Breaking barriers for breaking ground: A categorisation of public sector challenges to smart city project implementation

Public sector innovation and the main paradigms of public administration and management

Public sector organisations strive to fulfil their mission by constantly enhancing the quality of public service delivery in response to external and internal changes occurring across social, economic, and political landscapes. The obligations derived from their mission introduce growing pressures and demands for innovation (Hartley et al., 2013). Examples of factors influencing this imperative to innovate include the following: increasing fiscal pressures (Bartlett and Dibben, 2002), the rising expectations of citizens for improved public services (Borins, 2001; Demircioglu and Audretsch, 2017; Hartley, 2005), and the necessity to develop specialised responses to complex societal challenges like climate change and poverty (Hartley et al., 2013; Sørensen and Torfing, 2012).

Current studies extols the virtues of PSI, underscoring its potential to enhance organisational productivity, problem-solving abilities, and the quality of service delivery (Bloch and Bugge, 2013; De Vries et al., 2016). Moreover, it has been observed that the adoption of innovative practices by public sector organisations is profoundly shaped by shifts in the paradigms of public administration and management. Three major paradigms stand out for their significant impact on how scholars and practitioners understand and apply the principles of public administration and management (Hartley, 2005): Traditional Public Administration (TPA), New Public Management (NPM), and New Public Governance (NPG). Existing evidence shows that these paradigms introduce diversity across nations and among public sector entities in crafting innovation management strategies and in allocating authority to policymakers, innovation managers, and citizens (Arundel et al., 2015; Hartley, 2005).

Traditional Public Administration advocates for a state-as-a-producer model, wherein societal needs are addressed by public professionals through standardised services (Hartley, 2005). In this framework, politicians at various levels of public administration wield significant influence over PSI activities (Hartley, 2005), fostering a top-down management of innovation predominantly steered by political decisions (Arundel et al., 2015; Walker, 2006).

In contrast, NPM shifts these dynamics, advocating for public sector organisations to adopt practices from the private sector, including the introduction of market-based competition (Osborne and Gaebler, 1992) and the application of private sector management techniques (Hartley et al., 2013; Lapuente and Van de Walle, 2020). New Public Management core objectives emphasise outcome control in PSI operations (Sørensen and Torfing, 2012) and typically aims to commercialise government services and the government-citizen relationship (Gonzalez et al., 2013). Within the NPM framework, innovation activities focus on organisational forms and processes, highlighting the importance of decentralised decision-making and granting more autonomy to individual departments (Hartley, 2005). An entrepreneurial strategic outlook is encouraged among PSIs as they seek to identify and capitalise on new service and market opportunities (Andrews and Van de Walle, 2013). Moreover, NPM views the role of citizens in innovation as akin to that of customers, with public managers and policymakers acting as agents of market and efficiency maximisation (Hartley, 2005).

The NPM framework has faced significant criticism regarding its approach to fostering innovation in public service provision. For example, Hartley et al. (2013) have pointed out that NPM may discourage knowledge sharing across organisations, thereby impeding certain types of innovation. While NPM’s strong emphasis on performance management and control has been recognised for its positive impact on enhancing the efficiency of service production, Andrews and Van de Walle (2013) contend that an overemphasis on quantifiable performance metrics can limit the flexibility of public sector organisations in adapting services to meet the varied needs of different citizen groups. Furthermore, Hartley (2005) has argued that the “customer focus” inherent in NPM has improved certain services but has simultaneously neglected others that require closer relationships, such as co-design and co-production of services.

Some scholars have also highlighted that the principles of NPM can stifle the adoption of a bottom-up approach that centers services around citizens and involves collective decision-making by key stakeholders (Arundel et al., 2015). This body of research advocates for the adoption of new governance models, like NPG, which emphasises networked governance to facilitate collaboration among various public sector organisations (Christensen and Lægreid, 2007) as well as the development of innovative strategies through leveraging the expertise of policymakers, public managers, private sector entities, and citizens (Sørensen and Torfing, 2012). This shift towards NPG aims to prioritise collaboration and innovation by tapping into a broader range of insights and expertise.

New Public Governance conceptualises public administration and management being shaped by the diverse and evolving needs of citizen groups. It highlights the crucial role of networks and partnerships between local and national public sector organisations in driving the successful delivery of public services (Hartley, 2005), while arguing for the importance of national actors in providing the necessary space for local authorities (Van Duijn et al., 2021). This enables the development of interorganisational collaborative networks that include various stakeholders, thereby fostering innovation through collective effort.

Innovation within the NPG framework is portrayed as a collaborative act that involves a wide array of stakeholders, from different citizen groups to public and private sector organisations. This collaborative environment facilitates the cross-pollination of ideas and the co-creation of new solutions aimed at improving service delivery (Hartley et al., 2013). The networked governance paradigm positions policymakers, public managers, and citizens as co-explorers and co-producers of transformative changes and ongoing improvements in service quality across all levels of public administration (Hartley, 2005). Evidence shows that increased collaboration strengthens PSI management throughout the entire process (Gonzalez et al., 2013; Sørensen and Torfing, 2012). Moreover, organisations engaged in peer networks exhibit enhanced innovation capabilities (Hartley et al., 2013).

Smart city projects serve as a prime example of PSI attempts where an NPG approach to innovation is needed. Smart city technologies are increasingly recognised for their potential to tackle a broad spectrum of social, environmental, and economic challenges that citizens and local governments face (Barrutia et al., 2022). The primary aim of such projects is to deploy digital solutions that make urban systems and services more accessible and create public value (Bjørner, 2021; Martin et al., 2018). The advancements in Information and Communication Technologies (ICTs) over the last two decades have simplified the process for local authorities to collect and analyse large data sets, supporting evidence-based policymaking (Ullah et al., 2021). By engaging multiple stakeholders in smart city projects, local governments can be in a better position to enhance economic prosperity and public management efficiency (Andrea et al., 2013; Meijer, 2018; Nilssen, 2019).

While smart city technologies offer promising advantages and have seen several successful implementations worldwide, these projects are increasingly subjected to scrutiny due to various strategic and operational concerns. Martin et al. (2018) highlighted a notable gap in evidence supporting the achievement of smart and sustainability objectives by smart city projects, suggesting that these initiatives often embody a technocratic and neoliberal vision without providing a coherent, actionable plan for implementing and measuring sustainability metrics. This critique aligns with other studies that point out the predominance of a technocratic approach in the deployment of smart city projects (Arundel et al., 2015; Meijer, 2018), underpinned by managerial philosophies that resonate with the the NPM paradigm (Hartley, 2005; Hartley et al., 2013).

In addition to the challenges rooted in high-level public management paradigms, several other factors impede the successful implementation and long-term financial sustainability of smart city projects. These include the absence of effective governance mechanisms, the high costs of project implementation, difficulties in scaling up, a lack of interoperability among technologies and devices, as well as concerns related to data management and information security (Mora et al., 2023). These issues frequently emerge as critical obstacles, significantly influencing the success of smart city initiatives. However, these barriers have never been examined in the framework of PSI theory.

Categorisation of barriers to public sector innovation

Research examining barriers to PSI has grown rapidly in recent years, and so has its importance; it is widely acknowledged in public administration literature that public sector organisations can better manage their innovation efforts when they possess a clear understanding of what barriers can prevent their projects from being successful (e.g., see (Cinar et al., 2019; Mu and Wang, 2022; Torugsa and Arundel, 2016). Several single case studies have been presented, looking at an array of policy areas where the public sector has crucial obligations to meet, such as education, safety and security, healthcare, waste management, and mobility oversight (Cinar et al., 2019; Qiu and Chreim, 2022; Torvinen and Jansson, 2023). By analysing several exemplary projects, these studies help provide a broader understanding of what barriers can hinder successful implementation of PSI projects in the context of specific policy domains. However, they have a reduced generalisation capability, which results from the limitations imposed by their research design (Tangi et al., 2020), and these limitations have triggered the need for more systematic examinations focused on the framing of taxonomies of barriers.

To address this gap, public administration scholars have conducted several systematic literature reviews. These studies introduced different approaches to the examination and categorisation of barriers experienced by public sector organisation when dealing with innovation projects. Barriers are interpreted as either obstacle to overcome (D’Este et al., 2012; Meijer, 2015) and predictors of outcomes, or antecedents of innovation (De Vries et al., 2016). Research by De Vries et al. (2016), for example, exposes the need for public administration research to further explore barriers to PSI by considering the environmental and organisational contexts in which innovation processes take place. Other studies have focused on categorising barriers as internal versus external (Bloch and Bugge, 2013) or revealed as opposed to deterring (D’Este et al., 2012). Attempts to address these issues can be observed in some recent review articles. In line with the argument brought about by De Vries et al. (2016), the systematic literature review by Cinar et al. (2019) suggests that conceptualising barriers only as negative or enabling factors reflect a static view that fails to visualise the phased development of innovation processes. Accordingly, the authors create a taxonomy of barriers to PSI that accounts for different project phases, exposing how challenges may emerge and evolve over time.

Mu and Wang (2022) add to these cognitive frameworks with a study that compares barriers to innovation projects in two different types of settings: digital and non-digital transformations in public sector organisations. This distinction is instrumental in emphasising the contextual differences that project types imply when considering different application areas, but also the additional complexities that digital innovation may involve. Mu and Wang (2022) also contribute to proving that research on barriers to digital innovation in the context of local governments is gaining attention; however, no conceptual frameworks have been developed that provide a systematic view of the type of barriers that can emerge when public sectors organisations work on smart city projects. Global-scale data gathered by the United Nations shows that many local governments worldwide are already working on smart city projects (Beckers et al., 2022), and they progress without possessing a holistic understanding of the possible barriers that they might encounter (Ihrke et al., 2003; Queyroi et al., 2022). The complexity of these projects requires public sector organisations to deal with new challenges, such as more advanced technologies, more complex collaborative environments, and changes in existing institutional settings that might lead to conflict and resistance. Thus, we recognise the need for a dedicated inquiry that specifically targets the obstacles in implementing smart city projects.

To set the theoretical foundation for a new conceptual framework in which these barriers can be organised, we build on the abovementioned studies. More specifically, we start from the five macro categories proposed by Cinar et al. (2019, 2021). These categories focus on organisational, contextual, collaborative, technological, and resource-related barriers, respectively.

Organisational barriers refer to internal challenges that public sector organisations experience when dealing with leadership (Arundel et al., 2019; Mergel et al., 2018), the management of core business functions (Cinar et al., 2019), and the formulation of strategic visions and organisational strategies (Addae et al., 2019; De Vries et al., 2016). Organisational barriers also focus on issues that can emerge when the public sector adopts inappropriate approaches to the development and implementation of innovation projects (Anand and Navío-Marco, 2018; Ma and Lam, 2019).

Contextual barriers originate outside the boundaries of public sector organisations; however, they contextual environments shape their actions and affect what smart technologies they would deploy and how they should manage inputs and outputs of these technologies according to existing local policies, and national laws. By influencing structural and cultural features of public sector organisations (Cinar et al., 2019; De Vries et al., 2016), contextual elements impact on project arrangements.

Interaction-specific barriers reflect that the successful implementation of innovation projects depend on a complex network of stakeholders (D’Este et al., 2012), including private and third sector organisations, community groups, and citizens. The relationships between these stakeholders can be complex to coordinate and maintain, hence such interactions can cause frictions, affecting the quality of the implementation process of innovation projects and their outcome (Cinar et al., 2019; De Vries et al., 2016)

Technology has become a core element in many innovation projects, and it has a strong influence on the pace of innovation in public organisations. Technological barriers refer to challenges that originate from the characteristics of a particular technology. Some examples include financial burdens affecting development and deployment, compatibility, and interoperability with existing technological systems (Cinar et al., 2019; Razmjoo et al., 2021), existing regulations and old government structures (Janssen et al., 2017), cybersecurity (Ullah et al., 2021), and physical infrastructure (Merhi, 2021).

Finally, resource-related barriers reflect the internal and external availability of resources that are vital to innovation projects, including infrastructure, human, and financial resources. Resource availability is essential for any innovation project to unfold (De Vries et al., 2016). In the public sector context, for instance, it is widely acknowledged that organisations often struggle with limited budgets and skills gaps, which limit the potential to innovate fast and sustain scale up operations. Moreover, in recent years, the intensive implementation of digital transformation projects escalated the discussion around the state of human resources (Mergel et al., 2019; Paskaleva and Cooper, 2018; Pittaway and Montazemi, 2020), which refers to the lack of digital skills and knowledge in both developing and managing digital technologies in the public sector (Kuhlmann and Heuberger, 2021; Nadkarni and Prügl, 2020).

Phase 1: Literature search and initial sample selection

We started with the crafting of a search query comprising a list of selected keywords, which were used to gather relevant publications from relevant academic databases. The search query combined the locution ‘smart city’ with the term barrier and relevant synonyms and expressions that are often used to describe issues making it difficult for a project to be completed: for example, inefficiency, limitation, concern, shortcoming, drawback, failure, and absence. When possible, all the words that we selected were added to the search query in their plural and singular forms: (TITLE-ABS-KEY (“smart city” OR “smart cities”) AND TITLE-ABS-KEY (absence OR barrier* OR inefficienc* OR limitation* OR concern OR concerns OR shortcoming* OR drawback* OR failure*).

After being assembled, the search query was run in Scopus at the end of December 2019. The SLR has been undertaken as the part of a project, which ended in 2019. Therefore, the sample covers the articles that are published until the end of December 2019. The search was set to look for the selected keywords in titles, abstracts, or keyword lists of Scopus-indexed references. The selection of Scopus as our primary platform for conducting literature searches was driven by its comprehensive coverage, which encompasses a broad range of publication types. No publication types were pre-selected during the search, hence the initial sample included articles in peer-reviewed journals, books, book chapters, and conference papers for a total of 2809 unique records.

Phase 2: Definition of inclusion and exclusion criteria

A set of inclusion and exclusion criteria were formulated to assess the retrieved publications against the objectives of this study and to eliminate those that were not relevant to our investigation. First, all authors of this article concurred that it was essential to consider publications across various disciplines to avoid omitting relevant studies. However, we decided to only focus the review on material presenting empirical evidence to exclude publications that do not explicitly discuss any specific barrier or just mention such barriers without providing any empirical data. Furthermore, we chose to exclude publications written in a language other than English. Table 1 shows all the inclusion and exclusion criteria together with the relevant reasons.

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Table 1.

The inclusion and exclusion criteria.

Code	Criteria		Reasons for inclusion or exclusion
	Type	Focus
INC.1	Inclusion	Quantitative and qualitative empirical studies	Inclusion of articles that offer empirical evidence, which is the focus of this review
INC.2	Inclusion	Perspective	Inclusion of articles that make it possible to understand smart city barriers from the perspective of public administration and governance perspectives
INC.3	Inclusion	Publication type	All types of publications are included (i.e., books, book chapters, conference proceeding)
EXC.1	Exclusion	Unit of analysis	Exclusion of publications which do not mention barriers to smarty city implementations in their abstracts
EXC.2	Exclusion	Unit of analysis	Exclusion of publications which do not discuss barriers in detail in the main body of articles
EXC.3	Exclusion	Language	Exclusion of the articles which are written in a language other than English

Phase 3: Title and abstract analysis

Upon reaching consensus on the selection criteria, each co-author was assigned the task of reviewing the titles and abstracts of the 2809 publications initially identified for inclusion in our study. This was done to exclude any publications that did not align with the scope of our research. To ensure consistency and thorough discussion of the decision-making process, multiple rounds of meetings were convened both prior to and during the analytical phase. Independently, every author examined all publications in the sample, after which the findings were meticulously compared on a pairwise basis to achieve unanimous agreement. This approach to qualitative data analysis, which shares similarities with consensus coding, was lengthy but secured a rigorous standard of selection (Snyder, 2019; Xiao and Watson, 2019). At the end of this phase, the authors have agreed on 398 publications for further examination.

Phase 4: Full-text assessment

The same collaborative approach was adopted when assessing the content of each publication. The 398 publications selected during Phase 3 were distributed among the co-authors, each of whom independently evaluated their alignment with the inclusion criteria. After these independent assessments, the co-authors engaged in thorough discussions to elucidate the reasons for each publication’s inclusion or exclusion, with the aim of achieving consensus. Consequently, 154 publications were deemed suitable for the coding phase. The list of selected publications and the journal titles are presented in the Table 2.

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Table 2.

The list of venues that selected publications are published.

Title of publication venue	Corresponding publication
Journals
Accounting, Auditing & Accountability Journal	Argento et al. (2019)
ACM SIGSOFT Software Engineering Notes	Vieira and Alvaro (2018)
Annals of the American Association of Geographers	Crampton et al. (2019); Masucci et al. (2019)
Annual Review of Political Science	Brady (2019)
Applied Sciences	Dilawar et al. (2018)
Asia & the Pacific Policy Studies	Yang and Xu (2018)
Cities	Angelidou (2014); Lim et al. (2018); Marek et al. (2017); Mueller et al. (2018); Offenhuber and Schechtner (2018); Ruhlandt (2018); Addae et al. (2019); Bjørner (2021)
City, Culture and Society	Barns (2018); Reed and Keech (2019)
Computer Communications	Rodrigues et al. (2018)
Computer Networks	Ahmed et al. (2017)
Computers & Security	Pan et al. (2019); Vitunskaite et al. (2019)
Computing	Ye et al. (2019)
Data	McKenna (2019)
Energy Policy	Mosannenzadeh et al. (2017)
Environment, Development and Sustainability	Marsal-Llacuna (2019)
Equity & Excellence in Education	Leigh (2017)
European Urban and Regional Studies	Trivellato (2017)
Future Generation Computer Systems	Gianni et al. (2019)
Geo-spatial Information Science	Musakwa (2017)
Government Information Quarterly	Belanche-Gracia et al. (2015); Li and Liao (2018); Maccani et al. (2020); Matheus et al. (2020)
Health Policy and Technology	Bates et al. (2018)
Human-centric Computing and Information Sciences	Park et al. (2019)
IEEE Access	Liborio et al. (2018)
IEEE Internet Things Journal	Nelson et al. (2019)
IEEE Transactions on Industrial Informatics	Xiao et al. (2018)
Information	Lee et al. (2017)
Information Polity	Gil-Garcia (2012); Meijer and Thaens (2018)
Information Systems and e-Business Management	Peng et al. (2017)
Information Systems Frontiers	Gupta et al. (2019); Rana et al. (2019)
Innovation: The European Journal of Social Science Research	De Wijs et al. (2016)
International Journal of Parallel Programming	Alabady et al. (2018)
International Journal of Sustainable Development and Planning	Praharaj et al. (2018)
International Review of Law, Computers & Technology	Jewell (2018); Rinik (2019)
Internet Research	Janssen et al. (2019)
Journal of Cleaner Production	Almeshaiei et al. (2020); Colding and Barthel (2017); Lin et al. (2015); Lu et al. (2018); Adapa (2018)
Journal of Open Innovation: Technology, Market, and Complexity	Yigitcanlar et al. (2019)
Journal of Reliable Intelligent Environments	Pereira et al. (2018)
Journal of the Knowledge Economy	Tekin Bilbil (2017)
Journal of Transport and Land Use	Faisal et al. (2019)
Journal of Urban Technology	Joss et al. (2017)
Management Decision	Chinnaswamy et al. (2019)
Mobile Networks and Applications	Yao et al. (2017)
Mobilities	Perng (2019)
NEO 2016	Escamilla-Ambrosio et al. (2018)
Personal and Ubiquitous Computing	Kabáč et al. (2017); Qiu et al. (2017)
Pervasive and Mobile Computing	Valerio et al. (2017)
Procedia Computer Science	Patel and Doshi (2019)
R&D Management	Sandulli et al. (2017)
Sensors (Basel)	Garcia-Font et al. (2017)
Social Science Computer Review	Popham et al. (2020)
Sustainability	Expósito López et al. (2019); Lim and Taeihagh (2019); Lombardi et al. (2017); Lytras and Visvizi (2018); Yang (2019)
Sustainable Cities and Society	Cellina et al. (2020); Dagher et al. (2018); Mattoni et al. (2015); Nicolas et al. (2020); Silva et al. (2018)
Sustainable Development	Khan et al. (2020)
Technological Forecasting and Social Change	Corsini et al. (2019); Islam et al. (2020); Kummitha (2018); Trencher (2019); Wang et al. (2019)
Technovation	Paskaleva and Cooper (2018)
Telecommunications Policy	Anand and Navío-Marco (2018); Sangki (2018); Vu and Hartley (2018)
The Electricity Journal	Li and Shahidehpour (2017)
Urban Policy and Research	Barns et al. (2017)
Urban Research & Practice	Cowley et al. (2018)
Urban Studies	Spicer et al. (2019)
Waste Management	Esmaeilian et al. (2018)
Conference Proceedings
18th IFAC Conference on Technology, Culture, and International Stability	Kobza and Hermanowicz (2018)
2016 1st International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE) in partnership with Global City Teams Challenge	Rhee (2016)
2017 27th International Telecommunication Networks and Applications Conference (ITNAC)	Elsaeidy et al. (2017)
2017 9th International Workshop on Resilient Networks Design and Modeling (RNDM)	Sterbenz (2017)
2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)	Marchiori (2017)
2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation	Naphade et al. (2017); Sandnes et al. (2017)
2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC)	Vaidya and Mouftah (2018)
2018 21st Saudi Computer Society National Computer Conference (NCC)	Subasi et al. (2018)
2018 3rd Technology Innovation Management and Engineering Science International Conference (TIMES-iCON)	Chen et al. (2018)
2018 4th International Conference on Universal Village (UV)	De Aguiar et al. (2018)
2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)	Alromaihi et al. (2018)
2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)	Desai et al. (2018)
2018 IEEE International Conference on Smart Computing (SMARTCOMP)	Burns et al. (2018)
2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)	Morrissett and Abdelwahed (2018)
2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA)	Morrissett and Abdelwahed (2018)
2018 International Conference and Exposition on Electrical and Power Engineering (EPE)	Picioroagă et al. (2018)
2018 International Conference on Smart Systems and Inventive Technology (ICSSIT)	Mary et al. (2018)
2019 CHI Conference on Human Factors in Computing Systems	Freeman et al. (2019); Heitlinger et al. (2019)
2019 Global IoT Summit (GIoTS)	Frauenberger (2019)
2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems	Wu et al. (2019)
2019 IEEE/ACM 5th International Workshop on Software Engineering for Smart Cyber-Physical Systems (SEsCPS)	Barnaby (2019)
2019 International Conference on Information Management and Technology (ICIMTech)	Setyowati et al. (2019)
2nd International Conference on Smart Grid and Smart Cities (ICSGSC)	Lesperance et al. (2018)
2nd International Conference on Sustainable Engineering Techniques (ICSET 2019)	Alkanaani and Bahith (2019)
4th MEC International Conference on Big Data and Smart City (ICBDSC)	Guangul and Chala (2019)
CEUR Workshop Proceedings	Grieman (2019)
EVS26 International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium	Beeton (2012)
HCI 2018	Shreepriya et al. (2018)
ICIOT 2018	Tekinerdogan and Köksal (2018)
ICSR 2019	Yin et al. (2019)
IEEE International Smart Cities Conference (ISC2)	Tonekaboni et al. (2018)
Proceedings of the 10th Latin America Networking Conference	Ayora et al. (2018)
Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance	Ramos and Silva (2019)
Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age	Marzouki et al. (2018)
Proceedings of the 21st International Database Engineering & Applications Symposium on - IDEAS 2017	Costa and Santos (2017)
Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities	Kendrick et al. (2019); Potoczny-Jones et al. (2019)
Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security	Diallo et al. (2018)
Proceedings of the International Conferences ICT, Society, and Human Beings 2019; Connected Smart Cities 2019; and Web Based Communities and Social Media 2019	Mamay (2019)
The 20th World Multi-Conference on Systemics, Cybernetics and Informatics	Lom et al. (2016)
The 4th Conference on Sustainable Urban Mobility	Stefanouli and Economou (2019)
The International Conference on Industrial Engineering and Operations Management	Jayasena et al. (2019)
Books
Building on Smart Cities Skills and Competences	Panagiotakopoulos et al. (2020)
Climate Change in Cities	Rajasekar et al. (2018)
Green Building in Developing Countries	Hui et al. (2020)
Living in the Internet of Things: Cybersecurity of the IoT	Madaan et al. (2018)
Smart Cities: Technologies and Models of Governance for Citizen Engagement	Manda and Backhouse (2019)
Smart Technologies for Smart Governments	Edelenbos et al. (2018)
Web 2.0 Technologies and Democratic Governance	Anthopoulos and Tougountzoglou (2012)

Phase 5: Thematic coding

The coding methodology employed in this study is based on the study developed by Gerli et al. (2022), drawing inspiration from Gioia et al. (2012). After conducting the full-text assessment, the selected publications were subjected to a rigorous three-level thematic coding process. Initially, all passages that provided empirical evidence on barriers to implementing smart city projects were extracted (first-level coding). This task, undertaken by the lead author, resulted in the identification of 58 distinct barriers. Subsequently, all co-authors devised second-level codes to categorise these barriers into themes and independently matched the identified barriers to their corresponding themes. After this independent analysis, the coders convened to compare their coded data, engaging in thorough discussions to achieve consensus on emerging patterns, themes, and relationships (Richards and Hemphill, 2018).

Upon aligning the first and second-level codes, they were linked to five macro-level categories proposed by Cinar et al. (2019, 2021), which we used as theoretical dimensions. This step was carried out individually by all authors, with the results compared and deliberated upon until unanimous agreement was reached. This coding strategy significantly enhanced the reliability and credibility of the study, enabling the analysis of an extensive dataset. Furthermore, it fostered a collaborative and participatory research environment by involving multiple coders and their cross-disciplinary backgrounds in the analytical process (Cascio et al., 2019).

Organisational barriers

Under this category, four categories of barriers have been identified: failures in the strategy and vision, failures in leadership, failures in public procurement, and failures in data management. All these barriers reflect actions leading to the mismanagement of critical organisational issues with an impact on the delivery of individual projects and long-term digital strategies.

Interaction-specific barriers

Under this category, two types of barriers have been identified: Failures in stakeholder collaboration and failures in project implementation. Both barriers reflect ineffective actions (or inactions) by municipal governments, resulting in the mismanagement of multi-stakeholder collaborations (that are vital for the completion of smart city projects and to boost their acceptance among local communities).

Contextual barriers

The review revealed that contextual barriers to smart city developments can be associated with failures in markets and political systems, as well as shortcomings of the public administration. While much emphasis has been placed on contextual barriers related to the structure and functioning of the public sector, market failures were less frequently debated and analysed in the revised publications.

Resource-related barriers

Four categories belong to this category: shortcomings in the enabling infrastructures, lack of skills and knowledge, shortcomings of supportive tools, and lack of funding. All these barriers have critical implications for the long-term viability and scalability of smart city projects, and are affected by economic, cultural, and political contingencies specific to urban contexts.

Technology-specific barriers

Finally, some barriers experienced by smart city projects reflects idiosyncrasies in the design and economics of smart city technologies. These barriers also include shortcomings and gaps in the existing regulations and business models, specific to these technologies.

Theoretical contributions

Cinar et al. (2019: 284) state that “the nature of barriers is complicated and not well understood, although there has been a general perception that the barriers to public sector innovation are well studied”. Our review builds on this observation; it contributes to bridging this gap by offering a more nuanced and comprehensive framing of the barriers to innovation that public sector organisations might experience, with a focus on the fast-growing domain of smart city projects. The theoretical implications of this study are likely to benefit scholars across different domains, adding to the literature on PSI and smart city transitions.

The existing systematic literature reviews that investigate barriers to PSI focus on public sector as a whole and they present barriers without focusing on a specific administration level (Cinar et al., 2019, 2021). In this study, we only consider the perspective of local governments, using smart city project development as our empirical setting. Several studies stressed the importance of studying barriers to digital transformations at the level of local governments. This call is structured upon three interrelated reasons, which highlight the growing centrality of local governments in smart city development practices. First, local governments act as “the pivot of the network of actors that can take a role in implementing smart cities projects” (Sancino and Hudson, 2020: 716) and have the autonomy to implement technological innovation policies and administrative reforms (Weißmüller et al., 2023). Second, local governments often represent the initiator of smart city projects (De Vries et al., 2016). Third, local governments have become more entrepreneurial, with innovation that has become a means to fight poor fiscal health in the face of a growing demand for services (Andrews et al., 2021), to manage New Public Management reforms (Cinar et al., 2022; De Vries et al., 2016), and to adapt nationally developed digital transformation programmes to local-context conditions (De Vries, 2018; Nicolas et al., 2020).

Focusing on the smart city literature enabled us to gain additional insights into the barriers that public sector organisations face when developing these initiatives. Our findings support some of the findings of Mora et al. (2023), in which the authors highlighted the strengths and weaknesses in the current approaches to the governance of smart city transitions. The authors identified several challenges in relation with administrative structures, internal capabilities, technological innovation policies, implementation strategies, collaborative tools and spaces, cross-sector partnerships, technical regulations and standards, and business models (Mora et al., 2023). These findings are aligned with several barriers we identified in this systematic literature review regarding the failures in strategy and vision, the shortcomings of the public administration, the failures in data management, the lack of skills and knowledge, the shortcomings in the supporting tools, the shortcomings in the regulation of technologies, and the gaps in business models.

Finally, our analysis provides a granular and nuanced categorisation of the barriers that local governments experience when implementing smart city projects. Through a systematic review of the literature, we identified 58 barriers (micro-level), which we grouped under the five macro categories presented by Cinar et al. (2019, 2021) in their studies on barriers to PSI. Generally, our analysis highlight that, within these macro categories, there coexists different types of barriers, which can be classified in failures, shortcomings, and gaps. Failures stem from actions that do not lead to the intended outcomes because of the mismanagement of existing tools and resources, or ineffective decision-making. Shortcomings result from faults and flaws in existing institutions or tools that should instead support PSI. Lacks reflect the absence of such resources, tools, and mechanisms. By distinguishing between these three typologies of barriers (failures, lacks and shortcomings), we respond to Criado et al. (2023: 12) call for “a rigorous classification of the enablers and inhibitors of implementation”. These three typologies also help us understand the relative intensity of the barriers, which the existing taxonomies do not provide an indication about, and this understanding makes possible to configure appropriate mitigation strategies based on barriers’ severity and strength (Rjab et al., 2023). The intensity of the barriers arises from the fact that whether capabilities, conditions and tools exist, or whether they are appropriate for initiating smart city projects or can nestle the required properties to initiate viable projects. In the case of the failures, it is a combination of non-existing capabilities and inappropriate actions, as well as tools. For the shortcomings, we observed that regulations, institutions, physical infrastructure, and technological capabilities are in place; however, they are either not sufficient to enable a smooth transition to smart city technologies or they do not address (issues arising due to the implementation of a new technology. Therefore, current regulations, institutional practices and infrastructure need to be reviewed and changed wherever is necessary. The lacks (or gaps) point to resources that do not exist but essential for starting smart city projects and being able to scale-up in the long term. Also, we focused on identifying the barriers that appear in the initial stages of smart city projects. Wang (2023: 2) highlighted that “firms learn more effectively from early-stage failure experiences than from late-stage failure experiences”.

Practical contributions

Our findings also offer practical contributions for public sector officials and other stakeholders that work with local governments to initiate smart city projects. The practical contributions are enabled by implementing meso-level categories to capture the root causes of the identified barriers. We realised some of these barriers arise from the absence of required resources, expertise, or stakeholders to implement and operationalise smart city technologies, whilst other barriers arise from the mismanagement of existing resources, tools, and practices. Making this differentiation between problems will help local governments to create mitigation strategies and timelines that are realistic for solving these emerging issues identified through these barriers. Local authorities need realistic timelines more than ever because they work under immense resource pressures. It will help them to realise they will need more time to build resources and knowledge from scratch when they are not in place. Also, they will realise the timeline is different to building mitigation strategies when it comes to the barriers that focus on shortcomings, which may require a shorter period of time and resources to change/mitigate the existing practices. Smart city projects are complex and the identification of barriers will enable public sector officials to understand the complexities of their existing environments and to set-up the right conditions for a smooth implementation of smart city technologies (Criado et al., 2023). Although changing established and complex operational practices is difficult and requires the right skills, experience and cultural conditions, the results of the review can help critique, pause, and reset practice. This will be instrumental in offering cognitive resources for sustaining transformative learning and deep transitions driven by innovation efforts (Cole and Hagen, 2023).

Considering the findings of this review, several action plans can be developed in the following domains: (1) community-based problem-solving strategies, (2) risk management strategies, and (3) expertise development programs. These areas are identified as the most critical subjects that will help local authorities to re-focus their actions for predicting barriers as early as possible and mitigating any negative consequences of these barriers. Global trends in smart city implementations points in the direction towards adopting people-centred and inclusive approaches as a critical part of national policies and digital transformation agendas. However, Beckers et al. (2022) argued in the Global Smart City Governance Framework help local authorities that have difficulties in ensuring the active involvement of citizens in the initiation of smart city projects and one third or local authorities worldwide fall behind responding feedback they receive from their residents. The interaction-specific barriers along with the findings of Beckers et al. (2022) indicate that local authorities need to develop community-based problem solving strategies. Whilst it is crucial that citizens should participate in the design of smart city projects for a successful final product (Zarei and Nik-Bakht, 2021), local authorities, as well as national policy guidelines, need to acknowledge that cities have their own individual features (i.e., cultures, norms, challenges, lifestyles) that need to be considered when community based problem strategies are developed (Simonofski et al., 2021). It is inevitable that community based strategies and problem-solving mechanisms will be affected by various modes of governance (whether it is based on TPA, NPM or NPG) (Przeybilovicz et al., 2020); however, local authorities need to work on several topics that will become the essential pillars of community based problem solving strategies. This approach will also enable local authorities to embrace a structured approach towards managing their innovation which can reduce risk and uncertainty caused by implementation of new technologies. Local authorities need to identify and document engagement domains (i.e., environmental, social, political, or economic challenges), engagement goals (i.e., citizens’ needs and priorities), engagement platforms (i.e., social media, games), and finally engagement incentives to attract the attention of citizens and to maximise value proposition for them (i.e., entertainment, community interaction, learning) (Zarei and Nik-Bakht, 2021).

Next, local authorities need effective risk management strategies because risk-averse nature of public sector organisations appear as one of the strongest barriers in the design and implementation of innovation projects (Cinar et al., 2019). Ideally, risk management strategies should provide local authorities with appropriate tools and knowledge for making situational analysis to continuously increase their awareness on current and future challenges that their projects need to endure. Such analysis needs to be supported through information sharing, active cooperation between stakeholders, and building necessary capacities. However, the findings of this systematic literature review showed that local authorities either do not have structured risk management strategies specifically for smart city projects or they do use their existing tools and techniques, which do not necessarily work for radical innovations and new technologies that smart city project require. Ullah et al. (2021) argued that the existing knowledge and practices provide no comprehensive risk taxonomy or methods specifically for understanding the risk exposure in smart city projects and it makes projects more prone to failures. There is a need for developing comprehensive risk management strategies that suit the nature of complex innovations. These strategies need to provide step-by-step guidelines for local authorities to identify and assess risks that stakeholders, especially public sector organisations, may come across throughout different stages of smart city projects. A multi-layered strategy that considers organisational, technological, and environmental elements of smart city projects will provide local authorities with required knowledge and tools to identify, analyse, act upon, and monitor several factors that may hinder the implementation of viable projects (Ullah et al., 2021).

The final recommendation that policy makers and practitioners can take on board is about initiating programs which specifically focus on developing necessary skills and expertise within public sector organisations, particularly in local authorities, for managing smart city projects and digital transformations. A lack of skills and expertise in managing smart city projects and digital transformation overall within public sector organisations appear as one of the most challenging organisational barriers (Beckers et al., 2022; Cinar et al., 2019), and yet individual level skills are highly critical for enabling innovation, as well as managing smart city projects with minimum friction possible (Bartlett and Dibben, 2002; De Vries et al., 2016). Therefore, local authorities need to pay attention in enabling effective acquisition and building of required talents. Building up teams and acquiring individuals that are equipped with required managerial as well as technical skills will have an impact on several other domains within local authorities such as managing complex innovation projects, analysing, and interpreting a vast amount of data for better decision making to support policy development, and managing risks under high uncertainties. Developing training and hiring programmes for expanding internal skillsets will enable local authorities to have the expertise required to manage smart city projects and services (Beckers et al., 2022).

Limitations and agenda for future research

This review comes with limitations. We ran our search terms only on Scopus and although the database is comprehensive; some studies may be omitted. We only considered a single type of PSI while identifying implementation barriers, therefore, future studies can expand the findings of this review by considering other types of innovation such as projects focusing on the digital transformation of public service provisions and organisations’ internal IT processes. Another limitation for this review arises from the heterogeneity of the studies included in terms of areas of research, methods, outcomes, and cases/samples used, which makes it challenging to synthesise the results. Finally, the selection bias can sometimes be a limitation for systematic literature reviews as the quality of these reviews heavily relies on the selection of studies. To tackle this limitation, the search terms, the inclusion and exclusion criteria, and the database were evaluated by each author and the search strategy was built based on a consensus among the authors of this review.

The barriers identified in this review provide a comprehensive understanding of several domains that public sector organisations need to deal with when implementing smart city technologies. The review showed that some of these domains and some of the barriers have been studied more extensively than others. Three areas stand out while analysing barriers: limiting nature of public procurement, lack of skills and knowledge needed to implement and manage smart city projects, and the need for comprehensive business models. First, future studies can identify the role of public procurement as a key policy instrument for fostering innovation through attracting more investments from private sector organisations (Pihlajamaa and Merisalo, 2021), and as an engagement platform for value co-creation and value destruction by various stakeholders involved in smart city projects (Torvinen and Haukipuro, 2018). Second, the discussion concerning the lack of skills and knowledge has recently emerged as a topic in the context of smart city literature and public management literature. Further research is needed to understand what skills and knowledge is required to upskill the existing workforce and equip new graduates with necessary skills throughout their studies (Mora et al., 2023). Further studies should investigate the required skills and knowledge beyond building digital skills and there is a need for understanding what business and management skills are needed for initiating viable smart city projects. Finally, we identified issues raised around the use of unsuitable business models that do not acknowledge the multifaceted nature of smart city projects (Mora et al., 2023) and assumes that local governments are at the centre of smart city projects (Timeus et al., 2020). Further studies should investigate how smart cities can become an integral part of city management and how business models can correspond to this need. There is also a need for understanding how value proposition can be constructed for a wide variety of stakeholders and how citizens can influence the design and development of smart city projects (Grossi et al., 2020). Table 4 shows further areas of research which are categorised based on each macro level components.

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Table 4.

Further research questions identified per macro level of barriers.

Framework component	Summary of the findings	Relevant areas for future research enquiries
Organisational Barriers (Failures in the strategy and vision, failures in leadership, failures in public procurement, and failures in data management.)	Local authorities lack understanding of both the technical and non-technical components in smart city projects; therefore, they tend to follow a technocratic approach and are unable to innovate their existing processes (e.g. for procurement and data government), as well as leadership practices.	• What are the most effective approaches for developing cohesive and dynamic strategies that encompass both technical and non-technical aspects of digital transformation in smarty city projects? • How can the monitoring and execution of smart city strategies be performed, considering both technical and non-technical factors? • What leadership styles are more effective in the context of smart city projects? • How can public leadership be nurtured to foster more sustainable, inclusive, and resilient smart city projects? • What methods can be employed to enhance agility and innovation in public procurement, while still preserving market competition and ensuring efficient use of public funds? • How can public procurement be harnessed as a platform for value co-creation? • What data governance practices work best in for smart city projects within the public sector? • How can data governance and procurement practices be updated to more effectively manage the forthcoming challenges associated with emerging technologies, such as AI?
Interaction-specific Barriers (Failures in stakeholder collaboration; failures in project implementation).	Local authorities struggle to form effective partnerships, encompassing all stakeholders affected by digital transformation projects. Existing project management practices are not suitable to deal with specific issues emerging in digital transformation projects (e.g., data sharing).	• What formal and informal agreements are more suitable to foster successful partnerships in the context of smart city projects? • How can local actors, including residents and community members, be encouraged to participate in the implementation of smart city initiatives? • What strategies can be employed to effectively promote collaboration among stakeholders across different sectors (including but not limited to public organisations and private companies)? • Why are project management practices ineffective when applied to smart city projects in the public sector? • What methodologies and tools can be leveraged to improve the implementation and evaluation of smart city projects within the public sector?
Contextual Barriers (failures in markets, failures in political shortcomings of the public administration)	Whereas much emphasis has been placed on contextual barriers related to the structure and functioning of the public sector, market failures were less frequently debated and analysed in the revised publications.	• What strategies can be employed to mitigate the impacts that failures in global supply chains have on local smart city projects? • How can public procurement boost competition among technology suppliers? • What approaches can be adopted to promote the development of scalable digital solutions tailored to the specific needs of different public organisations? • How can public administration at multiple administrative levels prevent and/or mitigate the widening of socio-economic inequalities and the exacerbation of discriminations following the diffusion of emerging technologies? • How can the administrative structure of the public sector be reformed to enhance coordination among public organisations across different geographical levels? • How can smart city strategies be integrated in the political agendas of public leaders at different administrative levels? • How can alignment be ensured between the smart city project strategies devised by different public organisations? • In what ways do the impacts of existing contextual barriers differ among smart city projects based on their size, geographic scale, and focus?
Resource-related Barriers (shortcomings in the enabling infrastructures, lack of skills and knowledge, shortcomings of supportive tools, lack of funding).	These barriers have critical implications for the long-term viability and scalability of smart city projects, and are affected by economic, cultural, and political contingencies specific to urban contexts.	• What technical and non-technical skills are needed within public sector organisations to successfully manage smart city projects? • What are the most appropriate methods to bridge skills gaps within the public sector? • How can technological path dependencies within the public sector be addressed and their impact on smart city projects mitigated? • What management practices and tools can help public sector organisations to better manage the risk of smart city projects? • How can these tools and practices be developed and disseminated? • What financing models are available to fund smart city projects in the public sector? What are their socio-economic and political implications?
Technology-specific Barriers (financial burden of technology, shortcomings in the design of digital technologies, shortcomings in existing regulations, gaps in existing business models).	These barriers are associated with idiosyncrasies in the development and policymaking of digital technologies, issues that are not specific to public sector organisations but disproportionately affect them over other parties involved in digital transformation projects.	• How can public procurement be leveraged to enhance the user-friendliness and sustainability of digital technologies? • What financial and operational models are more suitable for the development and acquisition of emerging technologies by public sector organisations? • What strategic and operational tools can help to better plan and implement maintenance activities associated with smart city projects? • In what ways can and should public sector organisations contribute to the design of AI-enabled systems and other emerging technologies? • How can smart city projects in the public sector create value for all the stakeholders involved? How can this value be captured? • What strategies can be employed to enhance the technical and data interoperability of the digital technologies implemented within the public sector? • What regulatory approaches are more suitable to address the ethical concerns associated with emerging technologies? • What regulatory approaches are more likely to foster data access and sharing while simultaneously ensuring data protection and cybersecurity?