The scientific literature on artificial intelligence for public administration

According to Zuiderwijk et al. (2021), scholars have been intrigued by AI research for many years (Natale & Ballatore, 2020; Wirtz & Müller, 2019), with some areas within AI having a well-established background (Desouza, Dawson, & Chenok, 2020), including expert systems (Hurley & Wallace, 1986), agent-based systems (Oliveira & Cardozo, 1977), algorithms (Horowitz & Sahni, 1978; Lynch, 1996), and chatbots (Shawar & Atwell, 2003). Despite AI not being a new field of study, there has been a recent resurgence in interest due to the impressive advancements made (Aoki, 2020) and increased government focus (Kuziemski & Misuraca, 2020).

There is a limited understanding of the unique AI challenges faced by the public sector compared to the private sector. Much of the existing AI research is focused on technical issues and computer science-based solutions (Aoki, 2020). While there are studies on the use of AI in government that extend beyond purely technical fields (e.g., Etscheid, 2019; Kankanhalli et al., 2019; Winter & Davidson, 2019), research on AI governance, policy, and regulation is scarce (Wang & Siau, 2018; Zuiderwijk et al., 2021).

According to the findings by Zuiderwijk et al. (2021), several research initiatives centred on AI usage in the public sector have already been established (e.g., Dwivedi et al., 2019; de Sousa et al., 2019; Kankanhalli et al., 2019). Some of these focus on utilizing AI technology within the public sector, such as Kankanhalli et al. (2019) exploring the combination of AI and the Internet of Things to create smart governments and de Sousa et al. (2019) presenting AI applications for the public sector, however, through a systematic literature review of 59 articles. The research agenda by Dwivedi et al. (2019) takes a broader approach, examining AI implementation across multiple industries, including business and management, government, the public sector, and science and technology.

Despite the recent but rich literature, the AI applications aspect has not been investigated from the public administration perspective. We were interested in providing an overview of the research production of AI themes considered in the literature so far in the public administration outlook, with a specific focus on the use of AI in PA.

According to the 526 documents that we identified in the Scopus database as being relevant for AI in PA research, the majority of the documents did not specify the type of AI technology used but were focused on general discussions on the opportunities, challenges, impacts, and consequences of AI, often with an emphasis on ethics and the development of fair AI. There were 161 documents with a specific focus on AI technology, of which 50.8% were focused on machine learning.

Source: Scopus database (161 documents).

Machine learning is a widely used AI technology that enables systems to learn, make decisions, and adapt on their own, improving over time without explicit programming and is also discussed in the literature on various applications in the public sector, such as fraud detection, energy efficiency management, and pandemic response.

The rest of the documents focused on artificial neural networks, deep learning, chatbots, and natural language processing (NLP). Deep learning is a term that is gaining traction in the literature and is associated with machine learning but at a deeper level of neural network layers. Chatbots are a popular AI trend in the private sector, used for automated communication and transaction management, and are becoming increasingly prevalent in the public sector. NLP is another subcategory of AI that focuses on the ability of systems to handle human language in written or spoken form. The articles on the use of chatbots in public administration explore their potential for improving communication between citizens and government, reducing response time, and increasing efficiency. The articles on NLP examine its applications in document processing, chatbots, and virtual assistants, with a focus on text generation, text mining, classification, and machine translation

In conclusion, the analysis of the scientific documents highlights the increasing literature on AI technologies, particularly machine learning (neural networks) and chatbots, in the public sector. However, there is a need for careful consideration of the ethical and practical implications of the deployment and management of AI in public administration with all AI technologies.

References

Aoki, N. (2020). An experimental study of public trust in AI chatbots in the public sector. Government Information Quarterly, 37(4), 101490.

de Sousa, W., Pereira de Melo, E. R., De Souza Bermejo, P. H., Sousa Farias, R. A., & Oliveira Gomes, A. (2019). How and where is artificial intelligence in the public sector going? A literature review and research agenda. Government Information Quarterly, 36(4), 101392.

Desouza, K. C., Dawson, G. S., & Chenok, D. (2020). Designing, developing, and deploying artificial intelligence systems: Lessons from and for the public sector. Business Horizons, 63(2), 205–213.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.

Etscheid, J. (2019). Artificial intelligence in public administration. In Paper presented at the international conference on electronic government, San Benedetto del Tronto, Italy.

Horowitz, E., & Sahni, S. (1978). Fundamentals of computer algorithms. Potomac: Computer Science Press.

Hurley, M. W., & Wallace, W. A. (1986). Expert systems as decision aids for public managers: An assessment of the technology and prototyping as a design strategy. Public Administration Review, 46, 563–571.

Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2019). IoT and AI for smart government: A research agenda. Government Information Quarterly, 36(2), 304-309.

Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications policy, 44(6), 101976.

Lynch, N. A. (1996). Distributed algorithms. San Francisco: Morgan Kaufmann Publishers, Inc.

Natale, S., & Ballatore, A. (2020). Imagining the thinking machine: Technological myths and the rise of artificial intelligence. Convergence, 26(1), 3–18.

Oliveira, P. C. D., & Cardozo, E. (1977). Mobile agent-based systems: An alternative paradigm for distributed systems development. In Paper presented at the Simposio Brasileiro de Redes de Computadores, Sao Carlos.

Shawar, B. A., & Atwell, E. (2003). A corpus-based approach to generalising a chatbot system. Procesamiento del Lenguaje Natural, 31.

Wang, W., & Siau, K. (2018). Artificial intelligence: A study on governance, policies, and regulations. In Paper presented at the 13th Annual Conference of the Midwest Association for Information Systems, Saint Louis, Missouri.

Winter, J. S., & Davidson, E. (2019). Governance of artificial intelligence and personal health information. In Digital policy, regulation and governance

Wirtz, B. W., & Müller, W. M. (2019). An integrated artificial intelligence framework for public management. Public Management Review, 21(7), 1076–1100.

Zuiderwijk, A., Chen, Y. C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 101577.

Applications of artificial intelligence cases in public organisations

The widespread use of artificial intelligence (AI) technology is being advanced by recent improvements in computational power, the exponential growth of (big) data, and new algorithmic techniques. AI has much potential to disrupt almost all industries, and the public sector is not excluded. On the contrary, it has been identified as one of the sectors where AI can have a more significant impact by improving public services, internal operations, decision-making, etc. and has the potential to provide, and in several cases is already providing, considerable benefits and public value to citizens.

Artificial intelligence encompasses various technologies such as machine learning, neural networks, natural language processing, etc. and can be defined as a technology for advanced prediction (Agrawal et al., 2017). AI technology identifies patterns in large amounts of data to predict outcomes for similar instances (Dwivedi et al., 2019). There is a growing interest in using AI in the public sector to re-design internal service delivery processes and policymaking (Misuraca & Van Noordt, 2020). Public sector and government organizations generate large amounts of data, making a lot of potential for AI technologies applications (Dwivedi et al., 2019). When used ethically, AI and big data sources can improve the public sector's operations by freeing up workers' cognitive resources for higher-value tasks (Eggers et al., 2017). AI has the potential to enhance public service quality, build citizens' trust, increase efficiency, and impact competitiveness and public value creation (Zuiderwijk et al., 2021). It can also improve communication and interaction between users and organisations, reduce time and costs, help allocate resources, and handle complex tasks (Criado & Gil-Garcia, 2019; Kankanhalli et al., 2019).

The potential of AI applications is not only suggested in the scientific literature but is also evident in several cases of use around the European Union (and also globally), with improving public services and administrative efficiency in 363 and 322 cases respectively, out of 686.

The largest category of AI cases in the European Union (according to Tangi et al., 2022) is based on Machine Learning (ML) techniques, in several different usages, reaching 58% of the cases. Automatic Reasoning is 30% overall. The number of cases using Natural Language Processing (NLP) technologies is also remarkable, standing at 24%.

General Public Services (30%), Economic Affairs (18%), Health (15%), and Public Order and Safety (14%), are the key governmental functions that are emerging. This is not unusual because public administrations typically play a significant role in the provision of general public services.

Few cases (9% and 4%, respectively) centre on social and environmental protection. Defence and Recreation, Culture, and Religion all have a dearth of AI cases. There may be a lack of transparency surrounding the use of these technologies in the defence sector due to safety and security concerns, or public administrations themselves may not be the leading implementing organisations in these policy areas as a result of historical outsourcing and/or increased involvement of private sector organisations (Tangi et al., 2022).

Source: Tangi et al. (2022).

To comprehend examples under this broad and inclusive category, it helps to have a more in-depth understanding of the General Public Service cases. The group contains:

• Chatbots and virtual assistants are used to speed up internal procedures in addition to interacting with customers and other businesses on the outside,
• the alerting, observation, and identification of various public locations by cameras, microphones, or other sensors,
• the classification, storage, and searching of documents (including handwritten ones), videos, and/or speeches with metadata and automatic information extraction,
• the classification, storage, and searching of documents (including handwritten ones), videos, and/or speeches with metadata and automatic information extraction,
• many methods for detecting data anomalies, or potential frauds (Tangi et al., 2022).

References

Agrawal, A., Gans, J., & Goldfarb, A. (2017). What to expect from artificial intelligence. MIT Sloan Management Review, 58(3), 22–27.

Criado, J. I., & Gil-Garcia, J. R. (2019). Creating public value through smart technologies and strategies: From digital services to artificial intelligence and beyond. International Journal of Public Sector Management.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.

Eggers, W. D., Schatsky, D., & Viechnicki, P. (2017). AI-augmented government. Using cognitive technologies to redesign public sector work. Deloitte.

Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2019). IoT and AI for smart government: A research agenda. Government Information Quarterly, 36(2), 304-309.

Misuraca, G. & van Noordt, C. (2020). Overview of the use and impact of AI in public services in the EU. EUR 30255 EN. Publications Office of the European Union, Luxembourg. Retrieved from https://publications.jrc.ec.europa.eu/repository/handle/JRC120399

Tangi L., van Noordt C., Combetto M., Gattwinkel D. & Pignatelli F. (2022). AI Watch. European Landscape on the Use of Artificial Intelligence by the Public Sector. EUR 31088 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92- 76-53058-9 , doi:10.2760/39336, JRC129301.

Zuiderwijk, A., Chen, Y. C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 101577.

Smart public governance at the organisational level – use cases of smart technology: Artificial Intelligence

Based on a comprehensive analysis, the members of the Hercules research group defined smart public governance as a "Modern approach to public governance that uses sophisticated information technologies to transform processes (interventions) between public administration and citizens with the aim of increasing collaboration, interaction, co-production, improve decision-making and to achieve results that meet the needs of citizens (that is generating public value) [1, 2, 3, 4].

From this, it follows that the adjective ‘smart’ refers to context- and site-embedded combinations of ICT, technology, innovation, and some sort of democratic aspect [5, 6]. Smart technologies have the potential to foster the co-creation of public services and the generation of public value in management processes based on the collaborative, social and horizontal nature of these smart technologies [1].

Defined as smart are also AI technologies, such as machine learning, rule-based systems, natural language processing, and speech recognition, and when adopted in the public sector, they carry potential implications for all aspects of government actions, including the inner workings of government agencies at the organisational level, the relationship between governments and citizens, and the role of governments as regulators [7]. In some cases, the integration of AI in the way public services are managed and delivered is already providing large benefits and public value to citizens.

The study by European Commission [8] includes an overview of 686 use cases of AI in the public sector organisations across all 27 EU Member States plus some other countries in Europe, characterised by different features and qualities. The high number of cases collected is an initial sign that AI is now more widespread in the public sector in all European countries. The figure shows the number of cases is growing significantly, and it is likely that in the future, more and more AI will be used in public administration organisations.

Source: Tangi et al. (2022).

The main results from the analysis show that a third of the cases were found to be implemented and used in daily operations, but many are still in the pilot or development phase. National governments are the ones that drive AI development because they have the human and financial capacity to sustain development. At the same time, a considerable number of AI initiatives are developed by regional and local administration organisations, demonstrating that regions, cities and municipalities – even small ones – can play a key role in pushing the development and usage of AI. Most AI deployed in the public sector supports public services engagement, and most AI use aims to improve the quality of public services or to improve administrative efficiency [8].

[1] Criado, J. I., & Gil-Garcia, J. R. (2019). Creating public value through smart technologies and strategies: From digital services to artificial intelligence and beyond. International Journal of Public Sector Management.

[2] Webster, C. W. R., & Leleux, C. (2018). Smart governance: Opportunities for technologically-mediated citizen co-production. Information Polity, 23(1), 95-110.

[3] Pereira, G. V., Parycek, P., Falco, E., & Kleinhans, R. (2018). Smart governance in the context of smart cities: A literature review. Information Polity, 23(2), 143-162.

[4] Gil-Garcia, J. R. (2012). Towards a smart State? Inter-agency collaboration, information integration, and beyond. Information Polity, 17(3-4), 269-280.

[5] Gil-Garcia, J. R., Helbig, N., & Ojo, A. (2014). Being smart: Emerging technologies and innovation in the public sector. Government information quarterly, 31, I1-I8.

[6] Scholl, H. J., & Scholl, M. C. (2014). Smart governance: A roadmap for research and practice. IConference 2014 proceedings.

[7] Eggers, W. D., Schatsky, D., & Viechnicki, P. (2017). AI-augmented government. Using cognitive technologies to redesign public sector work. Deloitte.

[8] Tangi L., van Noordt C., Combetto M., Gattwinkel D. & Pignatelli F. (2022). AI Watch. European Landscape on the Use of Artificial Intelligence by the Public Sector. EUR 31088 EN. Publications Office of the European Union, Luxembourg.

The role of smart public governance in achieving the sustainable development goals: A conceptual framework and empirical application

The annual meeting of the public administration academics, young researchers, practitioners and key partners, known as the EGPA conference, was held at the Institute of Social and Political Sciences from September 6 to 9. This year’s central theme was directed at public administration as a predisposition for the sustainable future of our societies. The conference featured numerous contributions that focused on the study of public policy issues with interrelated cultural, ethical, political, social, legal, economic, technological and natural science elements at the level of EU governance and between political sectors. Cooperation with civil society and interested stakeholders in political processes, which are increasingly focused on sustainable development goals, was also emphasised.

Within the working group for digital government, members of the project group presented a paper entitled: "The role of smart public governance in achieving the sustainable development goals: A conceptual framework and empirical application". In the conference paper, the working group presented several months of work, showing a comprehensive conceptual framework (see Figure 1 below). The conceptual framework consists of theoretically based elements, which have been recognised in numerous articles as relevant to three interrelated dimensions of the conceptual framework, namely (1) smart public governance (or SPG), (2) sustainable development goals (or SDGs) and smart environment. The originality of the conceptual framework presented by the working group for digital government is based on several improvements, which primarily promote innovation.

Source: Authors’ figure.

Furthermore, working group members empirically verified the conceptual framework using a two-stage methodological approach on a European sample of EU28 countries. The results showed the following:

(a) Performance of EU28 countries according to the dimensions of SPG, SDG, and smart environment (Figure 2).

The results show that, in terms of overall performance, Denmark, the United Kingdom, the Netherlands, Finland (especially due to SPG and smart environment) and Sweden (especially due to smart environment) are the best-performing countries. At the same time, Romania and Greece ranked at the bottom of the EU28 countries.

Also interesting is a more detailed review that reveals how successful the countries were according to each dimension of the conceptual framework. The results for the SPG dimension showed that the UK (mainly due to smart ICT infrastructure and smart human capital) and Denmark (mainly due to smart interaction) were the best-performing countries. On the other hand, we can see that Greece (especially due to smart ICT infrastructure and smart government) and Romania (especially due to smart interaction and smart people) landed again at the bottom of the EU28 country ranking.

According to the analysis, Sweden (mainly due to the ecological elements) and Denmark and the United Kingdom (mainly due to the technological and ecological elements) are the most successful countries within the smart environment dimension. On the other hand, we can see Romania (mainly due to economic and technological elements), Greece (mainly due to political elements) and Bulgaria (mainly due to ecological elements) ranked at the bottom of the EU28 countries.

Austria (especially due to its environmental pillar) and Denmark are the most successful countries within the SDG dimension. In contrast, Croatia (especially due to its social pillar) landed at the bottom of the EU28 ranking.

Source: Author’s figure.

(b) Correlation matrix (Figure 3)

The results of the correlation analysis showed that SPG (especially due to smart government, smart ICT infrastructure and smart interaction) is strongly related to the SDGs, especially the social and environmental pillars. The smart environment also plays an important role (especially due to ecological and technological elements). Other correlations are modest, for example, there is no evidence of a correlation between the economic pillar and others.

Source: Authors’ figure.

Bibliometric Analysis of Smart Public Governance Research: Smart City and Smart Government in Comparative Perspective

In June 2022, researchers from the Digital Government Working Group published an article entitled "Bibliometric Analysis of Smart Public Governance Research: Smart City and Smart Government in Comparative Perspective" in the journal "Social Sciences", which is the result of a one-year research framework. The purpose of the article was to comprehensively examine research on smart public governance (or SPG) in the context of smart government and smart cities in the last two decades. Consequently, the main emphasis of the article focused on four components, namely:

1. studying trends in publications and citations,
2. identifying the most relevant and influential countries, publications and writers,
3. study of the research focus, and
4. determining the occurrence of the concept of smartness related to compound terms in the publications of smart cities and smart government.

The results of the bibliometric analysis, which was carried out for the purpose of the article, showed the growth of smart public governance research in the observed twenty years (Figure 1). The article, which includes an extensive analysis of the development of the SPG concept over the past twenty years, is available here: Social Sciences | Free Full-Text | Bibliometric Analysis of Smart Public Governance Research: Smart City and Smart Government in Comparative Perspective (mdpi.com)

Definition of the concept of smart public governance

The working group members for digital government proposed a definition of the concept of smart public governance (or SPG) for the purpose of the project based on a comprehensive literature review. The latter is the result of several months of work, where members thoroughly reviewed the existing definitions of the SPG concept in the literature and identified the most frequently represented elements (Table 1). Based on a comprehensive analysis, the members defined smart public governance as: "Modern approach to public governance that uses sophisticated information technologies to transform processes (interventions) between public administration and citizens with the aim of increasing collaboration, interaction, co-production, improve decision-making and to achieve results that meet the needs of citizens (that is generating public value) [1,2,3,4].

Such an interpretation of the concept of smart public governance offers a broad perspective that includes both the importance of information-communication technologies and cooperation between the state and citizens and represents the basis for achieving results aimed at creating public value.

Source: Author’s own table.

[1] Criado, J.I.; Gil-Garcia, J.R. Creating public value through smart technologies and strategies: From digital services to artificial intelligence and beyond. Int. J. Public Sect. Manag 2019, 32(5), 438-450. [2] Webster, C. W. R.; Leleux, C. Smart governance: Opportunities for technological-ly-mediated citizen co-production. Inf. Polity 2018, 23(1), 95–110. [3] Pereira, G. V.; Parycek, P.; Falco, E.; Kleinhans, R. Smart governance in the context of smart cities: A literature review. Inf. Polity 2018, 23(2), 143–162. [4] Gil-Garcia, J. R. Towards a smart State? Inter-agency collaboration, information integration, and beyond. Inf. Polity 2012, 17(2012), 269–280.

Testing the conceptual framework for smart public governance at the national level

From June 2 to 4, the University of Bucharest hosted the 30th NISPAcee Conference (Network of Institutes and Schools of Public Administration in Central and Eastern Europe). "Crisis, Vulnerability and Resilience in Public Administration" was the conference's main theme, and organisers welcomed contributions that addressed the dual issue of public administration's vulnerability and resilience in light of recent events like the coronavirus crisis, risks in financial management markets, migration and demographic trends, and geopolitical instability.

As a part of the Digital government Working Group, the project team presented a paper, titled: »Smart Public Governance development at the national level: a conceptual framework«. Recent challenges related to globalization and digitalisation, further emphasized by the ongoing Covid-19 pandemic, have revealed several deficiencies in public administration, including public governance. Therefore, implementing smart technologies into public governance may provide effective and efficient support to day-to-day operations and long-term socio-economic challenges and, most importantly, significantly improve public administration performance and consequently public value. Therefore, it is not surprising that the concept of smart public governance is gaining increasing attention among researchers, policymakers, and practitioners worldwide. The main purpose of the paper was therefore to propose and present a conceptual framework which can act proactively as an enabler and driver of progress towards smart public governance, allowing for citizen centricity and achieving public value. The originality of the framework is based on several improvements, enabling an innovative evaluation of the most relevant (sub)elements related to smart public governance, public value, and a smart environment. A conceptual framework is based on theoretical foundations and is supplemented by secondary data obtained from various EU and OECD data sources.

The results of the paper reveal that the Scandinavian countries (such as Denmark and Finland), as well as Western European countries (such as the United Kingdom, the Netherlands and France) and Central European countries (such as Germany, Luxembourg, and Austria) perform best in terms of all three main dimensions, (sub) elements and measurable indicators. On the other hand, Eastern European countries (such as Romania, Poland, and the Czech Republic) and Mediterranean countries (such as Greece, Cyprus and Croatia) are at the very bottom of the scale (Figure 1).

Source: Murko & Vujković (2022, p. 9).

Figure 2 below shows the thermal map, which is a graphical representation of the correlation matrix and shows the interrelationships according to colour shades. As expected, the variables at levels 1 and 2 (see news 15.04.2022) are strong in correlation with the construction of the conceptual framework (see news 15.05.2022). Smart public governance is largely linked to all its variables, namely smart government (0.91), smart ICT infrastructure (0.83), smart interaction (0.74) and smart human capital (0.76). The same can be said for other levels 2 variables (i.e., public value and smart environment), which are also strongly related to their level 3 variables. However, more weakly related variables can also be observed on this heat map. Smart interaction is one of the variables of smart public governance and has a weak correlation with the public value variable, namely trust (0.26). Furthermore, the confidence variable is poorly correlated with demographics (0.23), which is a smart environment variable. There is also an extremely weak correlation between the public value variable, i.e., the result, and the two smart environment variables, namely legislation (0.26) and demography (0.17).

Source: Murko & Vujković (2022, p. 10).

Presentation of a conceptual framework for citizen-centred smart public governance at the national level

Academic researchers use theoretical lenses, theoretical grammars, conceptual frameworks, roadmaps, and a variety of other comparable conceptions and descriptive phrases, such as conceptualizing and theorizing are used nowadays by academic researchers to explain the starting point, main focus, and/or outcome of an academic report. The majority of such frameworks and concepts appear to choose the latter two approaches: developing and presenting conceptual/theoretical frameworks and roadmaps rather than using them as a vehicle for presenting empirical findings (Estavez et al., 2021).

The project team Hercules constructed a conceptual framework which show the interplay between three primary elements namely, smart public governance, public value, and the smart environment.

Smart Public Governance: The importance of the context of smart public governance is becoming increasingly apparent in publications addressing smart cities. This isn't the case in publications on smart states. Whether the concept of smart public governance is applied to the state or the city, systematic examination of its role is scarce (Meijer et al., 2016). After reviewing the relevant literature, we note that some scholars (Lin 2018; Šiugždiniene et al., 2017; Bolivar & Meijer, 2015; Scholl & Scholl, 2014) have already tried to classify the concept into (sub)elements when developing pioneering conceptual frameworks. Nevertheless, there is no consensus among the scholars in the scientific literature regarding which characteristics define smart public governance and how significantly they influence it.

A review of the literature on smart public governance has led us to the conclusion that if we want to achieve smart public governance, we must consider four (sub)elements, namely (1) smart government, (2) smart ICT infrastructure, (3) smart interaction, and (4) smart human capital. All (sub)elements are equally important in our conceptual framework, and their synergy leads to the successful establishment of smart public governance.

Smart Public Governance Principles: After analysing the scientific literature, we discovered that scientists have yet to identify the principles of smart public governance. Nevertheless, Gil-Garcia and co-authors (2016) came the closest to identifying such principles in their research. They proposed a framework for understanding and measuring government smartness and proposals for smart government development as a whole.

On the other hand, ideas of good public governance are better documented in the present literature. International organisations worldwide were actively applying the concept of good public governance by the end of the twentieth century, both in specific policy areas such as international environmental legislation and in a broader policy context. The need for good public governance is thus much larger today than it was twenty years ago, and its implementation can be seen at national, regional, and global levels, where good governance principles have been further developed. In light of the diverse roles of national authorities, implementation of good public governance in the EU Member States has also been addressed through interpretation and application (Addink, 2019a; 2019b).

By analysing the literature on the principles of good public governance and by attempting to understand smartness, we were, for our conceptual framework, able to emphasise the principles that, in our opinion, impact the development of smart public governance at the national level. We suggest nine principles of smart public governance based on what has been stated, namely (1) innovation, (2) integration, (3) trust, (4) intelligence, (5) openness, (6) collaboration, (7) participation, (8) citizen engagement, and (9) citizen centricity.

Public value: Traditional public administration has offered a unique set of government solutions in the past. In addition, it relied heavily on Weber's perception of the world. Later, we may observe in the scientific literature a transition to the new public management paradigm, which somehow deconstructed the Weberian paradigm of the bureaucratic pillar of traditional public administration. Over time, the paradigm of new public management has been supplanted in the theory and practice of public administration.

Today, there is a growing emphasis on public value in the scientific literature. Thus, according to some writers, public value has become a newly emerging governance paradigm. It does not seek to limit policy, as traditional public administration and the new public management have done, but considers it a key management task (Stoker, 2006; Smith, 2003; Goss, 2001; Moore, 1995). Kelly, Mulgan, and Muers (2002) recognised three fundamental components of public value: services, outcomes, and government trust, legitimacy, and confidence. Stoker (2006) recently attempted to outline a public value management model, an "alternative paradigm," or "generic framework" for post competitive, collaborative network forms of governance based mainly on Moore’s and Kelly et al. (2002) research.

In our conceptual framework, public value is a result that can be obtained only if the smart environment enables it and is especially essential if smart public governance is oriented toward citizens. As a result, a citizen-centric approach is critical for establishing public value. However, to focus on citizens and achieve a goal, such as the public value, our conceptual framework includes three major (sub)elements that must be adjusted, namely (1) services, (2) outcomes, and (3) trust.

Smart Environment: We live in an age of rapidly evolving smart technologies that change our environment and make it more interactive and informative (Gubbi et al., 2013). The development of smart technologies has been primarily strongly influenced by digitalisation. The convergence of these technologies and improved availability has further sparked interest in creating smart environments (Rashidi & Holder, 2011). The identification of general (sub)elements that lead to the development of a smart environment has thus become a fairly evolving topic in the existing literature. The consideration of smart environment is most frequently seen in the literature on smart cities (Zhuang, Lu, & Huang, 2017; Caragliu, Del-Bo, & Nijkamp, 2013; Giffinger et al., 2007). As a result, smart environments are frequently mentioned in these contributions as a potential for users to collaborate and interact with their immediate surroundings seamlessly. Technological advancements and the advent of smart technologies, as well as services, have made this possible.

In our conceptual framework, the development of a smart environment at the national level consists of five sub-elements, namely (1) economics, (2) technology, (3) legislation, (4) demography, (5) politics, and (6) ecology. These are categories that Rainey (2014) already recognized as important in his work. Taking the (sub)elements into account, the state is further able to create a smart environment that impacts smart public governance and creates a citizen-centric orientation of public value.

Development of a conceptual framework for citizen-centred smart public governance at the national level

The Hercules project carried out activities to construct a conceptual framework for citizen-centred smart public governance (CSPG) at the national level. After conducting a comprehensive review of relevant social science literature, the research group members gained a suitable theoretical basis for developing an organisational chart (Figure 1), the first stage before further building a conceptual framework.

The organisational chart graphically depicts the components of the conceptual framework at various levels and their formal links. The different elements of the organisation chart are colour-coded; green fields represent smart public governance, orange fields represent public value, and blue fields represent smart environment. The grey field depicts the interaction of the three primary elements indicated above, or their combined performance at level one. This is followed by the second level, divided into three parts – the primary elements (i.e., smart public governance, public value, and smart environment). For each primary element, the third level lists the (sub)elements that should be implemented if we want to achieve overall performance (i.e., level one). At the same time, they are further elaborated on the fourth level. This gives rise to the fifth level (indicators) by which institutions can periodically measure the effectiveness of primary elements.

The project team will keep developing a conceptual framework for citizen-centred smart public governance.

From Neo-Weberian to Hybrid Governance Models in Public Administration: Differences between State and Local Self-Government

The Hercules research team published a new article on public governance models and differences between state and local self-government. The presence of smart public governance model characteristics can be seen as an intersection between Digital-Era Governance and Hybrid governance models when observing the specific principles. We aimed to provide an overview of different public governance models, extract quantifiable elements based on models’ principles and examine the extent of layering of different governance models at different levels of public administration in the specific case of the Slovenian administration. Thus, the main focus is on identifying the differences in characteristics of public governance practices between state administration and local self-government.

The first phase involved identifying appropriate items that best reflect the characteristics of the corresponding public governance model, i.e., the Neo-Weberian model (WEB), New Public Management (NPM), Good Governance (GG), Digital-Era Governance (DEG), and the Hybrid model (HYB), which have the nature of latent constructs. In order to provide sufficient coverage of the constructs’ theoretical domain, the five most relevant items for each public governance model were identified. See Table 1 for selected items for DEG and Hybrid model. Accordingly, it was possible to proceed with the second phase of calculating mean values for each public governance model. Finally, in order to identify differences in mean values between state administration and local self-government, an independent samples t-test was performed.

Source: Aristovnik et al., 2022.

The results show that significant differences in public governance practices exist between state administration (M = 3.67; SD = 0.71) and local self-government (M = 4.07; SD = 0.59) for Digital-Era Governance, indicating the latter public governance model elements are more prominent in institutions on the local self-government level. However, despite local self-government achieving higher average values than state administration for the Hybrid model (M = 4.02; SD = 0.47 and M = 3.92; SD = 0.57, respectively), the results suggest there is no significant difference between these two segments of Slovenian public administration.

Differences between state administration and local self-government across the public governance models may be observed because local administrations tend to be more creative, receptive, and innovative. Contemporary local governance exceeds the legal aspects with its dedication to local users, solving their problems and fostering the community’s socio-economic development. Indicating, besides authoritative decision-making, local self-government institutions strengthen their legitimacy by providing quality public services and promoting democracy with public participation and co-decision-making on the local level, namely, utilising the main principles of Good Governance, the New Public Management model, Digital-Era Governance and even the Hybrid models.

Covid-19 pandemic and disruptive technologies across scientific areas: A bibliometric review

The 2021 International Conference on Digital Science (DSIC 2021) took place in Protaras, Cyprus from 15 to 16 October 2021. The organisers also enabled the possibility of on-line participation for all those authors who could not attend the conference in person. DSIC 2021 is an international forum for researchers and practitioners to present and discuss the most recent innovations, trends, results, experiences and concerns in the several advances of Digital Science.

As a part of the Digital Technology & Applied Sciences group we presented our paper, titled “Covid-19 pandemic and disruptive technologies across scientific areas: A bibliometric review”. Namely, disruptive technologies have been recognized as a key facilitator of the Covid-19 pandemic response and recovery efforts. Therefore, they have recently gained increased attention. However, due to the novelty of this research area, there is the lack of knowledge. Therefore, the main aim of the paper was to a provide bibliometric analysis on Covid-19 and disruptive technologies research with the focus on their application across different scientific areas. Bibliometric analysis was based on the Scopus database that contains 2353 documents published until July 2021. In this context, several innovative bibliometric approaches were applied.

The results show that most of the research has been conducted within Physical Sciences, while Health Sciences are identified to have the most prominent scientific impact, as further confirmed of most relevant documents being more frequently cited compared to other scientific areas. Further, the highest authors’ collaboration is observed for Life Sciences and Health Sciences, while Social Sciences exhibit the lowest authors’ collaboration. Moreover, the IEEE Access from Physical Sciences is identified as the most relevant source, while Health Informatics from Multidisciplinary Sciences as the most relevant research field. Finally, artificial intelligence (AI) is by far the most applied disruptive technology across all scientific areas, especially in the context of Life Sciences. Contrary, 3D printing, augmented reality (AR), and drones seem to be not so relevant in the general Covid-19 related context, particularly in Health Sciences and Life Sciences (see Figure 1). The findings of the paper add to the existing scientific knowledge and facilitate evidence-based policymaking.

Source: Aristovnik et al., 2021.

Mapping thematic trends and analysing hotspots in smart public governance research: A bibliometric analysis

The European Group for Public Administration has organized an annual conference. The EGPA conference is the annual meeting of a community of public administration academics, young researchers, and practitioners in Europe and key EGPA/IIAS partners. The conference took place in Brussels, Belgium from 7 to 10 September 2021. The conference was organised in hybrid form. As participants’ safety is organizers’ top priority, they also enabled the possibility of on-line participation for all those authors who could not attend the conference in person.

Our research group presented a bibliometric analysis within the e-government section, with the title: »Mapping thematic trends and analysing hotspots in smart public governance research: A bibliometric analysis«. The field of smart public governance is an emerging field. As researchers note already since the beginning of the 21st century, governments, and societies around the world have been faced with a dynamic economic and social pressures, and the complex challenges associated with public policies that come from the diversity and the complexity of the social objectives. These challenges need to be addressed and responded to quickly by the public governance system, that is, the smart public governance.

The main aim of the paper was to show how the research field of smart public governance has developed over time (see Figure 1), which topics have been most researched, which have been the most active, authors, to which articles most attention has been paid.

We find that regardless of the growing number of scientific papers in the field of smart public governance since 2013, the very concept of smart public governance in these papers is rarely defined. Furthermore, we note that within the research in the field of smart public governance, the studies related to smart cities are predominant, but (especially in recent years) the studies related to smart government are coming to attention. Researchers who pay the most attention to smart public governance come from English-speaking countries (e.g., the United States, the United Kingdom and Ireland). They publish most of their scientific papers in scientific journals such as the Government Information Quarterly, Information Polity, Cities, Sustainability (Switzerland) and Sustainable Cities and Society. In the last two decades, research on smart public governance has focused on topics such as citizen engagement, citizen centricity, e-services, sustainability, urbanization, and technological savvy.

Source: ARISTOVNIK, Aleksander, RAVŠELJ, Dejan, UMEK, Lan, VUJKOVIĆ, Petra. Mapping thematic trends and analysing hotspots in smart public governance research: a bibliometric analysis. V: EGPA Conference, Brussels, 7-10 September 2021 : conference du GEAP : resilience and agility in public institutions in times of crises. Brussels: EGPA. 2021, str. 1-28.  https://egpa-conference2021.org/index.php. [COBISS.SI-ID 76541955]

The most important articles in the field of smart public governance (SPG) in the period 1999-2021

As part of the bibliometric analysis of the field of smart public governance (SPG) for the period 1999-2021, the research group identified the most important articles related to the concept of smart city and smart governance. The presentation in Table 1 shows that the most cited articles in the field of SPG were published only in the last decade.

Within the literature on smart cities, six highly cited articles with more than 250 citations were identified. The most cited work in the literature is an article by Kitchin (2014), which has a total of 1,052 citations. The article refers to a critical analysis of existing projects at the local level that focus on the development of smart cities and the impact on big data. Journals that publish highly cited articles in the field of smart cities are GeoJournal, International Review of Administrative Sciences, Sustainable Cities and Society, Cambridge Journal of Regions and Environment and Planning D: Society and Space.

Subsequently, four highly cited articles with more than 40 citations were identified within the smart government literature. The most cited work in the literature is an article by Savoldelli et al. (2014), which has a total of 105 citations. In the article, the authors analyse in detail the adoption of e-government. Magazines that publish highly cited articles in the field of smart governance are Government Information Quarterly, Information Polity and Public Administration Review.

Preliminary analysis of research hotspots in smart public governance research

The research team conducted a preliminary keyword co-occurrence network analysis to identify research hotspots in smart public governance research. The analysis is based on the Scopus database. A literature search in early April 2021 resulted in 606 documents on smart or intelligent government or governance published between 2000 and 2021.

Figure 1 shows the keyword co-occurrence network for smart public governance research, where the nodes represent keywords and the links the co-occurrence relations between keywords. It is conducted on the 40 most frequent keywords by consolidating keywords that describe the same phenomenon (e.g., smart government or intelligent government, smart governance or intelligent governance, etc.). Note that the node size is in proportion to the number of keyword occurrences, the link width is in proportion to the co-occurrences between keywords, while the node colour indicates the cluster to which an individual keyword belongs.

The results reveal five clusters or research hotspots in smart governance research, namely:

1. smart city;
2. smart government;
3. e-government;
4. sustainable development; and
5. blockchain.

The latter was identified as the most recent and emerging research hotspot. Thus, smart public governance can be considered as an umbrella concept for many digital initiatives in the public administration on local and country level, fostering citizen orientation, sustainability, and disruptive technologies.

Conceptual positioning of the concept of smart governance to other concepts

In February, the project carried out activities bound to the placement of the concept of smart governance to other concepts recognized in the literature and their correlation. It has been identified that there is currently no universal denomination of the terms below in the literature and that they are still under development but have received in the last decade great attention from the researchers. Detailed positioning of the concept can be seen in Figure 1.

Dynamics of the number of documents and citations in the period 1999-2020 in the field of smart governance

In January, the project conducted activities that upgrade the existing theoretical starting points. A search query of concepts related to smart governance for the last two decades (1999-2020) has produced 509 units of scientific literature where the analysis found that the number of documents, as well as the number of citations in the first decade (1999-2008), was extremely low (only seven documents were recorded).

The first signs of moderate growth in documents and citations are seen in the first part of the second decade (2009-2012), with the largest growth recorded in the second part of the second decade (2013-2020), where growth increases by as much as 68 times compared to the first decade.