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Abstract

Recent societal challenges have highlighted the urgent need for public administration (PA) to harness the potential of disruptive technologies (DT) such as artificial intelligence (AI), blockchain, the Internet of Things (IoT), virtual and augmented reality, 3D printing, robotics and drones. This paper aims to fill the research gap in comprehensive analyzes, especially with regard to the practical applications of AI in the context of DT in PA. The paper has two main objectives: first, to identify the role of DT, with particular attention to AI, in PA considering current research trends; second, to provide a comprehensive overview of the main practical applications of DT in PA worldwide. First, a bibliometric analysis of 5,927 documents in the Scopus database published between 2010 and 2023 is conducted. This is followed by a content analysis of prominent documents from institutions such as the World Bank, the OECD and the EU. The results show that DT, especially AI, are gaining importance in PA research and have concrete applications in various sectors. This study provides a refined conceptualization of DT in the context of PA with a focus on AI.

Keywords

disruptive technologies artificial intelligence public administration bibliometric analysis content analysis

Introduction

Recent demographic changes, economic inequality, climate crisis, globalization, security concerns and political instability require a strong government commitment to implementing effective and efficient public policies that create significant public value and serve as cornerstones for public services and innovation (Criado & Gil-Garcia, 2019). According to Christensen (2013), innovations are divided into sustaining innovations, which maintain existing processes, and disruptive innovations, which introduce new solutions, change value networks and replace old technologies, with disruptive technologies (DT) driving both localized and broader societal change (Schuelke-Leech, 2018).

DT are used to improve the transparency and efficiency of governance and serve as key elements or catalysts for innovation (Gil-Garcia et al., 2014). In a rapidly evolving technology landscape, accurately predicting which innovations can transform practices for greater efficiency and effectiveness is critical to economic growth and societal prosperity (Dotsika & Watkins, 2017). This is particularly evident in public administration (PA), where agencies need to acquire new skills, train staff, purchase technology and upgrade network infrastructure (Lee & Kwak, 2012), often within a rigid and politically determined framework of legal and financial resources and within short timeframes. As Gascó (2017) points out, public organizations should improve their governance models to ensure more open approaches to innovation and take advantage of the opportunities that arise from collaboration between citizens, entrepreneurs, civil society and the new DT. As Tangi et al. (2024) highlight, the impact of DT in PA spans three dimensions: (a) internal (operational needs, process optimization), (b) external (service delivery) and in (c) policy decision-making with existing and future strategies.

In the existing literature, there is no consensus on which technologies could be defined as disruptive in PA. Aristovnik et al. (2022) define eight such technologies, namely artificial intelligence (AI), blockchain, the Internet of Things (IoT), virtual and augmented reality, 3D printing, robotics, drones and social media. In this context, Abdel-Basset et al. (2021) add Industry 4.0, big data and 5G to the DT mentioned above, and Bongomin et al. (2020) also include cloud computing, cyber-physical systems, smart sensors, simulation, nanotechnology and biotechnology in the context of Industry 4.0. An even greater diversity is observed in DT related to the smart city, as the authors also highlight mobile computing, 6G networks, Wi-Fi 7, Industry 5.0, digital forensics, automated vehicles, flying cars (Javed et al., 2022) due to the production of large amounts of sophisticated data about cities and their citizens (Kitchin, 2014), thus promoting telecity, information city and digital city concepts, as suggested by Silva et al. (2018). Despite the many challenges it faces (Desouza et al., 2020; Wirtz et al., 2019a), AI should be emphasized as a pivotal DT in PA (Dwivedi et al., 2021; Kuziemski & Misuraca, 2020).

Despite the increasing research interest, there is a lack of systematic studies that comprehensively address DT in public service and AI in particular. The problem lies in the unclear conceptualization of DT in PA and the insufficient focus on analyzes that go beyond basic implementation, which hinders the further development of research methods (Kankanhalli et al., 2019). Therefore, this paper aims to address the research gap in a comprehensive analysis, especially with regard to the practical applications of AI in the context of DT in PA. The paper has a twofold objective: first, to examine the role of DT, particularly AI, in PA considering current research trends; second, to provide a comprehensive overview of their key practical applications in PA worldwide. Particular attention will be paid to identifying the overlaps between the different technologies and placing the most commonly encountered concepts related to these technologies within this framework. This article thus builds on the conference paper “Mapping research trends on disruptive technologies in the public administration: A bibliometric approach” (Aristovnik et al., 2022), particularly with regard to the theoretical and practical integration of DT in PA, with a focus on AI and its differentiation from other technologies. To this end, this study aims to answer the following four research questions:

RQ1: What is the current state of DT research in PA by analyzing relevant scientific production?

RQ2: How much overlap is there between DT within PA research?

RQ3: What predictive relationships can be identified in DT research in PA?

RQ4: What are typical examples of the practical use of DT in PA?

Answering the research questions will provide crucial conceptual clarification, highlight the overlap between different technologies and bridge the gap between theory and practical application. This comprehensive understanding of DT is crucial for the further development of PA in the digital age, while addressing complex global challenges and ensuring more adaptable and citizen-centered public services.

The rest of the paper is structured as follows. The following section provides some basic definitions, followed by a literature review of existing and recent studies on DT in the public sector and PA. The next section describes the materials and methods used in this study. The subsequent section presents the results of the study, which is followed by the discussion section. The paper ends with a conclusion that summarizes the main findings and implications and points out the limitations of the paper.

Defining DT Landscape

In view of the theoretical ambiguity, it is essential to first define the core concept of DT. The term “disruptive technology” (also referred to as edge, emerging, smart or new technology) refers to technologies whose application has the potential to significantly change existing systems, processes and structures, often leading to new ways of governance and public service delivery in PA (Kostoff et al., 2004; Leitner & Stiefmueller, 2019). In this context, Schuelke-Leech (2018) points out that the mere development of DT does not guarantee success, especially in PA, where technologies cause both first-order disruptions (local improvements) and second-order disruptions that lead to broader, more profound changes in social norms and institutional frameworks. This perspective aligns with Lenk’s (2007) critique of the disconnect between PA theory and the practical application of DT, who emphasizes that to be truly disruptive, technologies must bring about transformative changes in governance structures and information processing. In line with the objectives of this study, this article therefore builds on Schuelke-Leech’s second-order disruption approach, which emphasizes the transformative potential of technologies for PA rather than focusing only on their technological characteristics. This approach resolves inconsistencies in the definition of DT by focusing on their broader institutional implications and the ways in which they reshape governance structures, thus ensuring a more precise analysis of DT in PA.

The broader implications of DT for PA require a more critical and nuanced assessment, particularly with regard to the question of alignment with fundamental social values in the redesign of governance structures. To the extent that Wirtz et al. (2019a) highlight the efficiency and effectiveness gains that these technologies can offer, this focus on operational improvements may obscure deeper concerns about how such innovations can challenge democratic accountability and public trust. As Taeihagh (2021) notes, DT often exceeds existing legal frameworks, leaving gaps in governance that can lead to unintended consequences, such as “black box” governance where citizens have little insight into or recourse against algorithmic decisions that affect their lives. This over-reliance on data-driven technologies risks overlooking human-centered policy nuances, leading to rigid, impersonal outcomes and raising concerns about the erosion of public values such as fairness and justice that are difficult to encode in algorithms (Zuiderwijk et al., 2021). Furthermore, Vesnic-Alujevic et al. (2020) warn that DT could unintentionally reinforce inequalities within public institutions by centralizing control and allowing elites or private actors to exert disproportionate influence over PA, potentially marginalizing vulnerable populations. Criado and Gil-Garcia (2019) add that without adaptation to the political context, these technologies could replicate existing inefficiencies. Similarly, Robinson (2020) also warns that DT, which is often geared toward private sector efficiency, could undermine core PA values and weaken oversight and public trust.

From this transformative perspective, it is crucial to define technologies that can have a disruptive impact in and for PA. First and foremost, AI is the ability of a computer system to correctly interpret external data, acquire knowledge from that data through various learning mechanisms, and then use what it has learned through flexible adaptation to achieve specific goals and tasks (Kaplan & Haenlein, 2019). To be more specific, the Organisation for Economic Co-operation and Development (OECD’s) (2024b) latest definition of AI states that an AI system is a machine-based system that, for explicit or implicit goals, infers from the inputs it receives how to produce outputs such as predictions, content, recommendations or decisions that can affect physical or virtual environments. Different AI systems vary in their degree of autonomy and adaptability after deployment. AI, consisting of machine learning, neural networks and deep learning (Coccia, 2020), is often associated with terms such as big data (Janssen et al., 2017), algorithms, autonomous vehicles, chatbots, etc. (Dwivedi et al., 2021). Blockchain is a constantly expanding chain of blocks that securely stores all transactions in a publicly accessible ledger. Each transaction is cryptographically verified and signed by all mining nodes (Bhushan et al., 2020) and is often associated with concepts such as smart contracts, authentication, ledger technology, etc. (Hammi et al., 2018). IoT refers to a network of physical objects or devices equipped with electronics, software, sensors and actuators that have the ability to communicate, interact and exchange data with each other, and is often associated with the concepts of smart city and smart government (Kankanhalli et al., 2019), which include sensors, digital twins, smart mobility, urban planning, sustainability, etc. Virtual reality (VR) is a computer-generated simulation of a real-world environment or scenario, while augmented reality (AR) is a technology that adds computer-generated elements to the real world, enhancing it and enabling interaction to make the experience more meaningful (Yagol et al., 2018). VR and AR are often associated with the concepts of computer vision, gamification or semantics. 3D printing is a process in which three-dimensional objects are created layer by layer from a digital model as a transformation of the digital world into the physical world (Akbari & Ha, 2020) and is associated with the concepts of computer simulation. To the extent that a robot is a programmable machine designed to perform specific tasks autonomously or under human control, Suzuki (2018) defines drones as unmanned aerial vehicles that fly autonomously or remotely, or, to put it simply, drones are flying robots. They are often associated with concepts for autonomous vehicles and embedded systems.

Literature Review

Bibliometric Studies

Based on the Scopus database, bibliometric analyzes of DT in the public sector and PA focus mainly on AI research. In this context, Lawelai et al. (2023) suggested that through the strategic development and utilization of AI-based technology, traditional public services can be transformed into smart services. In this setting, Straub et al. (2023), who investigated AI, particularly generative language modeling, as a transformer of government, merged previous efforts in social and technical disciplines. In addition, Gao et al. (2023) examined AI as a tool for creating sustainable open government data and smartness. Several studies focused on the role of AI in policy making and public participation (El-Taliawi et al., 2021; Waheed et al., 2018) or on civil society empowerment in this context (Savaget et al., 2019). Other bibliometric studies focused on ethical and privacy issues related to AI and their intersections (Zhang et al., 2021) or on the role of data for AI-based decision making (Di Vaio et al., 2022) or policy analysis (Zhang et al., 2018). In addition, some studies focused on pioneering areas of AI research, such as the analysis by Ho et al. (2021), who investigated emotional AI, also known as affective computing, or Jain et al. (2023), who investigated artificial neural networks (ANN) as the most prominent machine learning technique. There are also several studies that focused on the intersection of AI with big data, more specifically on evidence-based policy for e-government (Abuljadail et al., 2023) or the implementation of smart governance (Tiwari et al., 2019) and digital governance, which illustrates the overarching trend of shifting from localized to globalized governance (Sun et al., 2024). In this context, Kong et al. (2020) suggested that future research should integrate multiple big data sources and develop and utilize new methods such as deep learning and cloud computing, while Xia et al. (2022) emphasized that the focus in the future should be on normative theories and institutions in big data-driven public services.

The IoT-based bibliometric studies mainly focused on the concept of smart city and its impact on public value. Rejeb et al. (2022) found that IoT research has increased significantly in recent years. According to Sharifi et al. (2021), this research is dominated by either conceptual issues or the underlying technical aspects. Zhao et al. (2019) found that the current focus of smart city research is in areas such as IoT, sensor networks and cloud computing. El-Agamy et al. (2024) focused on reviewing the technology of the digital twin, a virtual representation of a physical object or system, which has proven to be crucial for the urban development of smart cities. In addition, Szum (2021) identified five main research directions for IoT in the smart city field: IoT application areas in smart cities, IoT architecture for smart cities, energy, security, privacy and data. In this context, Allam et al. (2022) stated that the emerging themes emphasize the need for citizen participation in urban policy and the need for sustainable innovation for e-participation, e-governance and policy frameworks. To achieve this, the study by Karger et al. (2024) underlines the increasing importance of blockchain use in smart cities.

The bibliometric analysis conducted by Rejeb et al. (2021) found that the number of articles dedicated to the study of blockchain applications has increased exponentially in recent years. Balcerzak et al. (2022) found that the decentralized system of blockchain enables seamless reliability, scalability and interoperability in data exchange, and blockchain-enabled smart contracts enable democratic governance structures. On the other hand, Tan (2023) found a high correspondence between barriers and blockchain adoption in the context of governance.

Several other studies also analyzed the integration of different DT in the context of PA. Szpilko et al. (2023) identified main research areas for the integration of AI and IoT in the smart city context, such as security, housing, energy, mobility, health, pollution and industry. Espina-Romero and Guerrero-Alcedo (2022) identified 10 areas where digitalization is most commonly applied, including AI, AR and VR. In addition, Wu et al. (2022) found that the combination of AI, blockchain and IoT offers significant applications for advancing sustainability research. Considering various research trends related to the above technological combinations, Ismail and Hartati (2023) concluded that the relevance of research in the field of PA lies in finding the right balance between application and theory development.

Thematic Overview

Apart from the bibliometric studies, there are only a few non-bibliometric studies that provide an overview of the use of DT in PA. Ronzhyn and Wimmer (2022) proposed a roadmap for future DT applications in the context of Government 3.0 and emphasized the need for organizational adaptations, secure data practices and improved system interoperability. Kalampokis et al. (2023) analyzed DT based on Horizon 2020 projects, a novel approach in the field, while Păvăloaia and Necula (2023) conducted a sentiment analysis of DT literature and found a predominantly positive attitude toward technologies such as AI, IoT and blockchain.

Several other studies dealt with individual DT or the integration of several DT in the context of PA or its narrower domains. The most numerous are the studies dealing with the use of AI, especially with regard to the benefits and challenges that AI brings to PA (Dwivedi et al., 2021; Sun & Medaglia, 2019; Wirtz et al., 2019a; Zuiderwijk et al., 2021) and leads to a “good AI society” (Cath et al., 2018; Floridi et al., 2018). To create public value for the administration, AI implementation should cover dimensions that include data and technology as well as organizational and environmental aspects (Desouza et al., 2020) by creating a framework for public AI management, as suggested by Wirtz and Müller (2019). In addition, several studies addressed the question of how AI can lead to an improvement in the efficiency of PA (Henman, 2020; Sharma et al., 2020) and smart e-government as opposed to traditional bureaucratic approaches (Liu & Yuan, 2015; Misuraca & Viscusi, 2013; Newman et al., 2022). Other studies addressed topics such as the development of AI policies (de Sousa et al., 2019), also in relation to big data analytics (Pencheva et al., 2020), artificial discretion (Barth & Arnold, 1999; Young et al., 2019) or some AI sub-technologies, such as machine learning (Zekić-Sušac et al., 2021) or generative AI (Dwivedi et al., 2023).

While the IoT is crucial to the development of smart cities, studies have looked extensively at its governmental applications. Wirtz et al. (2019b) have developed a framework for IoT in smart government, and Chatfield and Reddick (2019) have looked at security issues. El-Haddadeh et al. (2019) and Kankanhalli et al. (2019) highlighted citizen empowerment as one of the key benefits. Isaac et al. (2018) and Jasimuddin et al. (2017) examined the factors influencing the adoption of IoT, while Velsberg et al. (2020) created a smartness-based network framework for public services. Other studies examined the technological aspects of IoT, such as smart sensors (Tang & Ho, 2019), blockchain integration (Qi et al., 2017) and data-level communication (Ortiz et al., 2022). IoT applications tailored to specific societal needs also include combating COVID-19 (Siriwardhana et al., 2020; Tropea & De Rango, 2020) and improving the legal framework in the public services (Kennedy, 2016).

With regard to the use of blockchain in PA, the study by Ølnes et al. (2017) emphasized that the benefits of blockchain for the government are often exaggerated and therefore presents a critical assessment of this technology. To increase their efficiency, governments should pay attention to information security, costs and reliability when using blockchain (Hou, 2017) and develop field-specific blockchain applications (Grover et al., 2019). Studies on the transformation of PA operations (Warkentin & Orgeron, 2020) and administrative reforms through the implementation of solutions based on the use of blockchain (Myeong & Jung, 2019), including in most digitally developed countries (Ojo & Adebayo, 2017), suggested that the adoption of blockchain in PA should take a multidimensional and multistakeholder perspective (Toufaily et al., 2021). In this context, the studies presented by Clavin et al. (2020) and Tan et al. (2022) proposed specific blockchain-based governance to achieve this. Other research in this area also addressed critical issues of security (Tshering & Gao, 2020) and transparency (Sedlmeir et al., 2022), often in conjunction with the resilience of e-voting systems (Baudier et al., 2021; Khan et al., 2018).

Research on VR and AR in PA is relatively limited. In addition to studies dealing with the general aspects of the use of these technologies (Tozsa, 2013) and the factors influencing their adoption (Fernandes et al., 2006), other studies dealt with sector-specific aspects of use. Studies by Asad et al. (2022), Braun and Slater (2014) and Liu and Zhang (2021) examined the use of VR and AR in education, while healthcare was covered by Jung et al. (2019). In terms of organizational leadership, Semenets-Orlova et al. (2022) conducted research, while Fegert et al. (2020) focused on the aspects of citizen e-participation.

Research on drones and robots in PA is scarce, with studies on drones focusing on legal, regulatory and risk-related issues (Agapiou, 2020; Rădescu & Dragu, 2019) and on their use in policing and maritime surveillance (Anania et al., 2019; Stokes et al., 2020). Robotics research addresses ethics and specific applications such as automated decision making and crisis management (Kernaghan, 2014; Ranerup & Svensson, 2022; Wilk-Jakubowski et al., 2022), with additional studies on care robots in elderly care (Aaen & Nielsen, 2022; Blackman, 2013; Nielsen et al., 2016). In contrast, 3D printing is poorly documented in PA, although it is recommended for its benefits in multi-level governance (Dickinson, 2018; Jaya et al., 2021).

More recent studies deal with the challenges of implementing DT in public administration. Lindgren (2024) highlights key issues in automating public services. Weigl et al. (2024) argue that technologies should prioritize user needs and recommend government action to avoid conflicts between public values and user orientation. Aoki et al. (2024) emphasize the need for transparent algorithmic decision-making from a societal and human-centric perspective, taking into account the ethical and practical implications of AI (Caiza et al., 2024). Delfos et al. (2024) suggest viewing machine-learning safety systems through a systems theory lens, a view supported by Misra et al. (2024) who emphasize the importance of public managers and oversight of AI deployment.

Materials and Methods

To fill the identified research gap, comprehensive bibliometric data on DT in PA and the public sector were extracted from the Scopus database on February 2, 2024. The selection of Scopus, a world-leading bibliographic database for peer-reviewed literature, was based on its broader database coverage compared to other competing databases such as Web of Science (Falagas et al., 2008). This was confirmed during the initial search in both databases, with Scopus finding more documents than Web of Science for the specified search conditions. In addition, it was found that disciplines from the social sciences and humanities are significantly underrepresented in Web of Science compared to the Scopus database (Mongeon & Paul-Hus, 2016). Therefore, the Scopus database seems to be better suited to meet the need for literature research on DT in PA.

The search query in the advanced online search engine Scopus included a wide range of keywords related to PA, the public sector and related governance concepts, as well as DT and emerging technologies, including the 15 most prominent technologies identified in previous test searches to ensure that no important technology was excluded. Thus, the search query for this study included the following PA-related keywords: “public administration,”“public sector,”“public service,”“smart city,”“smart government,”“e-government,”“egovernment,”“electronic government,”“deg,”“digital era government,”“digital-era government,”“digital government,”“open government,”“smart governance,”“e-governance,”“egovernance,”“electronic governance,”“deg,”“digital era governance,”“digital-era governance,”“digital governance,”“open governance” AND the following DT-related keywords: “disruptive technologies,”“emerging technologies,”“internet of things,”“augmented reality,”“virtual reality,”“blockchain,”“artificial intelligence,”“3D printing,”“drone,”“robot,”“big data,”“machine learning,”“deep learning,”“neural networks,”“cloud computing.” The keywords indicated correspond to the concepts of digital transformation of government and the general evolution of the e-government discourse, which includes the recent conceptualization of smart governance (Criado & Gil-Garcia, 2019). The identification of documents was limited to subject areas such as social sciences, business, management and accounting, economics, econometrics and finance, arts and humanities, and psychology. The search parameters were set to include all types of documents published between January 1, 2010 and December 31, 2023. The number of relevant documents identified in this defined search was 5,927.

After collecting data on DT in PA research at Scopus, various bibliometric methods were applied, such as a descriptive overview, scientific production analysis, network analysis, and binary logistic regression analysis using various Python libraries such as Pandas, Matplotlib, and Statsmodels (Millman & Aivazis, 2011). It is important to note that the analysis for the descriptive overview, which relates to the distribution of publications by DT by year, was conducted solely on the basis of authors’ keywords. In cases where a keyword referred to more than one DT, such an article was counted as many times as it referred to multiple DT. In addition, the analysis of overlapping technologies done by keyword co-occurrence counts was performed based on the number of authors’ keywords and the regression analysis was also performed based solely on the keywords defined by the authors for each article.

In the following research phase, a content analysis was conducted. Various forms of content analysis, such as frequency analysis, contingency analysis and thematic analysis, were used to systematically analyze the meaning of the qualitative material (Schreier, 2012), taking into account the research objectives. In this context, the most appropriate practical documents from prominent institutions such as the European Commission (EC) (2024), Organisation for Economic Co-operation and Development (OECD) (2019, 2022, 2024a), World Bank (WB) (2020), consulting firms (Deloitte, 2018) and others were analyzed. The basic application characteristics of certain DT were inquired, the complex interdependencies between various technological and social aspects discussed in the documents were identified and the dimensions of the operations of their use (internal, external, policy) were defined.

Results

Descriptive Overview

The descriptive overview presented in Table 1 shows the most important features of DT research in PA. This study is based on 5,927 documents written by 15,702 different authors and published in 1,654 sources between 2010 and 2023, with an average of 3.58 documents per source. The highest number of citations was 1,653, with an average of 17.7 citations per document. The number of documents authored by a single author was 1,081. The average number of references per document was 36.78, with a total number of 202,695 unique references.

Table 1.

Descriptive Overview of DT Research in PA (2010–2023).

Bibliometric items	Findings
Timespan	2010–2023
Documents	5,927
Language of documents: English	5,749
Cited documents	4,377
Single-authored documents	1,081
Sources (journals, books, etc.)	1,654
References per document	36.78
Citations per document	17.7
Average year of references	2013.21
H-index	113

Note. Own elaboration based on the Scopus database.

The frequency analysis of the number of documents analyzed and the cumulative citations per year is shown in Figure 1. The data show a significant increase in publications from 2017 with 529 documents, peaking in 2019 with 974 publications. However, there is a gradual decline in the post-pandemic period, with 915 documents in 2020, 739 in 2021 and a sharper decline to 668 in 2022. There is a slight upturn in 2023, with 726 documents published. This pattern of post-pandemic decline has also been observed in some related bibliometric studies, including those by Lawelai et al. (2023), Szum (2021) and Wang and Kim (2023), which show the negative impact of the strict restrictions during this period on scientific production.

Figure 1.

Distribution of publications and citations by year (2010–2023).

The two most important documents for each DT, the most cited in total number and the one with the most citations per year, are listed in Table 2. Documents dealing with AI are the most cited according to both criteria, followed by documents dealing with IoT and blockchain. Documents dealing with VR and AR are cited the least. The source Sustainable Cities and Society appears twice.

Table 2.

The Most Relevant Documents by the Number of Citations in PA Research by DT.

DT	Authors	Year	Document title	Source title	Top cited by (total/per year)
AI	Kitchin, R.	2014	The real-time city? Big data and smart urbanism	GeoJournal	1,653 (total)
AI	Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … & Williams, M. D.	2021	Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy	International Journal of Information Management	465.5 (per year)
IoT	Silva, B. N., Khan, M., Han, K.	2018	Toward sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities	Sustainable Cities and Society	985 (total)
IoT	Allam, Z., Dhunny, Z. A.	2019	On big data, artificial intelligence and smart cities	Cities	137.3 (per year)
Block-chain	Ølnes, S., Ubacht, J., Janssen, M.	2017	Blockchain in government: Benefits and implications of distributed ledger technology for information sharing	Government Information Quarterly	602 (total)
Block-chain	Esposito C., Ficco M., Gupta B.B.	2021	Blockchain-based authentication and authorization for smart city applications	Information Processing and Management	130 (per year)
VR & AR	Jamei, E., Mortimer, M., Seyedmahmoudian, M., Horan, B., Stojcevski, A.	2017	Investigating the role of virtual reality in planning for sustainable smart cities	Sustainability	95 (total)
VR & AR	Xia H., Liu Z., Efremochkina M., Liu X., Lin C.	2022	Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration	Sustainable Cities and Society	66 (per year)
Robots & Drones	Van Pinxteren, M. M. E., Wetzels, R. W. H., Rüger, J., Pluymaekers, M., Wetzels, M.	2019	Trust in humanoid robots: Implications for services marketing	Journal of Services Marketing	222 (total)
Robots & Drones	Kim, K., Kim, J. S., Jeong, S., Park, J.-H., Kim, H.K.	2021	Cybersecurity for autonomous vehicles: Review of attacks and defense	Computers and Security	73 (per year)

Source. Own elaboration based on the Scopus database.

As can be seen in Figure 2, which shows the distribution of publications by number of documents on DT by year based on the authors’ keywords, after 2018 the number of publications dealing with AI as the most analyzed technology increases, while the number of those related to IoT decreases. Especially after 2019, a trend of increasing publications analyzing AI can be seen, resulting in a growing gap compared to the number of publications on IoT and blockchain.

Figure 2.

Distribution of publications by DT by year (2010–2023).

A similar downward trend in the production of scientific papers related to IoT after 2019 is also observed by other bibliometric analyzes that have examined different aspects of IoT, for example, Szum (2021) for smart city IoT, Mohsin Ahmed and Awasthi (2021) for smart mobility IoT, and Wang and Kim (2023) in relation to IoT Home. Plausible explanations for this phenomenon could be related to the emergence of the Covid-19 pandemic, which has also influenced research paths. Analysis of the documents describing the practical applications of these technologies (Organisation for Economic Co-operation and Development [OECD], 2022; World Bank [WB], 2020) shows that the number of AI-based practical applications for the prediction, prevention and treatment of pandemics has increased significantly during this period. Scientific studies also demonstrate the successful use of AI in numerous solutions in the healthcare sector, especially in government programs and procedures related to the Covid-19 epidemic (Valle-Cruz et al., 2024).

Scientific Production

Scientific production varies greatly from country to country, with China (788 documents), the United States (249 documents), India (180 documents) and the United Kingdom (170 documents) standing out as the countries with the highest number of scientific publications, as can be seen in Figure 3. In terms of number of citations and h-index, the United States leads with 6,193 citations and an h-index of 39, followed by China with 5,977 citations and an h-index of 36, and the United Kingdom with 5,007 citations and an h-index of 36. This raises the question of why China is a leader in scientific production even though it does not use English as the official language of science. One reason could be China’s leading role in integrating technology into daily life, especially through smart city projects (Rejeb et al., 2021). Despite political tensions, China maintains strong research partnerships with the West in the field of new technologies (Ho et al., 2021), and its modernized science and technology policy (Liu & Liu, 2011) has increased research output. While the US and China have a longer history of DT research, Saudi Arabia and Turkey have the most recent publications, with Turkey leading in PA-related DT research.

Figure 3.

Scientific production, citations and h-index by country (2010–2023).

As presented in Figure 4, by far the most influential source in the respected field is Sustainable Cities and Society with 10,030 citations and an h-index of 49, followed by Government Information Quarterly with 5,304 citations and an h-index of 36, Sustainability with 4,689 citations and an h-index of 37, and Technological Forecasting and Social Change with 1,589 citations and an h-index of 19. It should be emphasized that among the top 20 sources by number of documents, 12 are e-proceedings of international scientific conferences on DT, which indicates an awareness of the importance and research opportunities offered by these rapidly spreading technologies.

Figure 4.

Scientific production, citations and h-index by source (2010–2023).

The most influential author in this research area according to the criterion of citations is Janssen, M. with 2,832 citations and an h-index of 14, with an average of 472 citations per document, followed by Bibri, S. E. with 1,464 citations and an h-index of 7, with an average of 209.14 citations per document, and Allam, Z. with 840 citations and an h-index of 7, with an average of 105 citations per document. In terms of number of documents, Allam, Z. is the most prolific author with 8 documents, followed by Bibri, S. E., Bellini, P. and Roy, J. with 7 documents each (Figure 5).

Figure 5.

Scientific production, citations and h-index by author (2010–2023).

Overlapping DT in PA

Authors’ keywords that appeared at least 10 times in the database of analyzed documents were categorized using the axial coding method, which includes DT and related terms. Of the 256 authors’ keywords that met the criterion of 10 occurrences, 100 keywords were suitable for the objectives of the study and were therefore selected for semantic coding. For data categorized in this way, an analysis of the number of co-occurrences of keywords followed to identify overlaps between DT. As can be seen in Figure 6, the largest overlaps between technologies or related concepts are between AI and IoT (keywords co-occurring 202 times), AI and big data (keywords co-occurring 167 times), IoT and big data (keywords co-occurring 98 times), IoT and cloud computing (keywords co-occurring 92 times), blockchain and IoT (keywords co-occurring 87 times) and between IoT and sensors (keywords co-occurring 84 times). There are moderate overlaps between AI and blockchain (keywords co-occurring 37 times), AI and cloud computing (keywords co-occurring 37 times), IoT and edge computing (keywords co-occurring 32 times), AI and robots (keywords co-occurring 25 times), AI and sensors (keywords co-occurring 24 times) and big data and cloud computing (keywords co-occurring 23 times). There are also slight overlaps between IoT and 5G (keywords co-occurring 19 times), AI and edge computing (keywords co-occurring 19 times), IoT and drones (keywords co-occurring 14 times), big data and blockchain (keywords co-occurring 13 times) and AI and drones (keywords co-occurring 13 times). On the other hand, there is no significant overlap between other technologies and VR and AR.

Figure 6.

Overlaps of DT and related concepts in PA based on the keyword co-occurrence count.

Insofar as there are clear overlaps between different technologies in the literature examined, one cannot generally speak of a single exclusive DT in PA either, but rather of a spillover and a joint influence of technologies with the dominant influence of one or more DT, which is reflected in integrated technological solutions for use in public organizations (cf. Palomares et al., 2021, p. 6504).

In particular, AI overlaps with the IoT when it analyzes data from IoT sensors to optimize public services such as traffic flow or emergency response; when it overlaps with blockchain, it leverages smart contract automation for secure, efficient public service transactions and fraud detection. In addition, cloud computing enables scalable AI-driven analytics. IoT devices integrated with AI, blockchain and 5G technology improve real-time monitoring and management of urban environments, public health and smart utilities. Moreover, blockchain combined with AI and cloud computing secures transactions and improves the transparency and integrity of public records and digital contracts. Cloud computing facilitates the storage and analysis of big data from IoT devices and blockchain transactions and supports comprehensive analytics in PA. 5G technology ensures ultra-fast and reliable communication for IoT devices, which is crucial for real-time public service applications and emergency response systems. Big data is integrated with AI and IoT to process and analyze huge amounts of data, enabling more informed decisions and efficiency improvements in PA.

The conducted analysis of DT in PA also shows the backlog of PA-related literature compared to scientific literature on DT in the private sector. The keyword co-occurrence analysis conducted by the authors for DT in the private sector, which is available on request, shows that these technologies have been researched more intensively in the private sector. If there is no mutual overlap between VR and AR in PA and their overlap with other technologies, this is not the case in the private sector. Also, this technology is probably not as present in the private sector due to the strong association of IoT with the concept of the smart city and therefore there is a greater focus on AI in the overlap with big data, which is more significant in the private sector than in PA. It is also evident that robotization due to industrial processes is more present in the private sector than in the PA, especially in conjunction with AI. All these observations confirm the general assumption that the PA is lagging behind the private sector in terms of technology.

Regression Analysis

Binary logistic regression was used to empirically predict DT based on the most relevant authors’ keywords describing different aspects. Accordingly, every DT has been assigned a corresponding variable ( $Y_{1}, Y_{2}, \dots, Y_{5}$ ), which takes the value 1 if a document is related to that specific DT and 0 otherwise. In binary logistic regression models, these indicator variables are treated as separate dependent variables for each DT. Moreover, 14 keywords ( $X_{1}, X_{2}, \dots, X_{14}$ ) describing different aspects of DT functionalities and implications were identified as important for the broader context encompassing all DT and were used as predictor variables in binary logistic regression models. These keywords were selected based on the following procedure. The 100 most frequently occurring keywords were screened, and 14 were selected based on their relevance to various DT functionalities and implications. Accordingly, 5 different binary logistic regression models, each with 14 predictors, were estimated (Table 3). Based on their predictive nature, this approach provides information on the most characteristic keywords for a particular DT (Aristovnik et al., 2020). The formula for the binary logistic regression models corresponds to equation (1):

\begin{matrix} P (Y_{i} = 1 | X_{1}, X_{2}, \dots, X_{p}) = \\ \frac{\exp (β_{0 i} + β_{1 i} \cdot X_{1} + β_{2 i} \cdot X_{2} + \dots + β_{pi} \cdot X_{p})}{1 + \exp (β_{0 i} + β_{1 i} \cdot X_{1} + β_{2 i} \cdot X_{2} + \dots + β_{pi} \cdot X_{p})} \\ for i = 1, 2, 3, 4 \end{matrix}

(1)

The results of the binary logistic regression presented in Table 3 show that the good governance principles and integrity dimensions of PA are relevant for AI (ethics, regulation), blockchain (transparency, trust), for VR and AR (participation), and for robots and drones (trust). Specific digitalization safety aspects are relevant for IoT (security, privacy) and blockchain (security, privacy, authentication). Furthermore, specific technological capabilities are relevant for AI (automation, surveillance), for IoT (cloud) and for robots and drones (automation, surveillance). Analytical applications are relevant for AI (decision-making), VR and AR (simulation) and for robots and drones (simulation).

Table 3.

Binary Regression Analysis Predicting DT Articles Based on the Most Important Keywords.

Aspects	AI	IoT	Blockchain	VAR	RD
	1 = Yes, 0 = No	1 = Yes, 0 = No	1 = Yes, 0 = No	1 = Yes, 0 = No	1 = Yes, 0 = No
Good governance and integrity
Transparency	0.002	−0.942**	1.437***	−0.193	−0.460
Participation	−0.342	−0.393	0.560	1.872***	−0.891
Ethics	1.303***	−0.698*	−0.995	−0.701	0.727
Trust	−0.105	0.161	0.954***	−0.736	1.224**
Regulation	0.707**	−0.458	−0.080	−0.631	0.253
Digital safety
Security	−0.157	0.716***	1.081***	−1.573	0.134
Privacy	−0.721***	0.689***	0.658***	−1.387	−1.924*
Authentication	−0.520	0.286	1.719***	−0.317	−0.504
Technological capabilities
Automation	0.912***	0.200	−0.647	−0.570	2.505***
Cloud	−0.951***	0.533***	−0.901***	−1.428*	−1.923**
Surveillance	0.683**	−0.044	−0.393	0.193	1.598***
Innovation	0.045	−0.906***	0.223	0.167	−0.919
Analytical applications
Decision-making	1.426***	−0.505	−0.998	−0.338	−0.488
Simulation	−0.528*	0.048	−0.564	1.331***	1.646***

Note. Significance: The binary prediction model is performed on 14 predictors (PA keywords) for each of the DT. Abbreviations: AI; artificial intelligence, IoT; internet of things, BC; blockchain, VAR; virtual and augmented reality, RD; robots and drones.

p < .1. **p < 0.05. ***p < .01.

Source. Own elaboration based on the Scopus database.

To be more precise, the transparency principle as an indicator variable has predictive power for blockchain-related scientific publications. These articles often address blockchain in terms of decentralization, predefined rules and the elimination of human-induced errors (Diallo et al., 2018) and achieve disruptive transformation of public service delivery systems (e.g., in the case of land registries) (Lazuashvili et al. 2019). Participation has a predictive significance for articles dealing with VR and AR. This is mostly about the virtual participation of citizens in public services and policy development processes, especially for people with disabilities (Bricout et al., 2021). Ethics has predictive power for AI-oriented articles that focus on ethical issues related to the use of AI in this field (Ballantyne & Stewart, 2019; Chang, 2021; Cowls et al., 2023). As an indicator variable, trust has predictive significance for articles dealing with blockchain and robots. Articles dealing with blockchain deal with smart contracts as an embodiment of trust toward authorities (Kundu, 2019) as well as trustworthy e-voting solutions around the world (Baudier et al., 2021). All this is to ensure a shift from a technology-driven to a demand-driven approach as a basis for a good governance approach, where blockchain applications are adapted to the needs of administrative processes and where administrative processes are changed to benefit from the technology (Ølnes et al., 2017). On the other hand, articles dealing with robots emphasize the need for trust when using service robots with frequent human-robot interaction (Babel et al., 2022) and aim to promote trust when the robots are more human-like (Van Pinxteren et al., 2019). Regulation has predictive power for AI-related articles raising concerns about the importance of regulation to prevent harm in the use of AI (Wirtz et al., 2020), particularly with regard to the use of big data in PA (Maciejewski, 2017).

Security has predictive power for blockchain articles dealing with data sharing (e.g., electronic health records) to achieve the delicate balance between secure, interoperable, and efficient access to secured data (Dagher et al., 2018), building sustainable blockchain-based ecosystems (Singh et al., 2020), and enabling distributed management of identity and authorization policies using blockchain technology (Esposito et al., 2021). Furthermore, due to their complete autonomy, IoT-based applications must recognize and authenticate each other and ensure the integrity of the exchanged data by ensuring robust identification and authentication of devices (Hammi et al., 2018). Achieving verifiability, transparency, immutability and decentralization is crucial (Bhushan et al., 2020), especially in the context of smart city (Rathore at al., 2018). Similarly, privacy is predicted in articles dealing with blockchain and IoT. Blockchain can enhance privacy in various smart communities such as healthcare, transportation, smart grid, supply chain management, financial systems, and data center networks (Bhushan et al., 2020), while IoT in this context focuses on hosting various technologies and enabling interactions between them (Syed et al., 2021). Authentication as an indicator predicts articles analyzing blockchain and claiming that this DT enables a decentralized system for secure communication with various disruptive applications (Esposito et al., 2021; Hammi et al., 2018; Jegadeesan et al., 2019).

In terms of technological capabilities, automation predicts articles that deal with AI and focus on increasing the accuracy of decision-making, accelerating performance and reducing operational costs related to the decision-making process (Maciejewski, 2017), emphasizing that automation should go beyond purely technocentric efficiency (Yigitcanlar et al., 2021). Furthermore, automation also predicts articles dealing with robots. It claims that civil servants and clients will find themselves in an environment where automation and robotic technology will bring dramatic changes as digitalization and automated decision making become more prevalent in PA (Ranerup & Svensson, 2022). The cloud as a variable predicts articles on the IoT that focus on the storage and processing of big data captured by the IoT (Hashem et al., 2016), as well as related sensor-based big data applications and computational models (Bibri, 2018). Surveillance acts as a predictor for AI articles, which are mostly raise concerns about human rights (Land & Aronson, 2020), and for articles about robots and drones, which focus on the collection of real-time data in the environment (Vineeth et al., 2017). In terms of innovation, this predictive variable has negative statistical significance only in relation to IoT-based articles, meaning that it is statistically significant in the article in which this keyword occurs that the article is not about IoT. One possible reason for this is the observation that innovation is often analyzed in the context of smart (Gil-Garcia et al., 2014) and e-governance (Liu & Yuan, 2015; Oliveira et al., 2020), which are generally not associated with IoT. Finally, the indicator variable decision making predicts AI-based articles, claiming that AI-based decisions are one of the main benefits of their use in PA (Dwivedi et al., 2021), and simulations have the predictive power for VR and AR, especially in cases of virtual urban planning (Silvennoinen et al., 2022) and robots and drones, which mainly enable simulations for infrastructure monitoring (Nguyen et al., 2021).

Practical Applications

To increase the public value of administrative services, the practical implementation of DT in PA should include dimensions that take into account various societal, legal, organizational, technological and other aspects, as shown in Table 3. Furthermore, as can be seen in Figure 6, there is often an overlap of certain technologies in PA, which means that concrete technological applications often contain elements of several technologies and their related concepts. Considering the above, Table 4 presents some typical practical applications of DT in PA with the dimension of its use in PA (internal processes; external processes/service delivery; policy decision-making) summarized from the broader list in Supplemental Table A1 of selected practical applications of DT in PA with important practical implications and their dimensions with corresponding sources.

Table 4.

Typical Practical Applications of DT in General and in PA.

DT	Typical use	Typical use in PA	Selected examples
AI	- Security applications (advanced detection technologies and behavioral analytics) - Healthcare (analysis of clinical data to support diagnosis and treatment planning) - Transportation (autonomous driving technologies, intelligent traffic management systems) - E-commerce (recommendation systems to personalize the user experience) - Education (automation of administrative tasks and adaptation of learning paths for individualized education) - Marketing and social media (analysis of user data to place targeted advertising and manage content effectively)	- Automation of government tasks (legal assistance, compliance, financial monitoring, and public safety management)^a - Improving decision-making (in areas such as taxation, immigration, and social benefits)^b - Public safety and regulatory enforcement (using predictive analytics and real-time monitoring)^{b, c} - Public engagement (providing responsive virtual assistance and interaction with government, leading to more efficient operations and informed policy decisions)^{b, c}	Prometea; Zero Trust; Alex; Jaque; DigiMe; Nadine; Northern Border Surveillance System; ECU 911; Aida; TranSEC; PHREDSS
IoT	- Smart homes & cities (improving the living environment and the efficiency of city administration) - Healthcare (monitoring wearables and remote-controlled medical systems) - Agriculture & manufacturing (automation for higher productivity and more efficient processes) - Transportation & retail (optimization of transport logistics and innovation in retail) - Environmental monitoring (advanced environmental monitoring systems)	- Urban infrastructure & traffic (real-time monitoring)^b - Agriculture & energy management (implementation of agricultural policy and control of energy use)^b - Public services & environment (simplification of access to services, control of water quality and monitoring of environmental allergens)^b	Smart Nation; Smart Farms; AquaWatch; Geolocated Allergen Sensing Platform
Blockchain	- Supply chain & financial transactions (improving transparency and security in supply chains and financial systems) - Identity & contract management (verification of identities, automation of smart contracts and immutable records in healthcare, real estate and voting systems)	- Digital transactions & services (facilitating secure transactions for land registration, electronic voting, e-participation and certification)^b - Contract management & integrity of public records (enabling digital residences, enforcing smart contracts and monitoring public records to increase transparency and trust in public authorities)^a	Digital India; iVoting; e-Residency; Keyless Signature Infrastructure, The Pension Infrastructure
Robots & Drones	- Logistics & surveillance (delivery of goods, land surveying and support for search and rescue operations) - Agriculture & infrastructure (improving agricultural practices and inspecting infrastructure) - Environment & public safety (monitoring the environment and improving disaster management strategies)	- Robots that automate routine tasks, help in healthcare and improve environmental and infrastructure monitoring^a - Drones that monitor security, control traffic, map disasters, inspect infrastructure, detect tax evasion and bring aid to remote areas^{a, b}	Robocop; Robotic Process Automation; R1, Graasdierpremie, LIDAR
VR & AR	- Medical (surgery simulations) & military training - Real estate & entertainment (powering virtual tours and enhancing gaming experiences) - Professional remote collaboration & engineering (advancing design and prototyping) - Interactive learning environments	- Training first responders and public servants^a - Supporting urban planning^c - Promoting civic engagement and preserving cultural heritage^{b, c}	EXR Telemetry; Living Labs Rome Reborn

Note. ^adimension of internal processes, ^bdimension of external processes/service delivery, ^cdimension of policy decision-making.

Source. This table is a compressed version of Supplemental Table A1.

There are some typical practical applications for the use of certain technologies in PA as presented in Table 4. AI is typically used to improve the performance and efficiency of PA services, both in terms of service delivery to citizens (chatbots, virtual assistants) and in conjunction with big data, including for automated decision making and public participation in public policy making. The detection of fraudulent activities and compliance with regulations, as well as the improvement of public safety through predictive policing and security surveillance systems are also important areas of application for AI in PA. The IoT is mainly used in PA for monitoring. It provides real-time data in the observed environment, which forms the basis for effective, timely responses and the introduction of data-driven public policies. It covers various government sectors, from environmental issues and energy to infrastructure and public safety, including emergency response. Blockchain is typically used in PA to secure and manage digital identities, secure tamper-proof records, and automate government processes using smart contracts, which improves the security of public digital services, especially in the areas of i-voting, public procurement, tax collection, intellectual property verification, etc. Robots and drones are primarily used in PA for government tasks such as emergency response, infrastructure inspections, environmental monitoring and the efficient delivery of public services. VR and AR are typically used in PA to improve public services by enabling virtual interactions with authorities, simulating complex procedures for employee training, preserving cultural heritage, and providing real-time information data to citizens.

Discussion

The results of the analysis of relevant scientific production show an increasing interest of researchers in DT and PA, especially after 2017, but on the other hand point to the importance of greater attention to the socio-political and economic contexts in which DT studies are conducted, especially given the decline in scientific production after the peak in 2019, probably influenced by the Covid-19 pandemic, and China’s global position and its social and technological changes (Păvăloaia & Necula, 2023). As the results show that China, the United States, and the United Kingdom are leading in DT research while other regions remain underrepresented, this discrepancy raises critical questions about global equity in technology adoption and research capacity. Furthermore, the results show that the focus of research in this area is on AI among all technologies when looking at both the number of published papers and citation criteria. While this reflects the potential of AI to drive innovation in PA (Kuziemski & Misuraca, 2020), it also raises concerns about the uneven distribution of research across DT, which leads to missed opportunities to investigate the broader transformative impact of less researched technologies, such as citizen engagement through VR or process optimization through the use of drones (Baker et al., 2023; European Commission [EC], 2024). The fact that few of the most cited papers examine these DT (e.g., Kim et al., 2021; Van Pinxteren et al., 2019; Xia et al., 2022) suggests that future research should aim to broaden the scope and critically assess the impact of these technologies on PA. Furthermore, the excessive focus on certain “mainstream” technologies such as AI suggests a potential bias in policy-making that results in the under-representation of societal problems of other DT.

The overlaps between the different technologies not only indicate a technical convergence, but also show the increasingly hybrid character of DT conceptualization, adoption and integration in PA. The overlaps suggest that DT do not function in isolation, but are part of an interconnected digital ecosystem in which multiple technologies work together to enhance and amplify their impact, which happens through a dynamic integration of people, processes, organizations and data (Casalino et al., 2020). Specifically, the significant overlaps between AI and IoT and AI and blockchain point to a trend toward integrated technological solutions that improve data-driven decision-making, automation and transparency. This offers opportunities for improved public service delivery, such as real-time monitoring and response systems, but also requires robust governance frameworks to manage the vast amounts of data these technologies generate, especially when interacting with big data. The ability to analyze, manage, and secure this data is critical, especially when integrated solutions such as blockchain, IoT, and AI are incorporated into critical functions such as managing public records or providing public safety, as Dagher et al. (2018) point out. Secondly, the overlap between blockchain and IoT shows the potential for creating more transparent and secure public services, particularly in areas such as secure electronic voting, land registries and identity verification (Baudier et al., 2021; Esposito et al., 2021; Kundu, 2019). This has clear implications for the integrity of public services and the protection of citizens’ data. Governments need to invest not only in these DT, but also in the regulatory and cybersecurity framework that ensures their safe and ethical operation. On the other hand, the lack of overlap between VR/AR and other DT, raises questions about the uptake of these applications, suggesting that their practical impact in PA remains isolated or underdeveloped. This signals to policy makers that more targeted investment and development of VR/AR is needed, for example, to engage citizens in co-designing public services, as highlighted by Bricout et al. (2021), particularly with regard to urban planning and emergency response, where immersive simulations can enable better decision-making. Furthermore, the extensive overlap of DT in the private sector compared to the PA illustrates that PA is lagging behind in the comprehensive adoption of these technologies (Zuiderwijk et al., 2021). This underscores the need for PA to accelerate their efforts to explore these technologies in order not to be left behind in the digital age. To achieve this, PA should favor an ecosystemic approach to digital technology adoption and recognize that integrated solutions, rather than isolated technologies, offer the greatest transformational potential by linking the transformational potential of digital technology to the cultural, ethical and societal determinants of the respective environment.

While binary logistic regression analysis reveals meaningful relationships between certain governance dimensions and DT, such as the association of transparency with blockchain and the relationship between decision-making and AI, it also reveals gaps, particularly in relation to UN good governance principles such as efficiency and effectiveness, equity, accountability and the rule of law (Sheng, 2009). The absence of these basic principles as statistically significant predictors is surprising given their central role in PA, but it does prompt reflection on how DT is conceptualized and implemented within PA. One likely reason for the lack of predictors is the overlap of different DT in practice. PA rarely utilizes a single technology; instead, AI, blockchain, IoT and others work together, making it difficult to attribute the outcomes of good governance principles to a specific technology. Furthermore, the fact that the regression analysis did not reveal negative values for these principles across technologies suggests that the core PA principles reflecting public values are relevant to all DT, but not as differentiating factors. Therefore, the use of DT in PA should not be determined solely by their potential to increase operational efficiency or introduce innovation. Instead, the broader impact of these technologies on fundamental governance principles must be critically assessed, highlighting the need for a more principled approach to DT adoption in PA (Floridi et al., 2018). The findings suggest that regardless of the specific technology being implemented, governments must remain vigilant to ensure that these core values are upheld (Madan & Ashok, 2023).

The analysis of practical applications highlights that different technologies play different roles in PA, such as AI in automation and analytics, which contrasts with the role of IoT in real-time monitoring and infrastructure management. This requires a context-specific approach to technology adoption. Rather than choosing DT for their novelty, PA should focus on specific governance issues and select the technology that best meets those needs to ensure a targeted and effective DT implementation. As different sectors within PA face unique challenges that require tailored technological solutions, sector-specific expertise is required to manage and optimize the use of DT. As Fetais et al. (2022) point out, this should include dedicated training programs for public servants and collaboration with relevant stakeholders to ensure that deployed solutions are tailored to the specific needs of the sector. Furthermore, as the true potential of DT lies not in their isolated use but in their interoperability and combined impact, governments should prioritize the creation of systems that allow these technologies to work seamlessly together to maximize the benefits resulting from their integration. On the other hand, this widespread integration also raises significant ethical, legal and societal concerns, particularly in relation to algorithmic bias, transparency, accountability and privacy (Taeihagh, 2021; Wirtz et al., 2020). Robust frameworks need to be put in place to regulate the use of DT in PA and ensure that fundamental human rights and principles of good governance are respected. In addition, the PA must strike a balance between internal and external processes and ensure that the pursuit of operational efficiency does not come at the expense of external service quality and public value creation. The ability of these technologies to support more personalized and accessible public services, especially for marginalized or underserved populations, should be an important aspect of ensuring government-citizen interaction and shaping more effective public policy (Androutsopoulou et al., 2019; Dwivedi et al., 2021). Finally, the dichotomy between internal and external processes underlines the importance of public value as a guiding principle for DT adoption. As Desouza et al. (2020) emphasize, technological solutions in PA must serve the public good and create public value that goes beyond simple cost and efficiency gains. By promoting a more inclusive approach to DT integration, PA can ensure that the technologies deployed meet the needs of all stakeholders and reflect broader societal goals (Chen et al., 2023).

Conclusion

Rapid technological progress and unpredictable societal conditions require effective utilization of the potential offered by digital transformation to create public value for government services. However, due to the lack of domain-specific studies (Kankanhalli et al., 2019) on the integration of these technologies into PA, this potential is not optimally utilized, resulting in under-implementation of DT solutions, especially AI, in this sector. This paper fills this research gap by categorizing DT, especially AI, in PA, considering current research trends, and providing a comprehensive overview of the practical applications of these technologies in different countries and sectors.

The extensive literature review of existing bibliometric and other studies dealing with DT in the public sector and PA reveals a significant lack of all-encompassing, in-depth analyzes of DT in this area. The existing studies vary in scope and often analyze only one technology or a combination of some, but not all, technologies. However, the literature review has identified the main sources, authors and relevant papers, as well as some key research directions. In addition, the existing studies mainly focused on the post-2020 period, which makes them highly relevant and applicable to the present given the rapidly changing technologies combined with the fast-paced societal challenges.

The results of this study point to an increasing focus on DT in the field of PA, which can be observed especially after 2017 and has been significantly driven by publications in renowned journals such as Sustainable Cities and Society, Government Information Quarterly, Sustainability and Technological Forecasting and Social Change. First and foremost, research initiatives have been pursued in China, followed by the United States, India and the United Kingdom, with the United States proving to be the most influential country based on citation metrics.

Most of the existing studies offer only partial views on the use of DT in the public sector and PA and therefore do not allow for a comprehensive categorization of these technologies within the PA framework. Therefore, this paper highlights both theoretical and practical potential trends in the development of DT in PA, which technologically follows the private sector, especially from the perspective of the intersection of specific technologies and their concrete application outcomes, and provides a comprehensive and concise study on DT in PA with a special focus on AI, enriched by the content analysis of the most prominent documents analyzing use cases in this field. In this context, it should be noted that this development also depends largely on the challenges faced by PA in the use of these technologies. From the analysis carried out, it can be concluded that AI occupies a central place among DT in PA and mostly overlaps with big data. The use of AI and its integration with other technologies leads to more efficient, personalized and data-driven public services as a result of public value. The use and integration of AI in PA is now relatively widespread and includes a range of applications such as predictive analytics, decision support systems, decision assistance, video surveillance systems and intelligent transportation systems, etc. (Valle-Cruz et al., 2024).

Surprisingly, the binary regression analysis of the predictive model did not identify some important principles, functions and impacts of DT as predictors, such as efficiency, effectiveness, equity, accountability, rule of law, etc. This can be attributed to the identified overlaps of different DT, as we can rarely speak of the “pure” use of only one emerging technology in PA. Also, the fact that the regression analysis did not yield any negative values for those preditors indicates that the mentioned terms occur equally in relation to all technologies, or more precisely, that they do not make statistically significant predictions for scientific articles related to any of these technologies. This further emphasizes the importance of the principles of PA as a fundamental guideline for the use of DT (Floridi et al., 2018).

This study has some limitations that need to be taken into account. First, the bibliometric analysis is based exclusively on documents indexed in the Scopus database. Although Scopus is widely recognized as one of the largest databases of peer-reviewed literature, it may not cover all research on DT in PA. Therefore, using additional databases such as Google Scholar or Web of Science could potentially provide further insights not captured here. Secondly, many analyzes within the bibliometric analysis refer to the keywords of the scientific papers. As these are determined by the authors themselves, there may be inaccuracies, particularly with regard to keywords that are not related to the content of the paper itself, or too much or too little emphasis on certain keywords. Including a broader range of content from scientific articles could mitigate this limitation. Third, the use cases analyzed are limited to relevant documents from prominent institutions dealing with DT in the public domain, such as the World Bank, the OECD, the European Commission or various consulting firms. It is possible that extending the analysis to documents from other institutions could reveal additional relevant practical applications of these technologies that are beyond the scope of this article.

Notwithstanding these limitations, this article aims to benefit both academics and practitioners by providing insightful theoretical perspectives and practical guidance on the application of digital technologies, particularly AI, in PA in different global regions. The findings suggest a significant impact on PA by demonstrating how AI and other emerging technologies can improve decision making, streamline operations, and promote personalization of public services. For researchers, the need for further studies that develop a comprehensive and robust framework linking DT to public values, ethical considerations, transparency and regulatory requirements, with a focus on their impact on governance structures, becomes clear. For policy makers and practitioners, the paper provides a practical overview of DT applications to improve efficiency, effectiveness and accountability within PA. The findings suggest that integrating DT into PA has the potential to address demanding challenges in meeting citizens’ needs. By emphasizing the practical implications of these technologies, this study seeks to bridge the gap between theoretical knowledge and practical implementation and encourage greater collaboration between academia and PA to ensure more responsive, transparent, and sustainable government systems.

Supplemental Material

sj-docx-1-sgo-10.1177_21582440251335516 – Supplemental material for Mapping the Adoption of Disruptive Technologies in Public Administration: A Bibliometric Analysis and Review of Practical Applications

Supplemental material, sj-docx-1-sgo-10.1177_21582440251335516 for Mapping the Adoption of Disruptive Technologies in Public Administration: A Bibliometric Analysis and Review of Practical Applications by Matej Babšek, Dejan Ravšelj, Lan Umek and Aleksander Aristovnik in SAGE Open

Footnotes

Acknowledgements

We would like to thank the anonymous reviewers and the editor for their valuable suggestions and comments. Moreover, we acknowledge the financial support from the Slovenian Research and Innovation Agency (research core funding No. P5-0093, project No. J5-50165 and No. J5-50183). In the preparation of this manuscript, the authors utilized ChatGPT, version 40, developed by OpenAI, for limited and supplementary purposes. Specifically, ChatGPT was employed to assist with grammar checks, enhancing clarity, and language polishing in certain sections of the manuscript. It is crucial to emphasize that the role of the ChatGPT was minor and purely supportive in nature. The core content of the manuscript, including all scientific interpretations, conclusions, and critical revisions, was exclusively conducted by the human authors. The ChatGPT did not contribute to the intellectual content or scientific insights of the manuscript.

ORCID iD

Matej Babšek

Dejan Ravšelj

Lan Umek

Aleksander Aristovnik

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research and the APC were funded by the Slovenian Research and Innovation Agency (research core funding No. P5-0093, project No. J5-50165 and project No. J5-50183).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

Not applicable.

Supplemental Material

Supplemental material for this article is available online.

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