilize narrative or systematic literature reviews to achieve these aims. It is also crucial to acknowledge that bibliometric analyses are dependent on the database utilized, with potential biases associated with reliance on Scopus. Thus, emerging researchers are encouraged to expand their search to alternative data sources such as the Web of Science (WOS) and Google Scholar.

The forthcoming research trend in NeuroIS is expected to emphasize the development of new tools and technologies. These advancements will facilitate a deeper exploration of the cognitive processes inherent in the use and adoption of information systems. Areas including brain–computer interfaces, neuroimaging techniques, and advanced data analytics are anticipated to play essential roles in elucidating the subtleties of user behavior and decision-making.

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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.

The convergence of NeuroIS and Generative AI holds immense potential to revolutionize information systems by creating more intuitive, adaptive, and human-centric technologies. By leveraging the strengths of both fields, researchers can develop solutions that not only enhance user experiences but also address ethical and societal challenges in the age of AI.

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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].