M EASURING S MART P UBLIC G OVERNANCE M ATURITY IN P UBLIC A DMINISTRATION I NSTITUTIONS : A M ULTI -A TTRIBUTE A PPROACH

The paper conceptualizes a multi-attribute model based on the design science approach for measuring smart public governance maturity in public administration institutions. This is achieved on the basis of a systematic literature review (i.e. content analysis) of Web of Science and Scopus records. The SPG attributes or criteria elicited from these two databases are integrated into a decision support model, thus setting the layout of a multi-attribute model for measuring smart public governance maturity in public administration institutions. The final model conceptualized here consists of 29 attributes or criteria grouped into four categories: (ICT) -enabled governance innovation in the public sector, inter-organisational changes, changes in citizen-government interaction, and outcome–public value creation.

The paper conceptualizes a multi-attribute model based on the design science approach for measuring smart public governance maturity in public administration institutions.This is achieved on the basis of a systematic literature review (i.e.content analysis) of Web of Science and Scopus records.The SPG attributes or criteria elicited from these two databases are integrated into a decision support model, thus setting the layout of a multiattribute model for measuring smart public governance maturity in public administration institutions.The final model conceptualized here consists of 29 attributes or criteria grouped into four categories: (ICT)-enabled governance innovation in the public sector, inter-organisational changes, changes in citizengovernment interaction, and outcome-public value creation.

Introduction and Problem Definition
In this paper, we present a design of a multi-attribute model based on the design science approach for measuring smart public governance maturity in public administration institutions.The need for such a model emerges in a context where various governments and societies across the globe see a possibility to address rapidly changing socio-economic pressures and complex public policy problems by adopting the 'smart public governance' (SPG) concept (Šiugždiniene et al., 2017).However, the label SPG is a fuzzy concept (Lin, 2018; cf.Table 1 in Appendix for a selection of various SPG definitions), and the absence of a commonly accepted SPG definition makes measuring SPG difficult.For the purpose of this paper, the working definition of SPG is as follows: smart public governance is a process based on the use of ICT's or in today's times adoption of various smart "disruptive" technologies (e.g., big data, Internet of Things, and artificial intelligence) (Zhao & Zou, 2021;Zhu & Kou, 2019;Jiang et al., 2019;Pereira et al., 2018) to upgrade (modernise) traditional (bureaucratic or hierarchical) administrative systems (e.g., institutional change -dismantling old department/institutional silos) (Meuleman, 2021;Scott & Gong, 2021;Hansen, 2014;Bannister, 2001), involving new forms of multi-actor collaboration and participation (Popova & Popovs, 2023;Šiugždiniene et al., 2017;Bolivar & Meijer, 2016;Willke, 2007) in decision-making processes (Jiang et al., 2022;Örselli et al., 2022;Demirel & Mülazımoglu, 2021;Scholl & AlAwadhi, 2016), with a focus on outcomes (e.g., creating public value) (Criado & Gil-Garcia 2019;Webster & Leleux, 2018;Albino et al., 2015;Batagan 2011;Gil-Garcia, 2012).Such a holistic definition affirms that SPG must be seen as a transformative and socio-technical governance approach (Jucevicius & Juceviciene, 2018) and not solely techno-centric focused.
Over the last decade, scholars in the smart city (SC) research domain have increasingly turned their attention to SPG, referring to smart city governance (SCG), smart urban governance, and/or smart local governance (e.g., Jiang et al., 2022;Criado & Gil-Garcia, 2019;Pereira et al., 2018;Scholl & AlAwadhi, 2016;Meijer & Bolivar, 2015).However, SPG does not follow the same maturity process as the SC (Anthopoulos et al., 2021).Therefore, there's a gap in research regarding its application and maturity in broader governance contexts -especially in the context of public administration institutions.And, despite the sizeable opportunity of the SPG concept, challenges for public governance systems to become or be smart exceed the scope of their current capacities.A limited quantity of either theoretical frameworks, toolboxes or roadmaps and models to measure SPG have been put forward in literature by scholars (notably Ruijer et al., 2023;Lin, 2018;Šiugždiniene et al., 2017;Bolivar & Meijer, 2016;Scholl & Scholl, 2014).However, they are not directly useful (applied or practical) approaches for measuring the maturity of SPG.Therefore, a more holistic approach that identifies attributes (criteria) of SPG (as discussed in this paper) appears to be lacking.To provide the conceptual design for measuring SPG maturity in public administration institutions, this paper is guided by the following research question: which attributes (also criteria) correspond to the aspect (i.e. the subject/area) should be considered when measuring the maturity of SPG in public administration institutions?
By answering the research question, the paper endeavours to add clarity and rigor to the ongoing debate by proposing a practical tool, the multi-attribute model, designed to measure the maturity of SPG in public administration institutions.To provide an as clear as possible elaboration of our model's conceptual design, we have structured the paper into four sections.In the next section, we present the methodological framework based on content analysis of Web of Science and Scopus records.In addition, in the third section, we present the results of the content analysis studiesthe list of SPG attributes (criteria), which are used for the structure of the multiattribute model for measuring smart public governance maturity in public administration institutions.Finally, in the last section, we discuss open questions that need to be taken into consideration by future research.

Content analysis of the Web of Science (WoS) and Scopus records
In the search for an answer to the research question, a content analysis of the WoS and Scopus records was conducted in order to identify SPG attributes (criteria).
The list of attributes (criteria) for the SPG multi-attribute model was gathered from: • A structured literature review, i.e., content analysis of WoS and Scopus papers.The selection of papers has been performed on the basis of the following criteria: o time-span of the records: 8 years, between 2015 and 2023, o including terms (in title, abstract and keywords): "smart public governance" OR "smart governance" OR "smart city governance" OR "smart urban governance" OR "smart local governance" OR "smart public administration" AND "indic*" OR "meas*" OR "defin*" OR "tool" OR "empirical analysis" OR "model" OR "framework", o type: article, o written in the English language.
The content analysis focused on SPG and its associated/related terms.Despite representing different scopes of governance (e.g., state versus city or regional), these terms are often used synonymously (Vujković & Jukić, 2023).Restricting the analysis exclusively to records specifically referencing SPG would have excluded significant data about SPG attributes (criteria).
Using this criteria, 427 records were initially identified.After a thorough review to eliminate duplicates, 242 records remained.A screening process, which involved examining the introduction, literature review, and conclusions of each record, determined that 80.9% did not meet two crucial inclusion criteria -the paper needs to: • have definitions or interpretations of the following essential terms: smart public governance, smart city governance, smart urban governance, smart local governance, smart public administration, OR • coverage essential foundational components (building blocks), including attributes (criteria), indicators, elements, dimensions, and measurement tools (models, theoretical and conceptual frameworks).
Ultimately, 46 records (10.7%) qualified for in-depth analysis.This stage involved a second, detailed review of the full papers to extract definitions/interpretations of terms and a comprehensive list of SPG attributes for a multi-attribute model.On this basis, 29 subordinate and 13 single-parent SPG attributes (criteria) were identified.Table 1 presents a detailed list of the SPG attributes (criteria) and information about the source of each attribute.Ansell & Gash (2012, 2008)  Once the list of SPG attributes (criteria) was completed, we identified the common denominator of attributes (criteria), namely the subject/area (e.g., the role of ICT and emerging technologies, the evolving nature of data, internal structural and procedural adjustments, changes in public administration interactions with citizens, and outcomes).Hence, this aspect (i.e. the subject/area) was taken as the key criterion for the categorization of the SPG attributes (criteria) in the following four categories:

Changes in citizen-government interaction
• (ICT)-enabled governance innovation in the public sector; • inter-organisational changes; • changes in citizen-government interaction; • outcome -public value creation.

3
Preliminary Results and Discussion

(ICT)-enabled governance innovation in the public sector
In the general discourse on public administration (PA) transformation, the development of information and communication technology (ICT) is seen as an enabler or even a driver of (digital) transformation (Lindgren & Veenstra, 2018).
To discuss what this means in practice in the case of PA, 'current IT infrastructure, the use of new emerging "disruptive" technologies, data, privacy and cyber security' have been identified as attributes (criteria) of the first category '(ICT)-supported innovation in public sector'.
In the past, the public sector has lagged behind the private sector in ICT adoption (Wimmer et al., 2020;Ndou, 2004).Therefore, despite some technological advances in PA (both in research and in practice), knowledge of the actual effects of digitisation on changes in PA (resulting from the development and use of ICT) remains scarce.In this regard, Meijer (2014) noted that most scholars from the social science domain do not pay enough attention to the role of ICTs in PA.They seem to be rather reserved in this context, and research on the impact of ICTs on PA remained limited.
But the development of new emerging "disruptive" technologies -which have emerged in recent years -has forced PA to transform (Ronzhyn et al., 2019).
Examples of technologies that "disrupt"1 the traditional or bureaucratic (hierarchical) approach of public sector operations most commonly includes artificial intelligence (AI), machine learning and big data (Brennan et al., 2019).For public sector organisations, this means changes in internal structures and processes and changes in the way PA interact with citizens (Wimmer et al., 2020;Andersen et al., 2010).Changes in the public sector are, therefore, very much linked to the development of ICT.Although the use of ICT in PA is becoming more common, from the relatively simple automation of routine work by civil servants (screen-level bureaucrats) to more sophisticated and complex applications that support the performance of administrative tasks (Danziger & Andersen, 2002;Bovens & Zouridis, 2002), both scholars and practitioners commonly agree that the evidence of actual improvements in the performance of PA remains still rather scarce (Misuraca & Viscusi, 2015).
An important task of all PA organisations is the collection, processing, storage and sharing of information (Janssen et al., 2017;Janssen & Hoven, 2015).And, while public sector information is the main source of big data, PA is the main storehouse of such data.Citizens, in turn, play the role of big data generators (Abuljadail et al., 2023).It is important to note that the public sector had been collecting, storing, and processing data for at least a decade before the development of ICT.However, the rapid development of ICT (and the rise of emerging "disruptive" technologies) has increased the amount of this data, bringing new challenges to the public sector (Fredriksson et al., 2017).And while the private sector is making significant progress in the use and analysis of big data, the public sector seems to be falling behind once again (Rogge et al., 2017;Munne, 2016;Desouza & Jacob, 2014).
However, public sector stakeholders have already recognised that better use of big data would bring many benefits to the public sector (Klievink et al., 2017;Munne, 2016).Key ones include improving the efficiency and effectiveness of PA organisations, improving public service delivery and better support in data-driven decision-making (Pencheva et al., 2020;Okuyucu & Yavuz, 2020;Veenstra et al., 2019;Kim & Cho, 2017).However, the major problem that big data brings to the public sector seems to be its governance (Chen & Hsieh, 2014).PA organisations face the challenges of a lack of analysts who know how to process and analyse information in real-time and outdated technological equipment for processing and storing big data (Abuljadail et al., 2023;Sarker et al., 2018).In addition, the huge amounts of data collected within the public sector are typically fragmented (or localised) within PA institutions and their departments.And because PA organisations operate as separate departmental and functional bureaucratic units (silos), the fragmentation of data sources (and the related lack of data sharing) hinders the use of big data for modelling, real-time problem analysis and support for data-driven decision-making (Okuyucu & Yavuz, 2020;Janssen et al., 2017;Becker, 2016;Margel et al., 2016;Desouza & Jacob, 2014).
In addition, the rapid growth of ICT has changed (i.e., transformed) access to information.Whereas in the past, public records were available and not exactly accessible to the general public, today, we live in an era where data is freely accessible (Margel et al., 2016;Janssen & Hover, 2015).This shift towards open data enables new ways of collaboration between governmental and non-governmental stakeholders, as well as civil society (citizens), which in the digital age expects public services to be more efficient and PA organisations to be more responsive and transparent in their operations.However, despite the public sector opening up its databases to the public and thereby contributing to democratic principles, understanding the impact of open data on the public sector remains surprisingly narrow (Meijer et al., 2014).
Recently, breakthroughs in machine learning and the amount and availability of data have encouraged PA organisations to consider integrating (or adopting) AI into their processes and activities (Mergel et al., 2023;Craglia et al., 2018).Yet, although several fields are involved in AI research, very little is known about the use of AI in PA (Bailey & Barley, 2019).As Van Noordt and Misuraca (2022) note, a negligible number of public sector organisations in Europe have already started to use AI.And while this is partly due to the challenges of introducing new "disruptive" technologies into established work processes (Kolkman, 2020), Margetts and Dorobantu (2019), on the other hand, argue that PAs have not even begun to engage with AI in a comprehensive and consistent way.
The situation is different in the academic realm, where "optimism" prevails about the impact of AI on PA, as its implementation is expected to bring tangible improvements in the functioning of PA organisations.The biggest advances are expected to be in the large-scale automation of routine processes, more efficient delivery of public services, increased efficiency in data-driven decision-making and related improvements in public policymaking (Van Noordt & Misuraca, 2022;Veale & Brass, 2019;Mikalef et al., 2019).Indeed, AI technology in public policymaking follows the traditional public policy-making cycle, with the difference that public policymaking is data-based and policy decision-making, therefore, becomes more data-driven and based on better analytics that is accurate and less uncertain (Van Noordt & Misuraca, 2022;Vydra & Klievink, 2019).Key benefits of using AI in this context include not only increased efficiency and effectiveness of PA organisations but also increased legitimacy in public policymaking processes (Van Noordt & Misuraca, 2022;Pencheva et al., 2020).As AI also enables new ways of collaboration (participation) between government stakeholders and civil society (citizens), its use is expected to contribute to a more open and participatory policymaking process.Although we have discussed some promising theoretical perspectives on AI in PA, we should point out that there is currently still no solid empirical basis to support these theories (Van Noordt & Misuraca, 2022;Kuziemski & Misuraca, 2020).The problems and challenges associated with using AI often overshadow its benefits (Zuiderwijk et al., 2021).Theoretical studies on AI that dominate the public sector governance literature (Mergel et al., 2023), therefore, often focus on specific AI applications, such as the use of virtual assistants (e.g., chatbots/talkbot/interactive agent) that provide information and assistance to users of government platforms (Androutsopoulou et al., 2019).Or on discussions about the potential challenges that surround the adoption of AI, including gaps in the skills required and risks to data privacy and security (Campion et al., 2022).
Therefore, with the obvious development of ICT and the associated benefits, privacy and security concerns should not be overlooked (Margel et al., 2016;Bertot & Choi, 2013).Privacy and security are constantly in motion due to the development of emerging "disruptive" technologies (Romansky & Noninska, 2020).While privacy represents a fundamental value for maintaining democracy, unrestricted data collection can threaten democratic principles (Romansky & Noninska, 2020;Janssen & Hoven, 2015).In light of this, PA organisations must follow privacy and security policies that align with legislation when processing data collected from citizens (Angelopoulos et al., 2017).And if the fragmentation (or localisation) of the PA into separate departmental and functional units (silos), on the one hand, hinders the use of big data for modelling, real-time problem analysis and (policy) decision support in terms of privacy and security, it allows storing data in isolated departmentscommonly referred to as silos.This prevents unauthorised access by civil servants to all the information collected (i.e., citizen's data), further enhancing data security (Janssen & Hoven, 2015).It is true, however, that we live in a digital age where data can be accessed from many sources.The volume of information collected about individuals and organisations is changing "traditional" understandings of privacy and security.As a result, privacy and security are being transformed, with younger generations now revealing much more about their lives than older generations, which in turn leads to a changed perception of what is considered private and secure in the digital age (Janssen & Hoven, 2015).
Many PA organisations typically operate as traditional bureaucracies, organising their tasks by breaking down complex issues into simpler sub-problems.These subproblems are then handled by separate departmental and functional units (silos).
Civil servants usually remain confined within a single silo, which, while providing a structured work environment, can hinder effective collaboration both internally and externally.The latter is why the word 'silo' generally has a negative connotation (Meuleman, 2021;Scott & Gong, 2021;Hansen, 2014;Bannister, 2001).It is important to note that 'silo' is not limited only to physical structures (department) but can also refer to a mental attitude (Tett, 2014), leading to tunnel vision and resistance to change among civil servants who believe their approach is not only the best but also the sole solution to the problem (Meuleman, 2021).In Europe, recognising the issues arising from silos has been a driving force behind PA reforms (Scott, 2020;Navarra & Cornford, 2005).Therefore, structural change represents a shift from the traditional 'silo' approach, fostering the creation of connected organisational structures and promoting a culture of collaboration (passage of mental silos ʺstate of mindʺ).
Intra-organisational change is also linked in the literature to the streamlining (or improvement) of processes.It illustrates the response of public sector organisations to modernisation, including digitisation (i.e.changes, transformations or improvements implemented within the public organisation itself) and its consequences (i.e.innovations in the way public services are delivered, as well as changes in the way citizens interact with public sector organisations).When considering organisational change within the public sector, it is essential to highlight the significance of collaboration among different departments and organisations within the sector.This form of collaboration, known as internal collaboration, involves coordinated and cooperative efforts between various departments within an organisation (inter-departmental collaboration) or between different PA organisations (inter-institutional collaboration) (Pereira et al., 2017).
Similarly, Yahia et al. (2021) use the term "collaborative networks" to describe various forms of internal collaboration.The government (as executive authority) plays a crucial role in pursuing streamlining (or improving) these collaborative efforts.By establishing a digital environment or platforms, the government can facilitate and encourage mutual collaboration and the exchange of information and knowledge between PA organisations and their departments (Šiugzdiniene et al., 2017).As Bouckaert et al. (2010) pointed out over a decade ago, successful internal collaboration also hinges on adopting collaborative approaches and cohesive working methods among civil servants.Consequently, it is imperative to organise regular education and training programs for civil servants, particularly developing collaborative competencies (skills), as highlighted in recent literature (Šiugzdiniene et al., 2017).

Changes in citizen-government interaction
'Streamlining of external processes -collaboration and participation' has been identified as an attribute (criteria) of the third category of 'changes in citizengovernment interaction' (Šiugzdiniene et al., 2017).
External collaboration involves the participation of governmental and nongovernmental stakeholders, such as private stakeholders, academic institutions, and civil society (Ruhlandt, 2018;Pereira et al., 2018).External participation, therefore, refers to the participation of citizens -as individual forms of participation (citizens who are either individually participating) or in more or less organised groups (associative forms) (Meerkerk, 2019).External participation is considered a (dynamic) process (Ruhlandt, 2018;Bolivar & Meijer, 2016;Cano, 2014) that has been shaped or created by technology for a decade.It has enabled new ways of consultation and dialogue (or reflection) between government and citizens and changed (or transformed) citizen participation in consultation and decision-making processes (Cunha et al., 2013).
Collaboration between government and citizens can be initiated from both topdown (government-driven) and bottom-up (citizen-driven) approaches, as explained by Meerkerk (2019).Regarding top-down collaboration, the literature frequently discusses two primary directions, (i) direct involvement of citizens in the design, implementation, and decision-making processes of public policies, and (ii) cocreation of public services, where citizens take on roles as co-implementers, codesigners, or initiators (Voorberg et al., 2014;Bovaird et al., 2014).In the context of top-down collaboration, the critical factors are citizens' trust, willingness, and motivation to participate.
Conversely, bottom-up collaboration centres on community-led initiatives to address common needs.While Meerkerk (2019) emphasises the importance of facilitative leadership in this context, Šiugzdiniene et al. (2017) frame this process more regarding internal collaboration.According to them, facilitative leadership involves supporting and enabling public servants to develop and apply their skills and competencies.Leaders in this role act as "political entrepreneurs" by encouraging innovation, promoting new ideas, tolerating mistakes, facilitating dialogue, and fostering trust within their teams.Facilitate leadership -leaders act like policy entrepreneurs -they promote new ideas, encourage innovations, and build trust in the team 2.2.3.

Outcomes -creating public value
The concept of public value (PV) was initiated by Moore (1995), or rather by his idea of how to guide public managers in creating public value.His thinking was that public organisations should be equivalent to private organizations, where private managers create private (economic) value for their customers (Hartley et al., 2019;Benington & Moore, 2011).The symbol of this idea became a "strategic triangle", which helps public managers focus their attention on three complex issues they need to consider before committing themselves and their organizations to a particular course of action (Benington & Moore, 2011;Moore & Khagram, 2004): • first, what is the "public value" that the public organization wants to create (present to civil society)?• second, on which "sources of legitimacy and support" could they rely to empower the public organization to act and provide the resources needed to create this public value?• third, on which "operational capabilities" (including innovations and improvements) would the public organization rely to deliver the desired outcomes?
However, since Moore formulated his idea in the mid-1990s, it has understandably already been criticised.First, as Rhodes and Wanna (2007) note, it is not clear whether Moore is proposing a theoretical framework, a concept, or a strategic tool for public managers.Second, because Moore does not provide a definition of the term public value (but uses it anyway), some scholars argue that public value can, therefore, be considered as a paradigm (e.g.Stoker, 2006;Benington, 2005), concept (e.g. Kelly et al., 2002), model (O'Flynn, 2005), heuristic device or even a story (Smith, 2004).Third, Oakley et al. (2006) note that Moore sometimes appears to be talking about public goods and, at other times, about the public interest or even the public domain.Later, therefore, critically point out that public value is another "fuzzy" term which seems to be a messy hybrid of all three.In other words, where the term 'public value' has any meaning, it generally refers to public goods, the public interest or the public domain while offering nothing new to any of them.
Regardless, Moore remains the so-called father of public value creation (Meynhardt, 2022), although it remains unknown how to empirically measure the extent to which public organisations actually create public value (Faulkner & Kaufman, 2017;Williams & Shearer, 2011;Talbot & Wiggan, 2010).This is partly due to the fact that the term public value remains conceptually unresolved, as there is no universally accepted definition among scholars (Pang et al., 2014).Nevertheless, the creation of public value should remain a goal of public organisations, as public organisations use public value to meet the needs and wishes of the public -citizens (Brown et al., 2021;Neumann et al., 2019;Jørgensen & Bozeman, 2007).
For the purposes of this paper, we have defined public value creation as the outcomes that are the results of the previous three categories.On this basis, 'public service quality provision capability -improved efficiency and effectiveness of public service production' and 'citizen engagement capability -public administrations identify and respond more quickly to citizens' aspirations' were identified as attributes (criteria) of the fourth category 'outcomes -public value creation' (Twizeyimanaa & Andersson, 2019;Faulkner & Kaufman, 2017;Scott et al., 2016;Spano, 2014;Pang et al., 2014).In this paper, outcomes -public value creation are written as verbs -e.g.'efficiency' is not a public value in itself, whereas 'doing or performing something in an efficient way' becomes one.While this way of defining public value may seem unusual, it proves extremely useful, especially in the context of different views on what public value is and how to measure it (Twizeyimanaa & Andersson, 2019).
The ability to deliver quality public services is key to creating public value.Delivering public services more efficiently and effectively, with minimal use of public resources and in a faster (more responsive) time, makes a significant contribution to improving quality (Scott et al., 2016;Pang et al., 2014).This was already highlighted three decades ago by Moore (1995), who pointed out that it is not enough for public managers to produce results that are supported by the public (citizens or civil society at large).Public managers need to demonstrate that these results justify public spending, as only then can it be argued that public value has been created.Alford and Hughes (2008) further state that if the desired results are achieved with minimal public expenditure and within a reasonable period, it can be concluded that PA is also efficient and effective.
In addition to providing quality public services, citizens' ability (or capacity) to engage in the public policymaking process is also crucial for creating public value (Rasmussen & Rehe, 2023;Pang et al., 2014).Stoker (2006) points out that the paradigm of public value creation relies on citizens' trust in PA and the recognition of its legitimacy.Therefore, for public value creation to be supported and responsive to citizens' needs and desires, it is imperative that the government facilitates and supports active citizen participation.Without citizen participation and consent, public values cannot be created, no matter how good the government considers the quality of the outcomes created, as citizens may not consider them to be so (Wilson & Knighton, 2021;Faulkner & Kaufman, 2017;Pang et al., 2014;Talbot & Wiggan, 2010).In the digital age, where more and more tools for two-way communication are available, PAs are even more expected to foster citizen participation, which will increase the trust and legitimacy of their actions.Accountable and transparent PA means better access to government information and improves the transparency of PA while reducing the risks of corruption (Castro & Lopes, 2022).This is considered in an increasingly dynamic environment where ICTs are transforming the design of public services, and it is becoming increasingly clear that PA alone can no longer respond effectively to the changing needs and demands of citizens (Pang et al., 2014).However, as ICTs enable two-way communication more than ever before, PA is also expected to work more closely with citizens in the co-creation of public services.This mutual collaboration enables public services to become truly citizen-centred and tailored to citizens' needs (Li & Shang, 2023;Scott et al., 2016;Hellang & Flak, 2012;Jansen, 2012).

Hierarchical structure of the decision support model
With the help of the content analysis, we identified 29 subordinate and 13 singleparent -see Table 1.These attributes represent the foundation for the development of the structure of the multi-attribute model, designed to measure the maturity of SPG in public administration institutions presented in this section.Figure 1 represents the tree-like structure of the multi-attribute model consisting of four level attributes (criteria), which support the SPG maturity measurement in public administration institutions.In the tree-like structure, each subordinate attribute affects a single parent attribute (Bohanec, 2012).
In this structure, there are basic and derived attributes.An example of a basic (subordinate) attribute is 'the impact of artificial intelligence to automate routine processes' (1.2.1.1.)since it has no subordinated attributes.In the model, it represents the final node or "leaf".Such attributes are the model's input (operational attributes).Other attributes are derived (superior or single parent attribute); an example is 'the use of emerging technologies in PA' (1.2.), meaning they are aggregated nodes within the model (also called aggregated attributes).The superior attributes are calculated based on the values assigned to the basic attributes by the interview group.
The highest attribute, i.e., the final maturity of SPG in public administration institutions, is the main output attribute -the root of the tree-like structure (Bohanec, 2012).
It should be stressed, however, that the attributes within the multi-attribute model are treated as being non-redundant (avoiding unnecessary attributes that could impact the model's size and complexity), mutually independent (one attribute for a specific decision factor), and operable (applicable in practice) (Bohanec, 2012).The paper makes a pioneering step aiming to stimulate a broader academic debate about which attributes (criteria) corresponding to the subject/area should be considered when measuring the maturity of SPG in public administration institutions.
The multi-attribute model creation is based on several iterations, using the DSR approach (Dresch et al., 2015) and the DEX method, belonging to a multi-attribute utility theory method group.Prior research findings obtained through content analysis played a crucial role in identifying attributes (criteria) essential for measuring the maturity of SPG.Considering what we already learned, we can argue that maturity in SPG is not only about the use of technology but also about changes that public sector organisations face internally, as well as evolving dynamics of citizen interaction with public sector organisations in order to create public value (outcomes) for society.
Further research should focus on the practical application and validation of the multi-attribute model introduced in this paper.Future studies are encouraged to test and refine the identified attributes (criteria), enhancing the model's robustness and applicability.
SPG is "the importance of technology-based tools in transforming government institutions from a public administration perspective (i.e., technology interaction with the institution)" Zhu & Kou (2019, p. 2) SPG is "applying ICTs in the processing of information and decision-making in order to improve the capacity of governance" , 2015, (in Yolles, 2019, p. 1) , p. 1) SPG is "the combining of digital technologies with innovative practices to improve government service delivery and citizen inclusion in developing and implementing policy.This enables responsive, transparent, and inclusive policy decisions that build citizen trust in government institutions at all levels, and create a dialogue between supply (government) and demand (citizen)" Giffinger et al., 2007al., (in Ruhlandt, 2018, p. 13) , p. 13) SPG "comprises aspects of political participation, services for citizens as well as the functioning of the administration" Andermatt & Göldi, 2018(in Babić et al., 2022, p. 317) SPG is "not only about digitising existing processes and services but also about developing and establishing entirely new processes and public services in a participatory way for citizens" Šiugzdiniene et al. (2017, pp. 589-590) SPG is "a mode of governance that relies on rationally utilizing internal and external resources, making adequate progress, and making advanced decisions relevant to specific circumstances to create shared value to make a social system (country, region or city) and its actors (government, citizens, communities, businesses and non-governmental organizations) operate effectively in a fast-changing and complex environment" Scholl & AlAwadhi (2016, p. 22) SPG is "the capacity of employing intelligent and adaptive acts and activities of looking after and making decisions about something" Meijer & Bolivar, 2016(in Babić et al., 2022, p. 317) SPG is "make the right policy decisions and implement them effectively, and the need for smart decision making that includes the processes and implementation of

Figure 1
Figure 1: Multi-attribute model for measuring smart public governance maturity in public administration institutions