AI and Blockchain For Next-Generation E- Governance: A Comprehensive Bibliometric Review In Smart City Innovation

AI and Blockchain For Next-Generation E- Governance: A Comprehensive Bibliometric Review In Smart City Innovation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI and Blockchain For Next-Generation E- Governance: A Comprehensive Bibliometric Review In Smart City Innovation Sandi Lubis, Achmad Nurmandi, Jamaluddin Ahmad, Eko Priyo Purnomo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5694080/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The merging of AI and blockchain technologies offers essential potential for e-governance, especially in improving predictive policy execution within smart cities. This study thoroughly reviews and assesses existing literature to clarify trends, key publications, and research gaps. Using detailed bibliometric analysis, we explored peer-reviewed articles from well-respected publishers indexed by Scopus, concentrating on works published from 2019 to 2024. Our results reveal an increasing volume of research examining the separate applications of AI and blockchain in e-governance. However, there is a noticeable lack of empirical studies focusing on their combined implementation. Major themes identified include the possibility of improved transparency and efficiency in public services, issues related to interoperability, and ethical considerations about data privacy and algorithmic accountability. This study is constrained by its dependence on bibliometric methods, which may not entirely reflect the practical complexities of technology integration across various governance contexts. Future research should emphasize longitudinal case studies and pilot initiatives to evaluate real-world uses of AI and blockchain in e-governance, addressing regulatory and ethical challenges to promote responsible adoption. This work adds to the global discussion on digital governance, providing a foundational framework for advancing AI and blockchain-enabled smart city projects. Artificial intelligence Blockchain E-Governance Smart City Bibliometric Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction The field of digital governance is developing rapidly, and the integration of AI and blockchain technologies is increasingly seen as a groundbreaking way to improve the efficiency, transparency, and responsiveness of governance systems [ 1 ]. These technologies are especially relevant in smart cities, which use advanced digital tools to optimize urban management and enhance the quality of life for residents [ 2 ]. AI allows smart cities to process extensive data in real-time, providing predictive insights that inform policy decisions and resource distribution; meanwhile, blockchain offers a secure and unchangeable record of transactions and decisions, encouraging public administration transparency and trust [ 3 ]. Despite the growing use of AI and blockchain across various sectors, their potential in e-governance, particularly in smart city frameworks, still needs to be fully explored [ 4 ]. While AI is commonly used for its data analysis and processing abilities, the decentralized nature of blockchain presents distinct benefits for ensuring data integrity and security, especially in contexts where transparency and accountability are essential [ 5 ]. The combination of these technologies has significant potential for transforming governance. However, their joint application in smart city projects has yet to be thoroughly investigated, emphasizing a gap in the existing literature and a missed chance to devise more integrated and effective governance strategies [ 6 ]. This research tackles a vital issue the need for more thorough studies on the joint application of AI and blockchain in policy enforcement within smart cities [ 7 ]. While a significant body of literature addresses AI and blockchain separately, few investigations assess their synergistic potential for enhancing e-governance [ 8 ]. Moreover, most current studies focus on theoretical or conceptual frameworks, with a scarcity of empirical research exploring the real-world challenges and opportunities of merging these technologies in governance contexts [ 9 ]. This study seeks to close this gap by providing a systematic review and bibliometric analysis of the literature regarding integrating AI and blockchain in e-governance, specifically emphasizing their role in smart city policy enforcement. The research aims to determine existing trends, challenges, and opportunities in this emerging field, offering a comprehensive understanding that can inform future studies and aid in policy development efforts [ 10 ]; [ 11 ]. This study seeks to address the research issue by achieving several key objectives. Its main aim is to identify the current trends in the application of AI and blockchain technologies within e-governance, mainly focusing on their roles in developing and managing smart cities. By examining these trends, the study aims to simplify the revolutionary impacts of these technologies on urban governance systems. Besides, this research intends to explore the synergies between AI and blockchain, emphasizing how combining these technologies can improve governance processes. This investigation will emphasize the potential for enhanced public administration efficiency, transparency, and security. Finally, the study evaluates the challenges and opportunities associated with implementing AI and blockchain in smart city governance, striving to offer a comprehensive view of the practical implications of their integration. This research provides valuable insights to policymakers and practitioners regarding the details and benefits of using emerging technologies in modern urban governance frameworks. Based on the background and objectives of the study, The study will address the following research questions: What are the prevalent trends in the application of Artificial Intelligence (AI) and blockchain technologies in e-governance, particularly within smart city frameworks? How do AI and blockchain technologies complement each other in enhancing transparency, efficiency, and decision-making in the context of smart city governance? What are the significant challenges and opportunities associated with integrating AI and blockchain in e-governance, as identified in existing literature? How do ethical considerations and regulatory frameworks impact the adoption of AI and blockchain technologies in public administration? What key research gaps remain in exploring the combined application of AI and blockchain in e-governance, and how can future studies address these challenges to inform policy and practice? This research aims to deepen understanding of digital governance by addressing key questions and providing practical insights for policymakers and researchers [ 12 ]; [ 13 ]. Its significance lies in filling a critical gap in the existing literature by examining the interaction between AI and blockchain within the context of e-governance, specifically in smart cities. The findings of this study are expected to enhance global comprehension of digital governance and offer practical recommendations for applying AI and blockchain technologies in managing smart cities. Besides, by pinpointing the challenges and opportunities associated with integrating these technologies, the research aims to equip policymakers and urban planners with insights into the potential benefits and risks, thereby promoting knowledgeable decision-making in advancing smart cities. 2. Methodology This study used a systematic literature review (SLR) approach to identify, evaluate and synthesize existing research [ 14 ]; [ 15 ], on the integration of AI and blockchain in governance, with a specific focus on smart city policy implementation. The SLR was conducted following established guidelines to ensure a comprehensive and unbiased assessment of the literature c ( TITLE-ABS-KEY ( “Artificial Intelligence” ) OR TITLE-ABS-KEY ( “Blockchain” ) OR TITLE-ABS-KEY ( “E-Governance” ) OR TITLE-ABS-KEY ( “Smart City” ) ) AND PUBYEAR > 2018 AND PUBYEAR < 2025 AND ( LIMIT-TO ( SUBJAREA, “SOCI” ) AND ( LIMIT-TO ( DOCTYPE, ‘ar’ ) ) AND ( LIMIT-TO ( EXACTKEYWORD, ‘Smart City’ ) OR LIMIT-TO ( EXACTKEYWORD, “Blockchain” ) OR LIMIT-TO (EXACTKEYWORD, ‘Artificial Intelligence’ ) OR LIMIT-TO (EXACTKEYWORD, ‘Governance’ ) OR LIMIT-TO (EXACTKEYWORD, ‘Literature Review’ ) OR LIMIT-TO (EXACTKEYWORD, ‘Bibliometric Analysis’ ) AND (LIMIT-TO (LANGUAGE, ‘English’ ) AND (LIMIT-TO (SRCTYPE, ‘j’ ) AND (LIMIT-TO (OA, ‘all’ ) ).). In addition to the SLR, bibliometric analyses were conducted to provide a quantitative perspective on the research landscape [ 16 ]. This analysis included statistical assessments of publication trends, citation behavior, and collaboration networks [ 17 ]. The bibliometric analysis began with data extraction from several Scopus-indexed publishers, followed by metadata cleaning and standardization, including authorship, publication year, keywords, and total citations [ 18 ]. Descriptive analyses of publication metrics, including annual publication trends, leading journals, most cited articles, and geographic distribution, provided an overview of research growth and focus areas [ 19 ]. Keyword co-occurrence analysis was conducted to identify recurring themes and relationships, with network visualization tools such as VOSviewer, R-Studio, and Citespace used to map clusters of related topics such as ‘smart cities,’ ‘blockchain,’ and ‘AI in electronic governance.’ Citation analyses identified influential papers and authors and their impact using metrics such as total citations and key findings in recent research [ 20 ]; [ 21 ]; [ 19 ]. In contrast, the analysis of collaboration networks reveals key partnerships and interdisciplinary trends among authors and institutions [ 22 ]. Finally, thematic evolution analysis tracks the development of fundamental research areas, highlighting the shift from theoretical frameworks to practical applications in innovative urban governance [ 23 ]. These bibliometric insights complement the SLR by revealing the intellectual structure and dynamic trends in integrating AI and blockchain for governance. The methodological framework guiding this research process is depicted in Fig. 1 . 3. Results 3.1. Overview of AI in E-Governance AI has significantly influenced governance, demonstrating the potential to enhance decision-making, service delivery, and operational efficiency [ 24 ]. In smart cities, AI proves particularly advantageous because it can process vast amounts of data generated by sensors and other digital platforms. It provides real-time insights to inform policy choices and improve urban management [ 25 ]. For instance, AI can predict traffic patterns, optimize energy consumption, and bolster public safety through techniques like predictive policing and emergency response systems [ 26 ]. Moreover, AI is increasingly integral to public service delivery as governments implement AI-driven tools to enhance citizen interaction and optimize administrative processes [ 27 ]. AI-powered chatbots, for example, assist 24/7 access to government services, answer citizen inquiries, and manage routine requests, eventually lessening the burden on government employees and improving service efficiency [ 28 ]. Besides, AI's ability to detect patterns in large datasets allows for the early detection of issues such as fraud or inefficiency, promoting more proactive and effective governance [ 29 ]. However, the deployment of AI in e-governance brings several challenges. These challenges encompass concerns about data privacy, algorithmic bias, and the ethical implications of AI in public administration decision-making [ 30 ]. As AI becomes increasingly integrated into governance processes, it is essential to address these challenges to guarantee that AI-driven systems remain transparent, accountable, and aligned with public values [ 31 ]. 3.2. Overview of Blockchain in E-Governance Blockchain technology is acknowledged for its decentralized and unchangeable ledger, offering a solid solution to various issues faced by traditional governance systems, particularly in promoting transparency, security, and trust [ 32 ]. In smart cities, blockchain can create secure, tamper-resistant records of transactions, contracts, and public decisions, thus enhancing the integrity of governance procedures [ 33 ]. For example, blockchain can be applied in voting systems to ensure that election results are transparent and verifiable, reducing the risk of fraud and boosting public trust in the electoral process [ 34 ]. Besides, the potential of blockchain to simplify bureaucratic processes is significant [ 35 ]. Besides, blockchain's transparency can help fight corruption by making government transactions and decisions more accessible to the public, boosting accountability [ 36 ]. For instance, blockchain can assist in the secure sharing of information between different government agencies, improving coordination and collaboration throughout the public sector [ 37 ]. Moreover, the transparency provided by blockchain can help combat corruption by making government transactions and decisions more visible to the public, thereby increasing accountability [ 38 ]. However, the adoption of blockchain in e-governance does pose several challenges [ 39 ]. These challenges include the technical complexities of blockchain systems, needing significant infrastructure investments, and possible resistance from stakeholders who are used to traditional governance models [ 40 ]. Additionally, there are legal and regulatory considerations that must be addressed to ensure that blockchain applications in governance are compliant with existing laws and policies [ 41 ]. 3.3. Convergence of AI and Blockchain Integrating AI and blockchain in e-governance is essential to how governments manage and safeguard data, improve decision-making, and provide better public services [ 42 ]. The capabilities of AI's data analysis are greatly strengthened by blockchain's secure and transparent data management systems, creating a partnership that is particularly beneficial in smart cities, where real-time data and trust are essential [ 43 ]. For instance, AI can analyze data stored on a blockchain, delivering valuable insights into urban planning, traffic control, and public safety. In contrast, blockchain technology ensures that this data is secure and remains unchanged [ 44 ]. Besides, integrating AI and blockchain can enhance the transparency and accountability of decision-making procedures [ 45 ]. AI technologies can refine and bolster these processes, while blockchain offers an immutable record of decisions that stakeholders can verify, enhancing accountability [ 46 ]. This collaboration is particularly essential in public procurement, where transparency and efficiency are essential for thwarting corruption and ensuring equitable distribution of public resources [ 47 ]. However, the convergence of AI and blockchain also presents several challenges that must be addressed [ 48 ]. These include the technical difficulties of integrating these complex technologies, the potential for increased system complexity, and the need for new regulatory frameworks to accommodate the unique characteristics of AI and blockchain applications in governance [ 49 ]. Additionally, there are concerns about the ethical implications of using AI and blockchain in public administration, particularly regarding privacy, data ownership, and algorithmic transparency [ 50 ]. 3.4. Gap Analysis There is a growing interest in AI and blockchain; however, there is an essential gap in the research concerning their combined application in e-governance, particularly considering smart cities [ 51 ]. Most existing studies focus either on AI or blockchain separately, offering a limited understanding of how these two technologies can work together and the potential advantages of their integration [ 39 ]. This fragmented approach fails to fully represent the potential of AI and blockchain in transforming governance practices and enhancing public administration's efficiency, transparency, and responsiveness [ 52 ]. Moreover, there is a need for more empirical research that examines the practical implementation of AI and blockchain in governance, as most existing studies are conceptual or theoretical [ 53 ]. Addressing these gaps requires a more integrated approach that explores the combined application of AI and blockchain in real-world governance settings, particularly in smart cities where these technologies can have the most significant impact [ 54 ]. This study aims to provide such an approach, offering new insights that can guide future research and inform policy-making in the digital governance domain [ 55 ]. 3.5. Publication Trends and Most Citation Examining publication trends is critical for understanding the evolution of academic and scientific output over time [ 56 ]. By analyzing the annual increase and fluctuations in published documents, researchers can identify factors that influence scholarly activity, such as improvements in research methods, funding availability, or significant global events [ 57 ]. Understanding these trends enables institutions, publishers, and academics to plan how to share research in the future strategically [ 58 ]. Figure 2 shows the consistent rise in the number of documents published annually from 2019 to 2024. It started with 498 documents in 2019, displaying a steady upward trajectory that peaked at 1,985 documents in 2023. A significant increase occurred between 2021 and 2023, with the number of documents increasing from 1,182 to 1,985. However, the data for 2024 indicates a slight drop to 1,871 documents. This information suggests strong growth in academic output in recent years, though there may be emerging factors in 2024 that contribute to this slight decline. Such fluctuations could reflect external factors influencing publication trends, such as economic, political, or technological changes that deserve further investigation. Figure 2 shows the publication trend in 2019–2024, which shows a steadily increasing graph indicating researchers' interest in AI and blockchain in e-governance and smart cities. Table 1 displays the top 10 most impactful authors and their insights on global research collaboration, concentrating on technological advancements such as AI, blockchain, and Industry 5.0. [ 59 ] emphasize the groundbreaking potential of AI, comparing its effects to those of the Industrial Revolution. Similarly, [ 60 ] explore AI's function in decision-making systems and its partnership with Big Data. Research conducted by [ 61 ] looks into the influence of digital and social media on consumer behavior. Conversely, [ 62 ] advocates for a human-centered approach in Industry 5.0, stressing the value of human-robot collaboration. The capacity of blockchain to enhance transparency and security has been examined by [ 63 ] and [ 64 ] despite its widespread implementation challenges. [ 65 ] illustrate AI's efficiency in medical education, demonstrated by ChatGPT's performance on tests. Besides, [ 66 ] emphasize public concerns surrounding AI in healthcare, mainly due to issues of privacy and personalization. Together, these studies emphasize the critical role of emerging technologies in transforming industries, decision-making processes, and societal structures while emphasizing the need for further investigation into their integration and acceptance. Table 1 Top 10 most influential authors and findings on global research collaboration. No Author (Year) Title, Finding and DOI Total Citations TC per Year Normalized TC 1 (Dwivedi, Hughes, et al., 2021) [ 59 ] Title : Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy Finding : This research emphasizes the transformative potential of AI, comparing it to the Industrial Revolution. It highlights the ability of AI to replace or enhance human tasks and examines its impact on various sectors. The research also explores the opportunities, challenges, and ethical issues associated with AI. Doi : https://doi.org/10.1016/j.ijinfomgt.2019.08.002 1494 373.50 47.13 2 (Duan et al., 2019) [ 60 ] Title : Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda Finding : This research examines the resurgence of AI, driven by supercomputing and Big Data, and its role in decision-making systems. Through a literature review, it explores the challenges of integrating AI to support or replace human decision-makers. The study proposes twelve research propositions for IS researchers, focusing on AI-human interaction, conceptual development, and effective AI implementation in the Big Data era. Doi : https://doi.org/10.1016/j.ijinfomgt.2019.01.021 1289 214.83 25.60 3 (Dwivedi, Ismagilova, et al., 2021) [ 61 ] Title : Setting the future of digital and social media marketing research: Perspectives and research propositions Finding : This research explores the internet and social media's influence on consumer behavior and business practices, including benefits like reduced costs and increased sales, as well as challenges like negative online word-of-mouth. It gathers insights from experts on topics like AI and mobile marketing, identifies research limitations, and provides propositions for future study. https://doi.org/10.1016/j.ijinfomgt.2020.102168 901 225.25 28.42 4 (Bentéjac et al., 2021) [ 67 ] Title : A comparative analysis of gradient boosting algorithms Finding : The study compares XGBoost, LightGBM, and CatBoost with random forests and traditional gradient boosting. CatBoost achieved the best accuracy, LightGBM was the fastest but less accurate, and XGBoost ranked second in both speed and accuracy. The research also analyzed the effects of hyper-parameter tuning on the performance of these algorithms. Doi : https://doi.org/10.1007/s10462-020-09896-5 897 224.25 28.30 5 (Dutta et al., 2020) [ 64 ] Title : This paper reviews the integration of blockchain technology into supply chain operations, highlighting its potential for transforming functions, improving transparency, and enhancing security. The study examines blockchain applications in multiple industries and suggests a future research agenda for exploring its role in supply chain innovation. Doi : https://doi.org/10.1016/j.tre.2020.102067 826 165.20 20.07 6 (Nahavandi, 2019) [ 62 ] Title : Industry 5.0-a human-centric solution Finding : The article discusses Industry 5.0, where robots and humans collaborate through brain-machine interfaces and AI to boost productivity without replacing workers. It highlights key features, concerns for manufacturers, and research developments, arguing that Industry 5.0 will create jobs and promote economic growth by enhancing human-robot collaboration. Doi : https://doi.org/10.3390/su11164371 786 131.00 15.61 7 (Gilson et al., 2023) [ 65 ] Title : How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment Finding : A study found that ChatGPT performed well on medical exams, outperforming InstructGPT. It had decreasing accuracy with harder questions but consistently provided logical justifications for answers. This suggests ChatGPT could be a useful educational tool in the medical field. Doi: https://doi.org/10.2196/45312 770 385.00 75.61 8 (Longoni et al., 2019) [ 66 ] Title : Resistance to Medical Artificial Intelligence Finding : Consumers show reluctance to use and trust AI-based medical services in healthcare due to concerns about the lack of personalization. Resistance can be reduced by emphasizing personalization and supporting human decisions. Doi : https://doi.org/10.1093/jcr/ucz013 711 118.50 14.12 9 (Dwivedi et al., 2020) [ 68 ] Title : Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life Finding : The study explores organizational transformations during COVID-19 from an information systems and technology perspective, gathering insights from 12 experts on areas like AI, cybersecurity, digital strategy, and blockchain. It highlights how the pandemic reshaped these domains and offers key challenges and recommendations for navigating the crisis. Doi : https://doi.org/10.1016/j.ijinfomgt.2020.102211 686 137.20 16.67 10 (L. Hughes et al., 2019) [ 63 ] Title : Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda Finding : This study examines the impact and potential of blockchain technology within Information Systems (IS) and Information Management (IM). While commercial adoption remains limited, it highlights blockchain's promise across various industries and explores key research themes, potential applications, and future directions. The analysis addresses barriers to adoption and emphasizes blockchain's role in advancing the UN Sustainability Development Goals, positioning it as a transformative technology for established industries. Doi : https://doi.org/10.1016/j.ijinfomgt.2019.02.005 571 95.17 11.34 3.6 Keyword Evolution and Words' Frequency over Time Recently, there has been an increasing focus on investigating keyword evolution and emerging trends, particularly within data science, digital content analysis, and information retrieval [ 69 ]. As the online environment grows, it becomes more critical for researchers to understand how keywords change, which helps them monitor changes in academic discourse, technological advancements, and societal priorities [ 70 ]. Keywords emphasize critical research areas, novel technologies, and developing terminology across various academic fields [ 71 ] By exploring the evolution of keywords, researchers can acquire meaningful insights into the development of knowledge creation and identify emerging fields that may influence future research trajectories [ 72 ]. The Sankey diagram in Fig. 3 comprehensively visualises the interconnectedness of research topics and authorship in artificial intelligence (AI) and related areas. The diagram highlights vital thematic areas such as "artificial intelligence," "machine learning," "sustainability," and "smart city," demonstrating their central role in contemporary research discourse. The flow of connections suggests that AI is not only a standalone topic but also profoundly integrated with emerging fields like the "Internet of Things," "blockchain," and "deep learning," illustrating the interdisciplinary nature of these subjects. Additionally, the diagram indicates a strong alignment between specific research areas and individual contributors, with authors such as Wang Y, Zhang T, and Lee J showing a broad engagement across diverse topics. This integration of multiple thematic clusters underlines the growing trend of convergence in AI research, where advancements in one area often spur innovations in another, thereby fostering a holistic ecosystem of technological development and application. Such visual mapping aids in understanding the dynamics of collaborative research efforts and the evolution of critical thematic domains [ 73 ]. Figure 4 provides information on the The longitudinal analysis of word frequencies from 2019 to 2024 reveals an essential increase in research interest across various vital subjects. "Artificial intelligence" emerges as the leading topic, climbing from 210 mentions in 2019 to 3,028 in 2024. This trend emphasizes the significance of AI in academia and research, likely fueled by its groundbreaking potential across multiple sectors. Besides, there is noteworthy growth in related fields such as "smart city" and "machine learning." These terms signify a shift toward practical, technology-driven solutions to enhance urban environments and refine computational methods. What is more, other important terms like "sustainability," "decision making," and "algorithm" indicate a balanced emphasis on technological progress and the ethical and practical dimensions required for societal advancement. The increasing prevalence of terms like "human" and "sustainable development" emphasizes the growing recognition of human-centered and sustainable approaches in technological research. This trend reflects the developing priorities of academia, which are progressively directed towards blending advanced technologies with societal and environmental concerns. It points to a transition towards more comprehensive, interdisciplinary research agendas that address global challenges and necessitate sustainable innovation. 3.7. Co-authorship networks In academic research, collaboration plays an essential role in creating and disseminating scientific knowledge [ 74 ]. Co-authorship networks demonstrate the relationships between authors who cooperate on academic publications, providing a valuable and informative perspective on the structure and dynamics within scholarly communities [ 75 ]. These networks not only reveal collaboration patterns but also indicate intellectual influence, resource sharing, and the dissemination of innovation across various fields [ 76 ]. By analyzing co-authorship networks, researchers can evaluate the impact of interdisciplinary collaboration, assess the significance and connectivity of key contributors, and spot emerging trends in particular academic areas [ 77 ]. Studying these networks as the research environment evolves provides essential insights into how knowledge is constructed and shared through collective scholarly efforts. Figure 5 show The analysis of co-authorship networks reveals a diverse range of collaborative efforts among prominent researchers, highlighting significant contributions to the academic field. Leading the list is Yogesh K. Dwivedi, who has authored ten documents that garnered a substantial 2,805 citations, indicating both prolific output and high impact within the research community. Following him are John S. Edwards and Yanqing Duan, whose works, although fewer in number (3 and 2 documents, respectively), have also received considerable citations (2,021 and 1,975). This suggests that their publications have achieved notable recognition. The total link strength metric further underscores the collaborative nature of these researchers, with Dwivedi displaying the highest connectivity at 24, suggesting a broad network of academic partnerships. Overall, these findings emphasize the role of co-authorship as a critical driver of scholarly influence, fostering the dissemination of knowledge through collaborative networks. 3.8 Keyword co-occurrences and produktive authors In academic research, collaboration plays an essential role in creating and disseminating scientific knowledge [ 74 ]. Co-authorship networks demonstrate the relationships between authors who cooperate on academic publications, providing a valuable and informative perspective on the structure and dynamics within scholarly communities [ 75 ]. These networks not only reveal collaboration patterns but also indicate intellectual influence, resource sharing, and the dissemination of innovation across various fields [ 78 ]. By analyzing co-authorship networks, researchers can evaluate the impact of interdisciplinary collaboration, assess the significance and connectivity of key contributors, and spot emerging trends in particular academic areas [ 79 ]. Studying these networks as the research environment evolves provides essential insights into how knowledge is constructed and shared through collective scholarly efforts [ 80 ]. In academic research, collaboration plays an essential role in creating and disseminating scientific knowledge [ 74 ]. Figure 6 shows The co-occurrence network data provides insights into crucial research topics' centrality and relational importance within a specific academic discourse. "Artificial intelligence" stands out with the highest betweenness centrality (208.838), closeness (0.02), and PageRank (0.133), indicating its pivotal role as a connecting node across the network and its influence on other research themes. Topics such as "machine learning" and "decision making" also exhibit notable centrality measures, reflecting their relevance and integration within interdisciplinary studies. The clustering of these terms suggests a thematic focus on AI-related applications, with substantial interconnections in domains such as "algorithm," "China," and other emergent technological and societal contexts. This data underscores the prevalent academic interest in AI and its intersections with various fields, affirming its status as a cornerstone of contemporary research with extensive influence across adjacent disciplines. The high betweenness of specific nodes reflects the structural importance of these concepts in linking diverse research areas, suggesting a wealthy and interconnected knowledge landscape driven by technological innovation. The trend analysis of author productivity in scholarly publications reveals significant contributions from leading academics. The data identifies "Yigitcanlar, T." as the most prolific author, with 24 documented publications showcasing their prominent role in advancing research within their domain. Following closely are "Janssen, M." and "Mosavi, A." each with 17 contributions, further demonstrating their active engagement in academic discourse. "Dwivedi, Y.K." and "Sætra, H.S." also make notable contributions, with 12 and 11 publications, respectively, underscoring their influence in the literature. This distribution of productivity not only highlights individual academic excellence but reflects the collaborative and cumulative nature of knowledge generation. Future research may benefit from a more profound exploration of these high-output authors' thematic focus and interdisciplinary impact. 4. Discussion 4.1. Key Trends, Insights and Bibliometric Coupling Map Examining essential trends and insights in academic research offers a thorough comprehension of the changing environment of scholarly inquiry [ 81 ]. By pinpointing prominent themes, newly emerging areas of interest, and changes in methodological strategies, researchers can acquire helpful foresight into the future directions of investigations [ 82 ]. Key trends frequently mirror more considerable societal, technological, or theoretical changes, while the insights from these trends simplify the practical implications of research across different fields [ 83 ]. This examination also emphasizes patterns of collaboration, innovation spread, and interdisciplinary convergence, providing a refined understanding of how scientific knowledge is influenced by and influences its broader context [ 84 ]. Figure 8 explains that The treemap visualization displays the key topics and research directions in current scientific discourse, emphasizing "artificial intelligence" (23%) and its essential role across numerous fields. Related areas such as "smart city" (8%), "machine learning" (4%), and "sustainability" (4%) further illustrate the interdisciplinary use of AI in urban development and environmental preservation. Additional essential themes, including "decision making," "human factors," and "algorithm," indicate a focus on the convergence of technology and human-focused applications, emphasizing the necessity for research that harmonizes technological progress with social consequences. Emerging fields like "blockchain," "deep learning," and "COVID-19" display the discipline's flexibility in tackling real-world issues, especially in health and governance [ 64 ]; [ 85 ]; [ 68 ]. The variety of subjects reflects a solid commitment to advancing technology while considering ethical, sustainable, and human-focused strategies, pointing to a comprehensive academic interest in shaping future societal structures through innovation and responsible integration of technology [ 86 ]. This visualization represents existing academic priorities and indicates a balanced research environment that combines technical expertise with societal welfare, aligning with the broader goals of promoting knowledge for comprehensive, sustainable development. The bibliometric coupling map in Fig. 9 illustrates research clusters' thematic relationships and influence within the scientific domain. Prominent clusters are characterized by their centrality and impact, highlighting their relevance to the academic community. Notably, clusters associated with "smart city" and "blockchain" demonstrate high centrality (88.5% and 85.7%, respectively) and significant impact, signifying their pivotal roles in interdisciplinary research and application. Similarly, topics such as "sustainable development" and "climate change" achieve high impact scores (60% and 100%, respectively), reflecting their global relevance and alignment with contemporary societal challenges. In contrast, clusters related to "artificial intelligence" and "machine learning" exhibit varying levels of centrality and impact, underscoring the nuanced evolution of these fields in addressing both theoretical and practical challenges. The visualization clearly illustrates the interrelation of these themes, providing a thorough view of the changing nature of scientific research. Upcoming studies could investigate the cooperation between these clusters to assess their collaborative possibilities and emerging trends. 4.2. Global Research Contributions The results outlined in this study greatly enhance the worldwide understanding of e-governance by clarifying the complex relationship between emerging technologies and governance systems. As digital transformation influences the public sector, incorporating technologies like AI and blockchain is essential for improving transparency, accountability, and citizen involvement in governance practices [ 87 ]. This research adds to the conversation on how innovative digital tools can improve governance frameworks, thus tackling current issues in public administration [ 88 ]. Besides, this study's findings align with global movements promoting data-driven decision-making and the necessity for flexible regulatory frameworks, which are essential for building trust and inclusivity in e-governance initiatives [ 89 ]. By investigating the consequences of these technological advancements, this research not only enhances academic knowledge but also acts as a valuable resource for policymakers aiming to improve the effectiveness of e-governance strategies around the globe. Figure 10 shows the global distribution of research on e-participation and cybersecurity, demonstrating a significant concentration of productivity among the top 10 contributing countries, reflecting their critical role in advancing these crucial areas. The United States has 1,146 publications, demonstrating its strong commitment to digital governance and cybersecurity frameworks. The United Kingdom followed closely with 1,062 documents, highlighting vital research initiatives and policy-driven studies in e-participation. With 822 contributions, China demonstrated its strategic focus on integrating cybersecurity into its vast digital infrastructure. Germany and Australia, which contributed 499 and 481 documents, respectively, further reinforced the importance of developed countries in driving innovation and knowledge in this domain. Other key contributors, including India, Canada, South Korea, Japan, and Italy, collectively underscore the global interconnectedness and shared responsibility in addressing challenges and opportunities in e-participation and cybersecurity. This distribution also underscores gaps in research outcomes, which invite further exploration of how regional capacities and international collaboration influence the dissemination of knowledge and best practices in this area. 4.3. Synergies and Challenges The combination of AI and blockchain technology in governance presents exciting opportunities to enhance the efficiency and transparency of public administration. AI's strengths in data analysis and predictive modeling work well with blockchain's secure and transparent ledger, promoting knowledgeable decision-making and building trust among stakeholders [ 90 ]. By using AI to examine data stored on blockchain networks, governance organizations can gain insights for policy formulation and resource distribution. For example, AI's capability to identify trends and anomalies in real time supports fast responses to issues, improving governance flexibility [ 91 ]. Also, AI can automate routine tasks such as compliance checks and reporting, which decreases administrative burdens and simplifies processes [ 92 ]. However, despite these benefits, deploying AI and blockchain in governance encounters essential obstacles. The technical details of these technologies require expertise often lacking in public sector organizations, a situation worsened by the swift evolution of technology that creates skill disparities [ 93 ]. Regulatory and ethical challenges hinder adoption, as current frameworks may fall short in addressing the unique issues posed by these technologies, potentially hampering innovation [ 94 ]. Ethical dilemmas, especially those concerning data privacy and security, emerge since blockchain's transparency could reveal sensitive information, necessitating robust data protection protocols [ 95 ]. It is essential to develop adaptive regulatory frameworks and targeted capacity-building programs to overcome these challenges. Future studies should focus on comprehensive approaches to responsibly integrating AI and blockchain, ensuring innovation aligns with public welfare and ethical principles. 4.4. Ethical and Practical Considerations The application of Artificial Intelligence (AI) and blockchain technology in governance raises critical ethical and practical challenges that require careful consideration [ 96 ]. One major ethical concern is the risk of bias embedded within AI algorithms, which often reflects historical inequalities, potentially perpetuating discrimination in public decision-making processes [ 97 ]. Such biases undermine principles of fairness and justice, particularly for marginalized groups [ 98 ]. To address this, governance frameworks must adopt robust auditing mechanisms to identify and mitigate algorithmic bias, ensuring equitable outcomes. Blockchain technology, while enhancing transparency and accountability, introduces data privacy and security challenges [ 99 ]. The immutable and transparent nature of blockchain can expose sensitive information, necessitating comprehensive data governance frameworks to balance transparency with individual privacy rights [ 100 ]. From a practical perspective, the successful integration of AI and blockchain in governance is hindered by technical expertise gaps and resource constraints [ 101 ]. Public sector organizations often lack the necessary skills and financial resources to implement and maintain these advanced technologies [ 102 ]. Furthermore, the rapidly evolving regulatory landscape struggles to keep pace with technological advancements, creating misalignments that stifle innovation [ 94 ]. Policymakers must develop adaptive regulatory frameworks that safeguard public interests while promoting ethical considerations [ 94 ]. Collaborative efforts among technologists, ethicists, and regulators are crucial to addressing these challenges and ensuring that the transformative potential of AI and blockchain enhances transparency, accountability, and equity in governance systems [ 103 ]. 4.5. Policy Implications As urban development speeds up, AI and blockchain technology offer revolutionary opportunities for improving governance in smart cities [ 104 ]. AI allows policymakers to examine extensive urban datasets, including information from transportation, energy, and public services, to reveal patterns and forecast trends, thereby optimizing systems like traffic management and bolstering public safety [ 105 ]. This data-driven strategy promotes responsive governance that aligns with the changing needs of urban populations [ 106 ]. Simultaneously, blockchain's decentralized and unchangeable ledger system boosts transparency and trust by securely monitoring resource distribution and ensuring accountability in government operation [ 107 ]. For example, blockchain-based procurement systems can help reduce corruption and enhance public trust, with pilot projects displaying their ability to simplify bureaucratic processes [ 108 ]. In addition to efficiency and transparency, combining AI and blockchain can improve citizen engagement and participation in governance [ 109 ]. AI-driven platforms assist in real-time public consultations, guaranteeing that governance aligns with community needs [ 110 ]. Blockchain enables citizens by granting secure access to their data and encouraging active involvement in decision-making [ 111 ]. Nonetheless, integrating these technologies necessitates tackling ethical and regulatory challenges, particularly concerning data privacy and security in smart cities [ 112 ]. Policymakers must develop comprehensive data governance frameworks and adaptable regulatory environments to protect public interests while promoting innovation [ 113 ]. By strategically using these technologies, policymakers can build efficient, accountable, and comprehensive governance systems that respond to the changing needs of urban populations [ 114 ]. 4.6. Future Research Directions Integrating Artificial Intelligence (AI) and blockchain technology in governance presents numerous opportunities for future research to address existing challenges and maximize their potential [ 96 ]; [ 115 ]. A key area is the development of ethical frameworks that mitigate biases in AI algorithms and address privacy concerns associated with blockchain's transparency [ 116 ]; [ 117 ]. Researchers can use case studies and empirical analyses to evaluate the effectiveness of these frameworks in ensuring fairness and accountability. Additionally, the interoperability of AI and blockchain systems within governance frameworks requires further investigation [ 115 ]. As intelligent cities adopt various digital technologies, understanding the technical and organizational challenges of integrating these systems is essential to creating cohesive and efficient governance solutions [ 118 ]. Collaborative studies involving policymakers, developers, and urban planners can yield insights into best practices for achieving effective integration [ 119 ]; [ 120 ]. Data governance and regulatory frameworks also represent critical areas for exploration [ 121 ]. Research should examine how data quality, ownership, and access impact the effectiveness of AI and blockchain in governance while also considering implications for public trust and transparency [ 122 ]; [ 123 ]. Comparative studies on regulatory approaches across jurisdictions can assess their influence on innovation, public trust, and ethical use, offering valuable guidance for policymakers [ 124 ]; [ 125 ]. Finally, the socio-economic implications of these technologies warrant attention, particularly their potential to address inequality and inclusivity in governance, especially in developing regions [ 126 ]. Investigating strategies to make digital governance accessible and affordable can ensure these benefits extend to all citizens, fostering equitable and effective governance solutions [ 127 ]. 5. Conclusion This study thoroughly examines the combination of AI and blockchain technology in e-governance, especially considering innovative city frameworks. The results indicate that AI's ability to perform real-time data analysis, predictive modeling, and improved decision-making is greatly enhanced by blockchain's features that provide transparency, data security, and trust through its decentralized and unalterable framework. These technologies present a synergistic opportunity to change public administration by enhancing efficiency, accountability, and public trust in governance systems. The bibliometric analysis carried out in this study emphasizes the swift increase in research interest in this domain, charting key trends, essential studies, and ongoing gaps, particularly the shortage of empirical assessments on their integrated application. In promoting global research, this study offers several significant contributions. It fills the gap between theoretical discussion and practical application by systematically reviewing the existing state of AI and blockchain integration in e-governance. The work explains the complementary aspects of these technologies, delivering actionable insights for researchers and policymakers to develop frameworks that harmonize technological advancements with governance requirements. Besides, it enhances the academic conversation surrounding technology-driven governance, stressing the necessity to tackle scalability, interoperability, and inclusivity issues. This research sets the stage for future investigations. It is an essential resource for policymakers aiming to deploy AI and blockchain solutions that improve public service delivery while ensuring ethical and sustainable results. Despite its essential contributions, the study recognizes some limitations. Firstly, the research is primarily based on bibliometric analysis, which, although practical, does not offer the contextual depth that field-based empirical studies can provide. Secondly, focusing on intelligent cities leaves space for exploring these technologies in broader governance contexts, such as rural administration or international governance networks. To address these gaps, upcoming research should focus on longitudinal case studies and pilot projects evaluating the practical implementation of AI and blockchain in different e-governance settings. Besides, regulatory and ethical issues, including worries about data privacy and the ethical use of AI, need continuous attention from academics and policymakers to guarantee that these technologies are used responsibly. In summary, the combined potential of AI and blockchain technologies signifies a revolutionary shift in governance, presenting unique opportunities to enhance decision-making, transparency, and citizen involvement. However, achieving this potential requires ongoing interdisciplinary research, robust policy frameworks, and ethical considerations to navigate the complexities of incorporating these technologies into governance systems. By confronting these challenges, AI and blockchain can become foundational technologies for establishing more innovative, more comprehensive, and more resilient governance frameworks in the digital age. Declarations Acknowledgements Not applicable. Author contributions Sandi Lubis contributed to bibliometric data analysis and writing the manuscript. Achmad Nurmandi integrated AI and blockchain in smart city governance. Jamaluddin Ahmad discussed the practical implications of these technologies. Eko Priyo Purnomo focused on strategies for implementing new technologies in government. Titin Purwaningsih explored the social and policy aspects of AI and blockchain. Hazel D. Jovita-Olvez provided global insights through comparative analysis and case studies. Funding The author(s) received no financial support for this article's research, authorship, and publication. Data availability All data associated with this article is available upon request from the author. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The author(s) affirm that there are no conflicts of interest concerning this article's research, authorship, or publication. All efforts have been made to ensure the integrity and impartiality of the study, with no financial, professional, or personal relationships influencing the content or conclusions presented References Clavin, J., et al.: Blockchains for government: Use cases and challenges. Digit. Gov. Res. Pract. 1 (3) (2020). 10.1145/3427097 Peng, S., Zhou, L., He, X., Du, J.: GPS-aided inter-microcell interference avoidance for request-transmission splitting slotted ALOHA-based scheme in smart cities with connected vehicles, Futur. Gener. Comput. 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Gov. 12 (1), 1–24 (2020). 10.29379/jedem.v12i1.604 Dhali, M., Hassan, S., Mehar, S.M., Shahzad, K., Zaman, F.: Cryptocurrency in the Darknet: sustainability of the current national legislation. Int. J. Law Manag. 65 (3), 261–282 (Jan. 2023). 10.1108/IJLMA-09-2022-0206 Vrabie, A., Ianole-Călin, R.: A comparative analysis of municipal public innovation: Evidence from Romania and United States. J. Open. Innov. Technol. Mark. Complex. 6 (4), 1–21 (2020). 10.3390/joitmc6040112 Patnaik, J., Bhowmick, B.: Revisiting appropriate technology with changing socio-technical landscape in emerging countries. Technol. Soc. 57 , 8–19 (2019). https://doi.org/10.1016/j.techsoc.2018.11.004 Trevisan, F., Cogburn, D.L.: Technology and accessibility in global governance and human rights: the experience of disability rights advocates. J. Inform. Commun. Ethics Soc. 18 (3), 377–391 (Jan. 2020). 10.1108/JICES-02-2020-0016 Additional Declarations No competing interests reported. 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Centrality of Key Research Themes in AI and Blockchain for E-Governance.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5694080/v1/9a5b175627a1e6312e54c692.png"},{"id":74073603,"identity":"4991b954-cb2e-4ae2-a5e0-9a32940df06b","added_by":"auto","created_at":"2025-01-17 13:21:13","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":314068,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 Global Contributions to Research on AI , Blockchain, E-governance and Smart City.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-5694080/v1/d35428ecd23333c522e45f79.png"},{"id":74075383,"identity":"f80dff72-3b6a-4e64-bd9c-387fb47f992c","added_by":"auto","created_at":"2025-01-17 13:37:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3408757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5694080/v1/0f967ced-8fe3-4a70-8a52-690d25c9d54a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI and Blockchain For Next-Generation E- Governance: A Comprehensive Bibliometric Review In Smart City Innovation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe field of digital governance is developing rapidly, and the integration of AI and blockchain technologies is increasingly seen as a groundbreaking way to improve the efficiency, transparency, and responsiveness of governance systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These technologies are especially relevant in smart cities, which use advanced digital tools to optimize urban management and enhance the quality of life for residents [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. AI allows smart cities to process extensive data in real-time, providing predictive insights that inform policy decisions and resource distribution; meanwhile, blockchain offers a secure and unchangeable record of transactions and decisions, encouraging public administration transparency and trust [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite the growing use of AI and blockchain across various sectors, their potential in e-governance, particularly in smart city frameworks, still needs to be fully explored [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While AI is commonly used for its data analysis and processing abilities, the decentralized nature of blockchain presents distinct benefits for ensuring data integrity and security, especially in contexts where transparency and accountability are essential [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The combination of these technologies has significant potential for transforming governance. However, their joint application in smart city projects has yet to be thoroughly investigated, emphasizing a gap in the existing literature and a missed chance to devise more integrated and effective governance strategies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis research tackles a vital issue the need for more thorough studies on the joint application of AI and blockchain in policy enforcement within smart cities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While a significant body of literature addresses AI and blockchain separately, few investigations assess their synergistic potential for enhancing e-governance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, most current studies focus on theoretical or conceptual frameworks, with a scarcity of empirical research exploring the real-world challenges and opportunities of merging these technologies in governance contexts [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This study seeks to close this gap by providing a systematic review and bibliometric analysis of the literature regarding integrating AI and blockchain in e-governance, specifically emphasizing their role in smart city policy enforcement. The research aims to determine existing trends, challenges, and opportunities in this emerging field, offering a comprehensive understanding that can inform future studies and aid in policy development efforts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study seeks to address the research issue by achieving several key objectives. Its main aim is to identify the current trends in the application of AI and blockchain technologies within e-governance, mainly focusing on their roles in developing and managing smart cities. By examining these trends, the study aims to simplify the revolutionary impacts of these technologies on urban governance systems. Besides, this research intends to explore the synergies between AI and blockchain, emphasizing how combining these technologies can improve governance processes. This investigation will emphasize the potential for enhanced public administration efficiency, transparency, and security. Finally, the study evaluates the challenges and opportunities associated with implementing AI and blockchain in smart city governance, striving to offer a comprehensive view of the practical implications of their integration. This research provides valuable insights to policymakers and practitioners regarding the details and benefits of using emerging technologies in modern urban governance frameworks.\u003c/p\u003e \u003cp\u003eBased on the background and objectives of the study, The study will address the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat are the prevalent trends in the application of Artificial Intelligence (AI) and blockchain technologies in e-governance, particularly within smart city frameworks?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do AI and blockchain technologies complement each other in enhancing transparency, efficiency, and decision-making in the context of smart city governance?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat are the significant challenges and opportunities associated with integrating AI and blockchain in e-governance, as identified in existing literature?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do ethical considerations and regulatory frameworks impact the adoption of AI and blockchain technologies in public administration?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat key research gaps remain in exploring the combined application of AI and blockchain in e-governance, and how can future studies address these challenges to inform policy and practice?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis research aims to deepen understanding of digital governance by addressing key questions and providing practical insights for policymakers and researchers [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Its significance lies in filling a critical gap in the existing literature by examining the interaction between AI and blockchain within the context of e-governance, specifically in smart cities. The findings of this study are expected to enhance global comprehension of digital governance and offer practical recommendations for applying AI and blockchain technologies in managing smart cities. Besides, by pinpointing the challenges and opportunities associated with integrating these technologies, the research aims to equip policymakers and urban planners with insights into the potential benefits and risks, thereby promoting knowledgeable decision-making in advancing smart cities.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eThis study used a systematic literature review (SLR) approach to identify, evaluate and synthesize existing research [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], on the integration of AI and blockchain in governance, with a specific focus on smart city policy implementation. The SLR was conducted following established guidelines to ensure a comprehensive and unbiased assessment of the literature c ( TITLE-ABS-KEY ( \u0026ldquo;Artificial Intelligence\u0026rdquo; ) OR TITLE-ABS-KEY ( \u0026ldquo;Blockchain\u0026rdquo; ) OR TITLE-ABS-KEY ( \u0026ldquo;E-Governance\u0026rdquo; ) OR TITLE-ABS-KEY ( \u0026ldquo;Smart City\u0026rdquo; ) ) AND PUBYEAR\u0026thinsp;\u0026gt;\u0026thinsp;2018 AND PUBYEAR\u0026thinsp;\u0026lt;\u0026thinsp;2025 AND ( LIMIT-TO ( SUBJAREA, \u0026ldquo;SOCI\u0026rdquo; ) AND ( LIMIT-TO ( DOCTYPE, \u0026lsquo;ar\u0026rsquo; ) ) AND ( LIMIT-TO ( EXACTKEYWORD, \u0026lsquo;Smart City\u0026rsquo; ) OR LIMIT-TO ( EXACTKEYWORD, \u0026ldquo;Blockchain\u0026rdquo; ) OR LIMIT-TO (EXACTKEYWORD, \u0026lsquo;Artificial Intelligence\u0026rsquo; ) OR LIMIT-TO (EXACTKEYWORD, \u0026lsquo;Governance\u0026rsquo; ) OR LIMIT-TO (EXACTKEYWORD, \u0026lsquo;Literature Review\u0026rsquo; ) OR LIMIT-TO (EXACTKEYWORD, \u0026lsquo;Bibliometric Analysis\u0026rsquo; ) AND (LIMIT-TO (LANGUAGE, \u0026lsquo;English\u0026rsquo; ) AND (LIMIT-TO (SRCTYPE, \u0026lsquo;j\u0026rsquo; ) AND (LIMIT-TO (OA, \u0026lsquo;all\u0026rsquo; ) ).).\u003c/p\u003e \u003cp\u003eIn addition to the SLR, bibliometric analyses were conducted to provide a quantitative perspective on the research landscape [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This analysis included statistical assessments of publication trends, citation behavior, and collaboration networks [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The bibliometric analysis began with data extraction from several Scopus-indexed publishers, followed by metadata cleaning and standardization, including authorship, publication year, keywords, and total citations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Descriptive analyses of publication metrics, including annual publication trends, leading journals, most cited articles, and geographic distribution, provided an overview of research growth and focus areas [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Keyword co-occurrence analysis was conducted to identify recurring themes and relationships, with network visualization tools such as VOSviewer, R-Studio, and Citespace used to map clusters of related topics such as \u0026lsquo;smart cities,\u0026rsquo; \u0026lsquo;blockchain,\u0026rsquo; and \u0026lsquo;AI in electronic governance.\u0026rsquo; Citation analyses identified influential papers and authors and their impact using metrics such as total citations and key findings in recent research [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]; [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In contrast, the analysis of collaboration networks reveals key partnerships and interdisciplinary trends among authors and institutions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Finally, thematic evolution analysis tracks the development of fundamental research areas, highlighting the shift from theoretical frameworks to practical applications in innovative urban governance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These bibliometric insights complement the SLR by revealing the intellectual structure and dynamic trends in integrating AI and blockchain for governance. The methodological framework guiding this research process is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Overview of AI in E-Governance\u003c/h2\u003e \u003cp\u003eAI has significantly influenced governance, demonstrating the potential to enhance decision-making, service delivery, and operational efficiency [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In smart cities, AI proves particularly advantageous because it can process vast amounts of data generated by sensors and other digital platforms. It provides real-time insights to inform policy choices and improve urban management [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. For instance, AI can predict traffic patterns, optimize energy consumption, and bolster public safety through techniques like predictive policing and emergency response systems [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, AI is increasingly integral to public service delivery as governments implement AI-driven tools to enhance citizen interaction and optimize administrative processes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. AI-powered chatbots, for example, assist 24/7 access to government services, answer citizen inquiries, and manage routine requests, eventually lessening the burden on government employees and improving service efficiency [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Besides, AI's ability to detect patterns in large datasets allows for the early detection of issues such as fraud or inefficiency, promoting more proactive and effective governance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the deployment of AI in e-governance brings several challenges. These challenges encompass concerns about data privacy, algorithmic bias, and the ethical implications of AI in public administration decision-making [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. As AI becomes increasingly integrated into governance processes, it is essential to address these challenges to guarantee that AI-driven systems remain transparent, accountable, and aligned with public values [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Overview of Blockchain in E-Governance\u003c/h2\u003e \u003cp\u003eBlockchain technology is acknowledged for its decentralized and unchangeable ledger, offering a solid solution to various issues faced by traditional governance systems, particularly in promoting transparency, security, and trust [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In smart cities, blockchain can create secure, tamper-resistant records of transactions, contracts, and public decisions, thus enhancing the integrity of governance procedures [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. For example, blockchain can be applied in voting systems to ensure that election results are transparent and verifiable, reducing the risk of fraud and boosting public trust in the electoral process [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBesides, the potential of blockchain to simplify bureaucratic processes is significant [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Besides, blockchain's transparency can help fight corruption by making government transactions and decisions more accessible to the public, boosting accountability [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. For instance, blockchain can assist in the secure sharing of information between different government agencies, improving coordination and collaboration throughout the public sector [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, the transparency provided by blockchain can help combat corruption by making government transactions and decisions more visible to the public, thereby increasing accountability [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the adoption of blockchain in e-governance does pose several challenges [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These challenges include the technical complexities of blockchain systems, needing significant infrastructure investments, and possible resistance from stakeholders who are used to traditional governance models [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, there are legal and regulatory considerations that must be addressed to ensure that blockchain applications in governance are compliant with existing laws and policies [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Convergence of AI and Blockchain\u003c/h2\u003e \u003cp\u003eIntegrating AI and blockchain in e-governance is essential to how governments manage and safeguard data, improve decision-making, and provide better public services [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The capabilities of AI's data analysis are greatly strengthened by blockchain's secure and transparent data management systems, creating a partnership that is particularly beneficial in smart cities, where real-time data and trust are essential [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. For instance, AI can analyze data stored on a blockchain, delivering valuable insights into urban planning, traffic control, and public safety. In contrast, blockchain technology ensures that this data is secure and remains unchanged [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBesides, integrating AI and blockchain can enhance the transparency and accountability of decision-making procedures [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. AI technologies can refine and bolster these processes, while blockchain offers an immutable record of decisions that stakeholders can verify, enhancing accountability [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This collaboration is particularly essential in public procurement, where transparency and efficiency are essential for thwarting corruption and ensuring equitable distribution of public resources [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the convergence of AI and blockchain also presents several challenges that must be addressed [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. These include the technical difficulties of integrating these complex technologies, the potential for increased system complexity, and the need for new regulatory frameworks to accommodate the unique characteristics of AI and blockchain applications in governance [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Additionally, there are concerns about the ethical implications of using AI and blockchain in public administration, particularly regarding privacy, data ownership, and algorithmic transparency [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Gap Analysis\u003c/h2\u003e \u003cp\u003eThere is a growing interest in AI and blockchain; however, there is an essential gap in the research concerning their combined application in e-governance, particularly considering smart cities [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Most existing studies focus either on AI or blockchain separately, offering a limited understanding of how these two technologies can work together and the potential advantages of their integration [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This fragmented approach fails to fully represent the potential of AI and blockchain in transforming governance practices and enhancing public administration's efficiency, transparency, and responsiveness [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, there is a need for more empirical research that examines the practical implementation of AI and blockchain in governance, as most existing studies are conceptual or theoretical [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Addressing these gaps requires a more integrated approach that explores the combined application of AI and blockchain in real-world governance settings, particularly in smart cities where these technologies can have the most significant impact [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. This study aims to provide such an approach, offering new insights that can guide future research and inform policy-making in the digital governance domain [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Publication Trends and Most Citation\u003c/h2\u003e \u003cp\u003eExamining publication trends is critical for understanding the evolution of academic and scientific output over time [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. By analyzing the annual increase and fluctuations in published documents, researchers can identify factors that influence scholarly activity, such as improvements in research methods, funding availability, or significant global events [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Understanding these trends enables institutions, publishers, and academics to plan how to share research in the future strategically [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the consistent rise in the number of documents published annually from 2019 to 2024. It started with 498 documents in 2019, displaying a steady upward trajectory that peaked at 1,985 documents in 2023. A significant increase occurred between 2021 and 2023, with the number of documents increasing from 1,182 to 1,985. However, the data for 2024 indicates a slight drop to 1,871 documents. This information suggests strong growth in academic output in recent years, though there may be emerging factors in 2024 that contribute to this slight decline. Such fluctuations could reflect external factors influencing publication trends, such as economic, political, or technological changes that deserve further investigation. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the publication trend in 2019\u0026ndash;2024, which shows a steadily increasing graph indicating researchers' interest in AI and blockchain in e-governance and smart cities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays the top 10 most impactful authors and their insights on global research collaboration, concentrating on technological advancements such as AI, blockchain, and Industry 5.0. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] emphasize the groundbreaking potential of AI, comparing its effects to those of the Industrial Revolution. Similarly, [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] explore AI's function in decision-making systems and its partnership with Big Data. Research conducted by [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] looks into the influence of digital and social media on consumer behavior. Conversely, [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] advocates for a human-centered approach in Industry 5.0, stressing the value of human-robot collaboration. The capacity of blockchain to enhance transparency and security has been examined by [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] despite its widespread implementation challenges. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] illustrate AI's efficiency in medical education, demonstrated by ChatGPT's performance on tests. Besides, [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] emphasize public concerns surrounding AI in healthcare, mainly due to issues of privacy and personalization. Together, these studies emphasize the critical role of emerging technologies in transforming industries, decision-making processes, and societal structures while emphasizing the need for further investigation into their integration and acceptance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 most influential authors and findings on global research collaboration.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor (Year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTitle, Finding and DOI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Citations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTC per Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNormalized TC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Dwivedi, Hughes, et al., 2021) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: This research emphasizes the transformative potential of AI, comparing it to the Industrial Revolution. It highlights the ability of AI to replace or enhance human tasks and examines its impact on various sectors. The research also explores the opportunities, challenges, and ethical issues associated with AI.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDoi\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2019.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2019.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e373.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Duan et al., 2019) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: Artificial intelligence for decision making in the era of Big Data \u0026ndash; evolution, challenges and research agenda\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: This research examines the resurgence of AI, driven by supercomputing and Big Data, and its role in decision-making systems. Through a literature review, it explores the challenges of integrating AI to support or replace human decision-makers. The study proposes twelve research propositions for IS researchers, focusing on AI-human interaction, conceptual development, and effective AI implementation in the Big Data era.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDoi\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2019.01.021\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2019.01.021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e214.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Dwivedi, Ismagilova, et al., 2021) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: Setting the future of digital and social media marketing research: Perspectives and research propositions\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: This research explores the internet and social media's influence on consumer behavior and business practices, including benefits like reduced costs and increased sales, as well as challenges like negative online word-of-mouth. It gathers insights from experts on topics like AI and mobile marketing, identifies research limitations, and provides propositions for future study.\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2020.102168\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2020.102168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e225.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Bent\u0026eacute;jac et al., 2021) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: A comparative analysis of gradient boosting algorithms\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: The study compares XGBoost, LightGBM, and CatBoost with random forests and traditional gradient boosting. CatBoost achieved the best accuracy, LightGBM was the fastest but less accurate, and XGBoost ranked second in both speed and accuracy. The research also analyzed the effects of hyper-parameter tuning on the performance of these algorithms.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDoi\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10462-020-09896-5\u003c/span\u003e\u003cspan address=\"10.1007/s10462-020-09896-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e224.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Dutta et al., 2020) [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: This paper reviews the integration of blockchain technology into supply chain operations, highlighting its potential for transforming functions, improving transparency, and enhancing security. The study examines blockchain applications in multiple industries and suggests a future research agenda for exploring its role in supply chain innovation.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDoi\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tre.2020.102067\u003c/span\u003e\u003cspan address=\"10.1016/j.tre.2020.102067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e165.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Nahavandi, 2019) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: Industry 5.0-a human-centric solution\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: The article discusses Industry 5.0, where robots and humans collaborate through brain-machine interfaces and AI to boost productivity without replacing workers. It highlights key features, concerns for manufacturers, and research developments, arguing that Industry 5.0 will create jobs and promote economic growth by enhancing human-robot collaboration.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDoi\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su11164371\u003c/span\u003e\u003cspan address=\"10.3390/su11164371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e131.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Gilson et al., 2023) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: A study found that ChatGPT performed well on medical exams, outperforming InstructGPT. It had decreasing accuracy with harder questions but consistently provided logical justifications for answers. This suggests ChatGPT could be a useful educational tool in the medical field.\u003c/p\u003e \u003cp\u003eDoi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/45312\u003c/span\u003e\u003cspan address=\"10.2196/45312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e385.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Longoni et al., 2019) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: Resistance to Medical Artificial Intelligence\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: Consumers show reluctance to use and trust AI-based medical services in healthcare due to concerns about the lack of personalization. Resistance can be reduced by emphasizing personalization and supporting human decisions.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDoi\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jcr/ucz013\u003c/span\u003e\u003cspan address=\"10.1093/jcr/ucz013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e118.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Dwivedi et al., 2020) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: The study explores organizational transformations during COVID-19 from an information systems and technology perspective, gathering insights from 12 experts on areas like AI, cybersecurity, digital strategy, and blockchain. It highlights how the pandemic reshaped these domains and offers key challenges and recommendations for navigating the crisis.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDoi\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2020.102211\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2020.102211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e137.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(L. Hughes et al., 2019) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTitle\u003c/b\u003e: Blockchain research, practice and policy: Applications, benefits, limitations, emerging research themes and research agenda\u003c/p\u003e \u003cp\u003e\u003cb\u003eFinding\u003c/b\u003e: This study examines the impact and potential of blockchain technology within Information Systems (IS) and Information Management (IM). While commercial adoption remains limited, it highlights blockchain's promise across various industries and explores key research themes, potential applications, and future directions. The analysis addresses barriers to adoption and emphasizes blockchain's role in advancing the UN Sustainability Development Goals, positioning it as a transformative technology for established industries.\u003c/p\u003e \u003cp\u003e\u003cb\u003eDoi\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijinfomgt.2019.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2019.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.6 Keyword Evolution and Words' Frequency over Time\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRecently, there has been an increasing focus on investigating keyword evolution and emerging trends, particularly within data science, digital content analysis, and information retrieval [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. As the online environment grows, it becomes more critical for researchers to understand how keywords change, which helps them monitor changes in academic discourse, technological advancements, and societal priorities [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Keywords emphasize critical research areas, novel technologies, and developing terminology across various academic fields [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] By exploring the evolution of keywords, researchers can acquire meaningful insights into the development of knowledge creation and identify emerging fields that may influence future research trajectories [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Sankey diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e comprehensively visualises the interconnectedness of research topics and authorship in artificial intelligence (AI) and related areas. The diagram highlights vital thematic areas such as \"artificial intelligence,\" \"machine learning,\" \"sustainability,\" and \"smart city,\" demonstrating their central role in contemporary research discourse. The flow of connections suggests that AI is not only a standalone topic but also profoundly integrated with emerging fields like the \"Internet of Things,\" \"blockchain,\" and \"deep learning,\" illustrating the interdisciplinary nature of these subjects. Additionally, the diagram indicates a strong alignment between specific research areas and individual contributors, with authors such as Wang Y, Zhang T, and Lee J showing a broad engagement across diverse topics. This integration of multiple thematic clusters underlines the growing trend of convergence in AI research, where advancements in one area often spur innovations in another, thereby fostering a holistic ecosystem of technological development and application. Such visual mapping aids in understanding the dynamics of collaborative research efforts and the evolution of critical thematic domains [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides information on the The longitudinal analysis of word frequencies from 2019 to 2024 reveals an essential increase in research interest across various vital subjects. \"Artificial intelligence\" emerges as the leading topic, climbing from 210 mentions in 2019 to 3,028 in 2024. This trend emphasizes the significance of AI in academia and research, likely fueled by its groundbreaking potential across multiple sectors. Besides, there is noteworthy growth in related fields such as \"smart city\" and \"machine learning.\" These terms signify a shift toward practical, technology-driven solutions to enhance urban environments and refine computational methods. What is more, other important terms like \"sustainability,\" \"decision making,\" and \"algorithm\" indicate a balanced emphasis on technological progress and the ethical and practical dimensions required for societal advancement. The increasing prevalence of terms like \"human\" and \"sustainable development\" emphasizes the growing recognition of human-centered and sustainable approaches in technological research. This trend reflects the developing priorities of academia, which are progressively directed towards blending advanced technologies with societal and environmental concerns. It points to a transition towards more comprehensive, interdisciplinary research agendas that address global challenges and necessitate sustainable innovation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Co-authorship networks\u003c/h2\u003e \u003cp\u003eIn academic research, collaboration plays an essential role in creating and disseminating scientific knowledge [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Co-authorship networks demonstrate the relationships between authors who cooperate on academic publications, providing a valuable and informative perspective on the structure and dynamics within scholarly communities [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. These networks not only reveal collaboration patterns but also indicate intellectual influence, resource sharing, and the dissemination of innovation across various fields [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. By analyzing co-authorship networks, researchers can evaluate the impact of interdisciplinary collaboration, assess the significance and connectivity of key contributors, and spot emerging trends in particular academic areas [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Studying these networks as the research environment evolves provides essential insights into how knowledge is constructed and shared through collective scholarly efforts.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show The analysis of co-authorship networks reveals a diverse range of collaborative efforts among prominent researchers, highlighting significant contributions to the academic field. Leading the list is Yogesh K. Dwivedi, who has authored ten documents that garnered a substantial 2,805 citations, indicating both prolific output and high impact within the research community. Following him are John S. Edwards and Yanqing Duan, whose works, although fewer in number (3 and 2 documents, respectively), have also received considerable citations (2,021 and 1,975). This suggests that their publications have achieved notable recognition. The total link strength metric further underscores the collaborative nature of these researchers, with Dwivedi displaying the highest connectivity at 24, suggesting a broad network of academic partnerships. Overall, these findings emphasize the role of co-authorship as a critical driver of scholarly influence, fostering the dissemination of knowledge through collaborative networks.\u003c/p\u003e\u003cp\u003e \u003cb\u003e3.8 Keyword co-occurrences and produktive authors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn academic research, collaboration plays an essential role in creating and disseminating scientific knowledge [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Co-authorship networks demonstrate the relationships between authors who cooperate on academic publications, providing a valuable and informative perspective on the structure and dynamics within scholarly communities [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. These networks not only reveal collaboration patterns but also indicate intellectual influence, resource sharing, and the dissemination of innovation across various fields [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. By analyzing co-authorship networks, researchers can evaluate the impact of interdisciplinary collaboration, assess the significance and connectivity of key contributors, and spot emerging trends in particular academic areas [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Studying these networks as the research environment evolves provides essential insights into how knowledge is constructed and shared through collective scholarly efforts [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn academic research, collaboration plays an essential role in creating and disseminating scientific knowledge [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows The co-occurrence network data provides insights into crucial research topics' centrality and relational importance within a specific academic discourse. \"Artificial intelligence\" stands out with the highest betweenness centrality (208.838), closeness (0.02), and PageRank (0.133), indicating its pivotal role as a connecting node across the network and its influence on other research themes. Topics such as \"machine learning\" and \"decision making\" also exhibit notable centrality measures, reflecting their relevance and integration within interdisciplinary studies. The clustering of these terms suggests a thematic focus on AI-related applications, with substantial interconnections in domains such as \"algorithm,\" \"China,\" and other emergent technological and societal contexts. This data underscores the prevalent academic interest in AI and its intersections with various fields, affirming its status as a cornerstone of contemporary research with extensive influence across adjacent disciplines. The high betweenness of specific nodes reflects the structural importance of these concepts in linking diverse research areas, suggesting a wealthy and interconnected knowledge landscape driven by technological innovation.\u003c/p\u003e \u003cp\u003eThe trend analysis of author productivity in scholarly publications reveals significant contributions from leading academics. The data identifies \"Yigitcanlar, T.\" as the most prolific author, with 24 documented publications showcasing their prominent role in advancing research within their domain. Following closely are \"Janssen, M.\" and \"Mosavi, A.\" each with 17 contributions, further demonstrating their active engagement in academic discourse. \"Dwivedi, Y.K.\" and \"S\u0026aelig;tra, H.S.\" also make notable contributions, with 12 and 11 publications, respectively, underscoring their influence in the literature. This distribution of productivity not only highlights individual academic excellence but reflects the collaborative and cumulative nature of knowledge generation. Future research may benefit from a more profound exploration of these high-output authors' thematic focus and interdisciplinary impact.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Key Trends, Insights and Bibliometric Coupling Map\u003c/h2\u003e \u003cp\u003eExamining essential trends and insights in academic research offers a thorough comprehension of the changing environment of scholarly inquiry [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. By pinpointing prominent themes, newly emerging areas of interest, and changes in methodological strategies, researchers can acquire helpful foresight into the future directions of investigations [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Key trends frequently mirror more considerable societal, technological, or theoretical changes, while the insights from these trends simplify the practical implications of research across different fields [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. This examination also emphasizes patterns of collaboration, innovation spread, and interdisciplinary convergence, providing a refined understanding of how scientific knowledge is influenced by and influences its broader context [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e explains that The treemap visualization displays the key topics and research directions in current scientific discourse, emphasizing \"artificial intelligence\" (23%) and its essential role across numerous fields. Related areas such as \"smart city\" (8%), \"machine learning\" (4%), and \"sustainability\" (4%) further illustrate the interdisciplinary use of AI in urban development and environmental preservation. Additional essential themes, including \"decision making,\" \"human factors,\" and \"algorithm,\" indicate a focus on the convergence of technology and human-focused applications, emphasizing the necessity for research that harmonizes technological progress with social consequences. Emerging fields like \"blockchain,\" \"deep learning,\" and \"COVID-19\" display the discipline's flexibility in tackling real-world issues, especially in health and governance [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]; [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]; [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The variety of subjects reflects a solid commitment to advancing technology while considering ethical, sustainable, and human-focused strategies, pointing to a comprehensive academic interest in shaping future societal structures through innovation and responsible integration of technology [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. This visualization represents existing academic priorities and indicates a balanced research environment that combines technical expertise with societal welfare, aligning with the broader goals of promoting knowledge for comprehensive, sustainable development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe bibliometric coupling map in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e illustrates research clusters' thematic relationships and influence within the scientific domain. Prominent clusters are characterized by their centrality and impact, highlighting their relevance to the academic community. Notably, clusters associated with \"smart city\" and \"blockchain\" demonstrate high centrality (88.5% and 85.7%, respectively) and significant impact, signifying their pivotal roles in interdisciplinary research and application. Similarly, topics such as \"sustainable development\" and \"climate change\" achieve high impact scores (60% and 100%, respectively), reflecting their global relevance and alignment with contemporary societal challenges. In contrast, clusters related to \"artificial intelligence\" and \"machine learning\" exhibit varying levels of centrality and impact, underscoring the nuanced evolution of these fields in addressing both theoretical and practical challenges. The visualization clearly illustrates the interrelation of these themes, providing a thorough view of the changing nature of scientific research. Upcoming studies could investigate the cooperation between these clusters to assess their collaborative possibilities and emerging trends.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Global Research Contributions\u003c/h2\u003e \u003cp\u003eThe results outlined in this study greatly enhance the worldwide understanding of e-governance by clarifying the complex relationship between emerging technologies and governance systems. As digital transformation influences the public sector, incorporating technologies like AI and blockchain is essential for improving transparency, accountability, and citizen involvement in governance practices [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. This research adds to the conversation on how innovative digital tools can improve governance frameworks, thus tackling current issues in public administration [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Besides, this study's findings align with global movements promoting data-driven decision-making and the necessity for flexible regulatory frameworks, which are essential for building trust and inclusivity in e-governance initiatives [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. By investigating the consequences of these technological advancements, this research not only enhances academic knowledge but also acts as a valuable resource for policymakers aiming to improve the effectiveness of e-governance strategies around the globe.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the global distribution of research on e-participation and cybersecurity, demonstrating a significant concentration of productivity among the top 10 contributing countries, reflecting their critical role in advancing these crucial areas. The United States has 1,146 publications, demonstrating its strong commitment to digital governance and cybersecurity frameworks. The United Kingdom followed closely with 1,062 documents, highlighting vital research initiatives and policy-driven studies in e-participation. With 822 contributions, China demonstrated its strategic focus on integrating cybersecurity into its vast digital infrastructure. Germany and Australia, which contributed 499 and 481 documents, respectively, further reinforced the importance of developed countries in driving innovation and knowledge in this domain. Other key contributors, including India, Canada, South Korea, Japan, and Italy, collectively underscore the global interconnectedness and shared responsibility in addressing challenges and opportunities in e-participation and cybersecurity. This distribution also underscores gaps in research outcomes, which invite further exploration of how regional capacities and international collaboration influence the dissemination of knowledge and best practices in this area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Synergies and Challenges\u003c/h2\u003e \u003cp\u003eThe combination of AI and blockchain technology in governance presents exciting opportunities to enhance the efficiency and transparency of public administration. AI's strengths in data analysis and predictive modeling work well with blockchain's secure and transparent ledger, promoting knowledgeable decision-making and building trust among stakeholders [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. By using AI to examine data stored on blockchain networks, governance organizations can gain insights for policy formulation and resource distribution. For example, AI's capability to identify trends and anomalies in real time supports fast responses to issues, improving governance flexibility [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Also, AI can automate routine tasks such as compliance checks and reporting, which decreases administrative burdens and simplifies processes [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, despite these benefits, deploying AI and blockchain in governance encounters essential obstacles. The technical details of these technologies require expertise often lacking in public sector organizations, a situation worsened by the swift evolution of technology that creates skill disparities [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Regulatory and ethical challenges hinder adoption, as current frameworks may fall short in addressing the unique issues posed by these technologies, potentially hampering innovation [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Ethical dilemmas, especially those concerning data privacy and security, emerge since blockchain's transparency could reveal sensitive information, necessitating robust data protection protocols [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. It is essential to develop adaptive regulatory frameworks and targeted capacity-building programs to overcome these challenges. Future studies should focus on comprehensive approaches to responsibly integrating AI and blockchain, ensuring innovation aligns with public welfare and ethical principles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Ethical and Practical Considerations\u003c/h2\u003e \u003cp\u003eThe application of Artificial Intelligence (AI) and blockchain technology in governance raises critical ethical and practical challenges that require careful consideration [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. One major ethical concern is the risk of bias embedded within AI algorithms, which often reflects historical inequalities, potentially perpetuating discrimination in public decision-making processes [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Such biases undermine principles of fairness and justice, particularly for marginalized groups [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. To address this, governance frameworks must adopt robust auditing mechanisms to identify and mitigate algorithmic bias, ensuring equitable outcomes. Blockchain technology, while enhancing transparency and accountability, introduces data privacy and security challenges [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. The immutable and transparent nature of blockchain can expose sensitive information, necessitating comprehensive data governance frameworks to balance transparency with individual privacy rights [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a practical perspective, the successful integration of AI and blockchain in governance is hindered by technical expertise gaps and resource constraints [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. Public sector organizations often lack the necessary skills and financial resources to implement and maintain these advanced technologies [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. Furthermore, the rapidly evolving regulatory landscape struggles to keep pace with technological advancements, creating misalignments that stifle innovation [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Policymakers must develop adaptive regulatory frameworks that safeguard public interests while promoting ethical considerations [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Collaborative efforts among technologists, ethicists, and regulators are crucial to addressing these challenges and ensuring that the transformative potential of AI and blockchain enhances transparency, accountability, and equity in governance systems [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Policy Implications\u003c/h2\u003e \u003cp\u003eAs urban development speeds up, AI and blockchain technology offer revolutionary opportunities for improving governance in smart cities [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. AI allows policymakers to examine extensive urban datasets, including information from transportation, energy, and public services, to reveal patterns and forecast trends, thereby optimizing systems like traffic management and bolstering public safety [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. This data-driven strategy promotes responsive governance that aligns with the changing needs of urban populations [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. Simultaneously, blockchain's decentralized and unchangeable ledger system boosts transparency and trust by securely monitoring resource distribution and ensuring accountability in government operation [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. For example, blockchain-based procurement systems can help reduce corruption and enhance public trust, with pilot projects displaying their ability to simplify bureaucratic processes [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to efficiency and transparency, combining AI and blockchain can improve citizen engagement and participation in governance [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. AI-driven platforms assist in real-time public consultations, guaranteeing that governance aligns with community needs [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. Blockchain enables citizens by granting secure access to their data and encouraging active involvement in decision-making [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. Nonetheless, integrating these technologies necessitates tackling ethical and regulatory challenges, particularly concerning data privacy and security in smart cities [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. Policymakers must develop comprehensive data governance frameworks and adaptable regulatory environments to protect public interests while promoting innovation [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. By strategically using these technologies, policymakers can build efficient, accountable, and comprehensive governance systems that respond to the changing needs of urban populations [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.6. Future Research Directions\u003c/h2\u003e \u003cp\u003eIntegrating Artificial Intelligence (AI) and blockchain technology in governance presents numerous opportunities for future research to address existing challenges and maximize their potential [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]; [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. A key area is the development of ethical frameworks that mitigate biases in AI algorithms and address privacy concerns associated with blockchain's transparency [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]; [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. Researchers can use case studies and empirical analyses to evaluate the effectiveness of these frameworks in ensuring fairness and accountability. Additionally, the interoperability of AI and blockchain systems within governance frameworks requires further investigation [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. As intelligent cities adopt various digital technologies, understanding the technical and organizational challenges of integrating these systems is essential to creating cohesive and efficient governance solutions [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]. Collaborative studies involving policymakers, developers, and urban planners can yield insights into best practices for achieving effective integration [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]; [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData governance and regulatory frameworks also represent critical areas for exploration [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. Research should examine how data quality, ownership, and access impact the effectiveness of AI and blockchain in governance while also considering implications for public trust and transparency [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]; [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. Comparative studies on regulatory approaches across jurisdictions can assess their influence on innovation, public trust, and ethical use, offering valuable guidance for policymakers [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e]; [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. Finally, the socio-economic implications of these technologies warrant attention, particularly their potential to address inequality and inclusivity in governance, especially in developing regions [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. Investigating strategies to make digital governance accessible and affordable can ensure these benefits extend to all citizens, fostering equitable and effective governance solutions [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study thoroughly examines the combination of AI and blockchain technology in e-governance, especially considering innovative city frameworks. The results indicate that AI's ability to perform real-time data analysis, predictive modeling, and improved decision-making is greatly enhanced by blockchain's features that provide transparency, data security, and trust through its decentralized and unalterable framework. These technologies present a synergistic opportunity to change public administration by enhancing efficiency, accountability, and public trust in governance systems. The bibliometric analysis carried out in this study emphasizes the swift increase in research interest in this domain, charting key trends, essential studies, and ongoing gaps, particularly the shortage of empirical assessments on their integrated application. In promoting global research, this study offers several significant contributions. It fills the gap between theoretical discussion and practical application by systematically reviewing the existing state of AI and blockchain integration in e-governance. The work explains the complementary aspects of these technologies, delivering actionable insights for researchers and policymakers to develop frameworks that harmonize technological advancements with governance requirements. Besides, it enhances the academic conversation surrounding technology-driven governance, stressing the necessity to tackle scalability, interoperability, and inclusivity issues. This research sets the stage for future investigations. It is an essential resource for policymakers aiming to deploy AI and blockchain solutions that improve public service delivery while ensuring ethical and sustainable results.\u003c/p\u003e \u003cp\u003eDespite its essential contributions, the study recognizes some limitations. Firstly, the research is primarily based on bibliometric analysis, which, although practical, does not offer the contextual depth that field-based empirical studies can provide. Secondly, focusing on intelligent cities leaves space for exploring these technologies in broader governance contexts, such as rural administration or international governance networks. To address these gaps, upcoming research should focus on longitudinal case studies and pilot projects evaluating the practical implementation of AI and blockchain in different e-governance settings. Besides, regulatory and ethical issues, including worries about data privacy and the ethical use of AI, need continuous attention from academics and policymakers to guarantee that these technologies are used responsibly. In summary, the combined potential of AI and blockchain technologies signifies a revolutionary shift in governance, presenting unique opportunities to enhance decision-making, transparency, and citizen involvement. However, achieving this potential requires ongoing interdisciplinary research, robust policy frameworks, and ethical considerations to navigate the complexities of incorporating these technologies into governance systems. By confronting these challenges, AI and blockchain can become foundational technologies for establishing more innovative, more comprehensive, and more resilient governance frameworks in the digital age.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eSandi Lubis contributed to bibliometric data analysis and writing the manuscript. Achmad Nurmandi integrated AI and blockchain in smart city governance. Jamaluddin Ahmad discussed the practical implications of these technologies. Eko Priyo Purnomo focused on strategies for implementing new technologies in government. Titin Purwaningsih explored the social and policy aspects of AI and blockchain. Hazel D. Jovita-Olvez provided global insights through comparative analysis and case studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The author(s) received no financial support for this article\u0026apos;s research, authorship, and publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e All data associated with this article is available upon request from the author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The author(s) affirm that there are no conflicts of interest concerning this article\u0026apos;s research, authorship, or publication. 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Ethics Soc. \u003cb\u003e18\u003c/b\u003e(3), 377\u0026ndash;391 (Jan. 2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/JICES-02-2020-0016\u003c/span\u003e\u003cspan address=\"10.1108/JICES-02-2020-0016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, Blockchain, E-Governance, Smart City, Bibliometric Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5694080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5694080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe merging of AI and blockchain technologies offers essential potential for e-governance, especially in improving predictive policy execution within smart cities. This study thoroughly reviews and assesses existing literature to clarify trends, key publications, and research gaps. Using detailed bibliometric analysis, we explored peer-reviewed articles from well-respected publishers indexed by Scopus, concentrating on works published from 2019 to 2024. Our results reveal an increasing volume of research examining the separate applications of AI and blockchain in e-governance. However, there is a noticeable lack of empirical studies focusing on their combined implementation. Major themes identified include the possibility of improved transparency and efficiency in public services, issues related to interoperability, and ethical considerations about data privacy and algorithmic accountability. This study is constrained by its dependence on bibliometric methods, which may not entirely reflect the practical complexities of technology integration across various governance contexts. Future research should emphasize longitudinal case studies and pilot initiatives to evaluate real-world uses of AI and blockchain in e-governance, addressing regulatory and ethical challenges to promote responsible adoption. This work adds to the global discussion on digital governance, providing a foundational framework for advancing AI and blockchain-enabled smart city projects.\u003c/p\u003e","manuscriptTitle":"AI and Blockchain For Next-Generation E- Governance: A Comprehensive Bibliometric Review In Smart City Innovation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 13:13:07","doi":"10.21203/rs.3.rs-5694080/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8b0c79c7-d824-4446-835e-9e9c497f5ebf","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-23T09:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-17 13:13:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5694080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5694080","identity":"rs-5694080","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}