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Abstract

Digital transformation, driven by disruptive technologies, is significantly altering economies and societies worldwide. For public organisations, embracing disruptive technologies can enhance efficiency, productivity, transparency, and service delivery. However, successful integration of these technologies requires comprehensive readiness at an organisational level. Accordingly, this study aims to identify and understand the critical factors that enable or constrain municipalities in developing their disruptive technology capability. The empirical analysis is based on data collected through a comprehensive questionnaire distributed to public managers in Slovenian municipalities between April and July 2023, yielding a sample of 143 responses. The results of a structural equation modelling approach reveal the complex relationships between identified disruptive technology capability determinants and components. These findings offer valuable insights for researchers, practitioners, and policymakers aiming to navigate the complex landscape of disruptive technology adoption within public organisations.

Key Points for Practitioners

To enhance disruptive technology capabilities and drive disruptive technology adoption, the results indicate to:

- promote a proactive leadership style that fosters innovation,

- leverage government incentives,

- respond to citizen expectations,

- prioritise investments in skills development and infrastructure, and

- implement robust data management practices to enhance service delivery and decision-making.

Keywords

disruptive technologies public administration local government public managers structural equation modeling Slovenia

1. Introduction

Over the past two decades, governments have recognised the importance of accelerating digital transformation, which is revolutionising economies and societies through rapid technological advances (Li et al., 2023). This transformation is also driven by disruptive technologies (DT), which have the potential to induce significant societal changes. “Disruptive technology” refers to innovations that markedly alter processes and operations within a specific sector. Such technologies can challenge the status quo, reshape individuals’ lives and work, and instigate substantial organisational transformations (Burri, 2017; Christensen & Raynor, 2003; Manyika et al., 2013). Nevertheless, disruption is characterised by rapid or dramatic changes. For a long time, technology has been recognised as a source of disruption in people's lives, effecting change at all scales (Wimmer et al., 2020).

Examples of disruptive technologies include artificial intelligence (AI), the Internet of Things (IoT), blockchain technology (BCT), virtual and augmented reality (VR, AR), unmanned aerial vehicles (UAV), and more. These technologies can potentially drive significant changes in the private and public sectors (Brennan et al., 2019; Rodríguez Bolívar & Scholl, 2019). As these technologies continue to evolve, they impact citizens’ expectations, encompassing technological advancements and organisational structure shifts (Brennan et al., 2019; Burri, 2017; Manyika et al., 2013). This evolving landscape necessitates thoughtful consideration of the implications of these technologies and identifies opportunities for adaptation and innovation. The benefits of employing disruptive technologies in public operations are manifold: improving efficiency and productivity, reducing costs, enhancing service delivery, enabling data-driven decision-making, fostering a transparent and accountable work environment, and encouraging a culture of innovation to prepare organisations for future challenges (Brennan et al., 2019; Burri, 2017; Manyika et al., 2013; Wimmer et al., 2020). Realising these benefits will require different technological infrastructures, impeccable data governance and management, new leadership styles, innovative decision-making processes, and alternative methods of organising and delivering services (Gil-Garcia et al., 2018).

However, to effectively introduce disruptive technologies, public managers must comprehensively understand the potential applications of these technologies. They must also grasp how disruptive technologies interact with key organisational elements such as structure (Rudko et al., 2021), processes (Waardenburg et al., 2021), employees (Pan & Froese, 2022), and organisational culture (Farrow, 2020). Most critically, managers must assess their organisation's readiness and capacity for adopting disruptive technologies, considering possible internal and external constraints (Tomaževič et al., 2024). The most advanced technologies cannot guarantee effective and efficient operations unless the organisation has matured to a point where it is fully capable of adopting disruptive technologies. Hence, it is important to understand the determinants that influence this capability.

To investigate the factors that enable or constrain municipalities in developing their disruptive technology capabilities, we adapted the model proposed by Mikalef et al. (2022). The model was originally based on the Technology-Organization-Environment framework (TOE) (Tornatzky & Fleischer, 1990). This study extended the model to encompass disruptive technology aspects and all organisational elements—people, culture, structure, processes, infrastructure—to understand how different forces within these categories shape disruptive technology capability. The original study's focus on AI capabilities represents a critical area of research, given the rapid advancement and integration of AI technologies in public sector operations (Mikalef et al., 2022). The adaptation to include other disruptive technologies beyond AI acknowledges the broader spectrum of technological innovations that can impact municipal operations. The broader focus acknowledges that the journey towards becoming a smart city is multifaceted, requiring the integration of various disruptive technologies that extend well beyond AI (Radu, 2020).

To achieve our study objectives, we designed a comprehensive questionnaire distributed to public managers in Slovenian municipalities. The Slovenian government operates with two primary levels: national (state) and local (municipal). Local self-government is organised around municipalities, which gained prominence after the 1993 and 1994 reforms. Initially, Slovenia had 147 municipalities, which grew to 212 by 2011. The areas of work where municipalities have tasks following their general jurisdiction are defined illustratively in the Local Self-Government Act. Specifically, most municipal tasks are determined by laws governing individual areas of public administration (spatial planning, construction of facilities, local public environmental protection services, primary education, childcare, and others). The state may delegate tasks to municipalities if it also provides the necessary funds. State-local relations reflect decentralisation, with municipalities operating autonomously, but state interference is allowed only by law, ensuring local participation in its drafting (Kovač & Virant, 2011). For municipalities to effectively deploy disruptive technologies and for governmental entities (ministries) to facilitate the utilisation of these technologies, it is essential to understand the primary drivers and inhibitors. This understanding allows for targeted support of these transformative processes. Accordingly, this study poses two research questions:

RQ1. What factors influence municipalities to develop disruptive technology capabilities?

RQ2. How do these factors affect municipalities in the development of disruptive technology capabilities?

The remainder of this paper is structured as follows: Following the introduction, the second section offers a literature review and the development of hypotheses. Subsequent to this, the methodology is outlined. Empirical results are presented in section four, followed by a discussion of these findings in the fifth section. The paper concludes by synthesising all insights gathered throughout the study in the final section.

2. Literature Review and Hypotheses Development

The development of DT capability heavily relies on foundational concepts like technological, IT, and digital capabilities. These capabilities, enhanced by Mikalef and Gupta (2021) into AI capability and rooted in Information Systems (IS) research, represent an organisation's ability to effectively utilise new technologies beyond just adopting them (Conboy et al., 2020; Handali et al., 2020). Technological capability, a key competitive factor in the private sector and a source of public value in government settings, supports process enhancements, increases value and reduces costs (Coombs & Bierly III, 2006; Dierickx & Cool, 1989). Its positive impact on organisational performance is well-documented (Deeds, 2001; Zahra, 1996). Similarly, building on dynamic capability theory, digital capability is crucial for fostering innovative processes and adapting to changes (Teece & Pisano, 1994). Equally, IT capabilities involve leveraging technological and complementary resources (Bharadwaj, 2000; Liu et al., 2013).

Mikalef and Gupta (2021) defined AI capability as the organisation's ability to select, orchestrate, and use AI-specific resources effectively, a concept rooted in the Resource-Based View (RBV), which argues that internal resources are vital to enhancing performance and competitiveness (Barney, 2001; Butler & Murphy, 2008; Gupta & George, 2016). RBV is particularly effective in fostering unique, hard-to-replicate capabilities essential for AI implementation in dynamic technological environments (Bromiley & Rau, 2016). Finally, DT capability includes, besides AI, other emerging technologies (e.g., Internet of things, autonomous vehicles, blockchain, drones, digital twins, augmented and virtual realities), requiring a comprehensive strategic vision that also includes cultural readiness to embrace significant transformations in the public sector (Brennan et al., 2019; Burri, 2017; Wimmer et al., 2020) and encompasses data, technology and basic resources on the tangible side of capabilities and technical and business skills for the human-related capabilities. Based on the past literature, tangible capabilities are critical for the successful implementation of disruptive technologies. Data serves as the backbone of DT by providing the foundation for training, validating, and deploying models that drive advanced analytics and decision-making. High-quality data is essential for generating insights, predicting outcomes, and supporting real-time operations, with the success of these technologies depending heavily on data's availability, quality, and granularity (Chen et al., 2012; Gandomi & Haider, 2015; Gubbi et al., 2013; Janssen et al., 2012; Russell & Norvig, 2016). Alongside data, basic resources such as sufficient financial capital, human resources with the necessary expertise, and adequate time are essential to foster such innovation, meaning that an organisation needs to be capable of allocating enough staff hours and project timelines to successfully implement and adapt to DT (Ahlgren et al., 2016; Alsheibani et al., 2018; Desouza et al., 2020; Duan et al., 2019; Shin, 2017). Moreover, technology and technological infrastructure, including advanced computing systems, cloud availability, high-speed internet, and secure data storage, is vital for running AI, IoT, and other disruptive technologies smoothly (Stankovic, 2014; Wirtz et al., 2019). Regarding human-related capabilities, organisations implementing DT must balance technical and business skills. Technical skills, such as those held by data scientists, engineers, and IT specialists, are essential for handling vast amounts of data, developing algorithms, and integrating technology solutions effectively. These skills involve expertise in data analysis, algorithm development, and programming languages, as well as in cybersecurity and cloud computing, ensuring the technical soundness of the deployed systems (Dwivedi et al., 2021; Whang et al., 2023). At the same time, business skills, including strategic management, domain knowledge, project leadership and change management, are necessary to align technological innovations with organisational objectives. Leaders with these skills help identify key areas for technology application, manage resources, and ensure the smooth adoption of the technologies within organisational processes (Spector & Ma, 2019; Tomaževič et al., 2024; Valle-Cruz et al., 2024).

The literature review identifies key factors that either facilitate or hinder the development of DT capabilities within municipalities. Utilising the Technology-Organization-Environment (TOE) framework (Tornatzky & Fleischer, 1990), it highlights the primary influences on such capabilities. Tornatzky and Fleischer's (1990) TOE framework, originally designed for private sector firms and presented as the context of innovation decisions, outlines three key contexts influencing technology adoption and implementation: organisational, technological, and environmental (Figure 1). While intended for businesses, this framework has been widely applied to public sector organisations as well (e.g., Defitri et al., 2020; Neumann et al., 2024; Pudjianto et al., 2011; Zhang et al., 2017). In their description, the organisational context includes an organisation's size, structure, centralisation, human resources, and internal processes like decision-making and communication. It also covers external communication links, which connect organisations to suppliers, knowledge producers, and other external sources. The technological context focuses on the internal and external technologies relevant to the organisation, including current technologies and practices, as well as technologies that exist and are available to the organisation. The environmental context refers to the industry, competition, external resources and regulatory bodies. The environment provides both opportunities (resources and information) and constraints (regulations, capital availability, and market forces) that influence innovation (Tornatzky & Fleischer, 1990).

Figure 1.

The context of technological innovation (original title) or the TOE framework. Source: Tornatzky and Fleischer (1990).

The TOE framework was the most suitable choice for our case because it comprehensively addresses not only the technological factors but also the organisational and environmental aspects that are critical for municipalities adopting disruptive technologies. Besides the TOE framework, several other models and theories exist for the adoption and implementation of technology. For example, the Technology Acceptance Model (TAM) is used to model the user acceptance of information systems, with two main factors, namely perceived ease of use and perceived usefulness (Davis et al., 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT), a technology acceptance model that uses the three constructs of performance expectancy, effort expectancy and social influence to explain users’ intentions to use an information system (Venkatesh et al., 2003). These two models focus primarily on individual user acceptance. The Diffusion of Innovations (DOI) theory centres on the diffusion process and explains the speed at which new technologies and ideas spread in organisations and how and why they spread (Rogers, 2003). However, the TOE framework captures the broader organisational context, including infrastructure, processes, and external pressures, which are crucial for understanding how municipalities can develop their disruptive technology capabilities in a complex and regulated environment. It stands out as one of the leading theoretical models for understanding the adoption and diffusion of technology across organisations. Its flexibility allows for the integration of specific contextual variables relevant to the particular technology or organisation under consideration (Wang & Lo, 2016). While not exhaustive, these factors are crucial in determining a municipality's capacity to develop disruptive technology capabilities, especially regarding human and tangible resources (Mikalef et al., 2022).

2.1 Perceived Direct Benefits (PB)

Government employees are crucial in adopting and using DT in the public sector, with their attitudes and willingness heavily influencing the success of digital transformation efforts (Ahn & Chen, 2022; Myeong et al., 2020). Research highlights that seeing direct benefits motivates technology adoption (Cruz-Jesus et al., 2019). Public managers of municipalities play a significant role in the decision-making process for DT adoption. Given that municipalities in Slovenia operate autonomously (Kovač & Virant, 2011), their managers’ perceptions of benefits and potential transformative impacts being key determinants in developing DT capabilities (Mikalef et al., 2019; Schaefer et al., 2021). Their effective planning, implementation, and evaluation shape how DTs will be utilised. Positive attitudes towards these technologies can lead to successful integration, while scepticism may impede adoption (Ahn & Chen, 2022; Sun & Medaglia, 2019). The role of fostering trust and positive expectations in the adoption process is vital, as trust often forms before direct experience with the technology and is influenced by media representation (Fritzsche & Duerrbeck, 2020; McKnight et al., 2002). Familiarity with an innovation boosts adoption likelihood, and high initial trust can reduce perceived risks and highlight benefits (Bedué & Fritzsche, 2022; Hengstler et al., 2016; Lu et al., 2011). Therefore, the subsequent (sub)hypotheses were formulated:

H1: Perceived Direct Benefits (PB) have a positive impact on Disruptive Technology (DT) Capability, including Technical Skills (TS) (H1a), Business Skills (BS) (H1b), Data (D) (H1c), Technology (TE) (H1d) and Basic Resources (BR) (H1e).

2.2 Perceived Financial Costs (PF)

The perceived financial costs associated with the adoption of new technologies have been a prominent theme in prior research (Baker, 2012). From the perspectives of public managers of municipalities, these costs are often viewed as barriers to the adoption process, especially when the measurable benefits of new digital solutions are not immediately apparent (Kuan & Chau, 2001). Scholars argue that the advantages of integrating technologies into public services should outweigh the expenses incurred in their development and operation (Batubara et al., 2018; Hou, 2017). Public organisations, largely dependent on government funding and tax revenues, usually face budget constraints that limit their flexibility in deploying new technologies (Misuraca et al., 2020). The introduction of new technologies also involves costs of supporting services and operational processes. These costs include overhead and personnel expenses, posing significant challenges for many organisations attempting to adopt these technologies. The scarcity of financial resources is a critical barrier, particularly for investments in high-cost technologies like AI. Municipalities with insufficient financial capabilities face considerable difficulties in undertaking such investments (Nam et al., 2021; Rjab et al., 2023; Ryan, 2019; Şerban & Lytras, 2020; Viale Pereira et al., 2017; Yigitcanlar & Cugurullo, 2020). As a result, the following (sub)hypotheses were proposed:

H2: Perceived Financial Costs (PF) have a negative impact on Disruptive Technology (DT) Capability, including Technical Skills (TS) (H2a), Business Skills (BS) (H2b), Data (D) (H2c), Technology (TE) (H2d) and Basic Resources (BR) (H2e).

2.3 Organisational Innovativeness (OI)

Reintroducing the concept of organisational innovativeness into theoretical discussion highlights that openness to innovation is crucial for adoption decisions (Lai & Guynes, 1994; Oliveira & Martins, 2010). In this context, we refer to municipalities as the organisations under study. Organisational innovativeness, reflecting an entity's cultural values and predispositions, plays a significant role in the willingness to adopt technological innovations, including DT (Aboelmaged, 2014; Misuraca et al., 2020; Smit et al., 2020). Organisations with an innovative culture are more likely to experiment with new technologies and invest in pioneering solutions (Lin et al., 2020). DT adoption often requires collaboration across multiple stakeholders, particularly in data integration and platform sharing, demanding new governance models and solutions for acceptability challenges (Konashevych, 2017; Ølnes et al., 2018). Leadership's commitment to innovation is vital in overcoming barriers to adoption, aligning technology with strategic goals, and ensuring sufficient resources and data readiness for effective technology implementation (Horani et al., 2023; Hsu et al., 2019). Consequently, organisations prioritising innovation are typically more inclined to adopt DT than those with a conservative approach. Therefore, the subsequent (sub)hypotheses were formulated:

H3: Organizational Innovativeness (OI) has a positive impact on Disruptive Technology (DT) Capability, including Technical Skills (TS) (H3a), Business Skills (BS) (H3b), Data (D) (H3c), Technology (TE) (H3d) and Basic Resources (BR) (H3e).

2.4 Perceived Government Pressure (PG)

Adoption of DT is heavily influenced by perceived government pressure, serving as a significant environmental factor. Regulatory norms and standards from the government create institutional pressures essential for adopting these technologies (Kuan & Chau, 2001). In this context, Perceived Government Pressure refers to the pressure exerted by national-level government institutions on municipalities. Often, state institutions establish digitalisation strategies that municipal managers feel compelled to follow, aligning local actions with national objectives (Mikalef et al., 2019). This requires developing organisational frameworks, ensuring data availability, and setting up pilot infrastructures for DT projects (Andreasson & Stende, 2019). However, factors such as legal uncertainties and frequent policy shifts can create a volatile environment that impedes DT readiness, affecting skills development and data governance (Agrawal et al., 2023). Additionally, government actions can strain municipalities by affecting budgets, staffing, and processes (Wirtz & Müller, 2019). In response, municipalities may reorganise resources to comply with governmental demands, thus facilitating the infrastructure for DT applications (Ølnes & Jansen, 2017). The absence of a comprehensive legal framework specifically for DT further complicates these issues, impacting the understanding of rights and responsibilities necessary for successful DT implementation (Balfaqih & Alharbi, 2022; Dash & Sharma, 2022; Hagendorff, 2020; Nam et al., 2021; Rjab et al., 2023). As a result, the (sub)hypotheses were presented as follows:

H4: Perceived Government Pressure (PG) has a positive impact on Disruptive Technology (DT) Capability, including Technical Skills (TS) (H4a), Business Skills (BS) (H4b), Data (D) (H4c), Technology (TE) (H4d) and Basic Resources (BR) (H4e).

2.5 Perceived Citizen Pressure (PC)

Research highlights that social influence significantly impacts the decision-making process for adopting new technologies, driven largely by public opinion and societal expectations (Tung & Rieck, 2005). This is particularly true in municipalities, which play a key role in public service delivery and are directly influenced by the needs and opinions of citizens; hence, the Perceived Citizen Pressure refers specifically to the pressure exerted by citizens on municipalities. As primary service recipients, citizens’ heightened expectations are shaped by digital advances in the private sector, setting higher standards for public services (Bousdekis & Kardaras, 2020; Zavolokina et al., 2023). Consequently, municipalities face significant public pressure to adopt new, efficient technologies. This societal influence not only drives municipal decisions but also accelerates the adoption of DT, encouraging a citizen-responsive digital transformation. Such an approach aims to meet current and anticipate future needs, promoting a proactive, citizen-centric public administration (Bousdekis & Kardaras, 2020; Bullock et al., 2020; Schaefer et al., 2021; Zavolokina et al., 2023). Therefore, the subsequent (sub)hypotheses were formulated:

H5: Perceived Citizen Pressure (PC) has a positive impact on Disruptive Technology (DT) Capability, including Technical Skills (TS) (H5a), Business Skills (BS) (H5b), Data (D) (H5c), Technology (TE) (H5d) and Basic Resources (BR) (H5e).

2.6 Government Incentives (GI)

Government incentives can be seen as catalysts for municipalities to implement disruptive technologies, offering the essential resources required to develop and integrate new technologies into their operations (Komninos, 2006). Given that municipalities depend on government support by the national-level government institutions to navigate new directions in technological adoption, the level of support they receive is expected to significantly influence the extent to which they promote the development of disruptive technologies. Moreover, governments often offer incentives tied to financial benefits to encourage the pursuit of significant objectives, which facilitate the adoption of new technologies or the recruitment of skilled managers and staff in public organisations (Misuraca et al., 2020). In the context of adopting new technologies, the availability of financial support along with skilled managers and employees is crucial for municipalities navigating through digital transformation processes (Niehaves et al., 2019; Schaefer et al., 2021).

Previous studies have identified the lack of financial opportunities as a primary obstacle already in the adoption of e-government, emphasising that substantial change at the community level demands considerable resources from diverse funding sources (Beynon-Davies & Martin, 2004; Hahamis et al., 2005; Previtali & Bof, 2009). Government incentives will be even more crucial when it comes to the adoption of disruptive technologies, as these incentives not only provide the necessary financial backing but also facilitate the organisational transformations needed for such advanced technologies. Hence, the (sub)hypotheses were given as follows:

H6: Government Incentives (GI) have a positive impact on Disruptive Technology (DT) Capability, including Technical Skills (TS) (H6a), Business Skills (BS) (H6b), Data (D) (H6c), Technology (TE) (H6d) and Basic Resources (BR) (H6e).

Based on the developed hypotheses proposing relationships between identified determinants of disruptive technology capability and its components, a conceptual model for municipalities was proposed to offer a comprehensive framework for understanding how various technological, organisational, and environmental factors contribute to the development of disruptive technology capability, defined through human and tangible resources (Figure 2).

Figure 2.

Proposed research model. Note: Only the main hypotheses are presented for simplicity. Source: Presentation adapted from Mikalef et al. (2022).

3 Methodology

3.1. Data Collection and Sample

For this study, data were collected through a web-based survey deployed using the open-source web application 1KA (One Click Survey; www.1ka.si). The survey was conducted in June 2023. The target populations for this survey were public managers, i.e., the directors of municipalities and heads of departments within these municipalities. These public managers play a critical role in guiding and implementing (disruptive) technological initiatives, requiring both a deep understanding of municipal operations and a solid foundation in technical expertise. This is especially true in smaller municipalities with limited staff and resources, where public managers often take on both managerial and technical responsibilities.In Slovenia, there are 212 municipalities representing critical local interfaces between citizens and the public administration (Kovač, 2014). To uphold the integrity of the research process, special attention was devoted to ensuring the anonymity of respondents’ data. All 212 municipalities were invited to participate in the survey, but not all responded. There are 143 representatives of municipalities included in the survey (Table 1). Most of the respondents were female (62.0%), with the majority having 11 to 30 years of work experience, although their managerial experience varied. The majority of respondents work in municipalities with up to 20 employees (43.4%) or 21 to 40 employees (33.6%), lack a dedicated department or employees for IT or digitalisation (67.1%), and rely on external IT or digitalisation contractors (93.7%).

Table 1.
Demographic Characteristics of the Respondents.

Demographic Characteristics Frequency Percentage (%)

Gender

Male 51 35.9

Female 88 62.0

Prefer not to answer 3 2.1

Work Experience

Less than 10 years 10 7.0

11–20 years 51 35.7

21–30 years 50 35.0

More than 30 years 32 22.4

Managerial Experience

Less than 1 year 12 8.5

1–5 years 32 22.5

6–10 years 29 20.4

11–15 years 26 18.3

16–20 years 16 11.3

More than 20 years 27 19.0

Number of employees

Less than 20 62 43.4

21–40 48 33.6

41–60 12 8.4

61–80 8 5.6

81–100 3 2.1

More than 100 10 7.0

IT or digitalisation department or employee(s)

Department 18 12.6

Employee(s) 29 20.3

No employee(s) 96 67.1

External IT or digitalisation contractors

Yes 134 93.7

No 9 6.3

Source: Survey data.

Demographic Characteristics	Frequency	Percentage (%)
Gender
Male	51	35.9
Female	88	62.0
Prefer not to answer	3	2.1
Work Experience
Less than 10 years	10	7.0
11–20 years	51	35.7
21–30 years	50	35.0
More than 30 years	32	22.4
Managerial Experience
Less than 1 year	12	8.5
1–5 years	32	22.5
6–10 years	29	20.4
11–15 years	26	18.3
16–20 years	16	11.3
More than 20 years	27	19.0
Number of employees
Less than 20	62	43.4
21–40	48	33.6
41–60	12	8.4
61–80	8	5.6
81–100	3	2.1
More than 100	10	7.0
IT or digitalisation department or employee(s)
Department	18	12.6
Employee(s)	29	20.3
No employee(s)	96	67.1
External IT or digitalisation contractors
Yes	134	93.7
No	9	6.3

3.2. Survey Instrument

The questionnaire used for this study was initially validated by Mikalef et al. (2022); however, their work focused on Artificial Intelligence (AI). To ensure that our study comprehensively addressed the nuances of disruptive technology and all organisational elements, we adapted the questionnaire significantly. This adaptation was undertaken by a multidisciplinary research group composed of law, management, and statistics experts. The collective expertise ensured that the questionnaire was finely tuned to measure disruptive technology capabilities effectively.

The revised questionnaire comprises 46 items, organised into seven thematic sections. The first section, perceived financial costs, contained items (3) measuring high initial implementation costs, ongoing maintenance expenses, and employee training costs regarding disruptive technologies. The second section, organisational innovativeness, included items (3) measuring the extent to which leadership actively accepts, seeks, and generates innovative ideas for digital development. This was followed by a section on perceived citizen pressure, more precisely, items (4) measuring citizens’ desire for digital services, their frequency of inquiries regarding digital services, their preference for using digital services over physical ones, and their regular requests for co-creating local policies. The fourth section concerned perceived government pressure (3 items) about the government's provision of data security and protection policies for municipalities, the establishment of regulations or laws concerning digital services for citizens and utilising disruptive technologies, and the clarity of these regulations or laws. The next section, perceived direct benefits, included items (4) measuring the expected improvements in data accuracy, data security, municipal operational efficiency, and document processing speed due to the use of disruptive technologies. This was followed by a section on government incentives, more precisely, items (3) measuring the extent to which the government (ministry) provides sufficient recommendations, financial resources, and strategic guidance to promote the use of disruptive technologies. Finally, disruptive technology capability comprised several sections (technology (3 items), data (4 items), basic resources (3 items), and human skills (both technical (5 items) and business (6 items)), including diverse items (5) measuring the municipality's access to and integration of various data sources, the computing and internet capabilities, and data storage infrastructure; the digital skills of employees and their effectiveness in data processing, as well as the recruitment of experts skilled in advanced and disruptive technologies; the availability of financial, human, and time resources for further digital development; the leadership's approach to guiding digital development, its recognition of the benefits of disruptive technologies, and its collaboration with stakeholders; and the leadership's response to employee resistance to digital changes, its awareness of the need for change management, and its commitment to new digital values.

The final version of the questionnaire consists of 46 items formulated as statements. Respondents expressed their agreement with the statements on a 5-point Likert scale: 1—Strongly disagree, 2—Disagree, 3—Neutral, 4—Agree, 5—Strongly agree, with the option of choosing the answer “I do not have enough information”. The Likert scaling method for measuring responses to a statement is widespread in the social sciences, including public administration research (Croasmun & Ostrom, 2011). The full version of the questionnaire, including a short survey description, is available from the authors.

3.3. Scale Reliability and Validity

In order to assess the validity of the measurement model, structural equation modeling (SEM) using IBM SPSS AMOS 27.0 (Arbuckle, 2021) was conducted. The maximum likelihood estimation method was applied to examine the relationships between observed variables and latent constructs. The baseline version of the measurement model was derived from the proposed research model (Figure 2). Modifications of the measurement model were carried out in order to achieve adequate validity and reliability of the model (Table 2). Out of a total of 46 items included in the conceptual model, 27 were retained in the final version (adjusted model), while 11 were excluded. Additionally, two constructs measuring Basic Resources and Technology were combined in the final version of the model. The measurement characteristics of the adjusted model are presented below. Scale reliability was assessed with Cronbach's alpha coefficient (Cα)—all coefficients are above 0.7, indicating adequate internal consistency of the latent variables. Convergent validity was assessed through composite reliability (CR), where values of CR (all are above 0.8) indicate the observed variables measure a latent factor well. Average variance extracted (AVE) values mainly exceed the threshold value of 0.50, with the exception of two latent variables (Data in Basic Resources & Technology), where the AVE is just below the threshold.

Table 2.
Reliability and Validity Test.

Construct Item Std. Estimate CR AVE Cα

Perceived Direct Benefits (PB) We expect that the use of advanced technologies will help improve data accuracy (e.g., without incorrect and duplicate data). 0.871 0.961 0.770 0.928

We expect that the use of advanced technologies will help improve data security. 0.758

We expect that the use of advanced technologies will help improve the efficiency of the municipality's operations. 0.939

We expect that the use of advanced technologies will help expedite document processing (applications, contracts, etc.). 0.929

Perceived Financial Costs (PF) The use of advanced technologies requires high implementation costs. 0.787 0.892 0.614 0.820

The use of advanced technologies requires high ongoing (maintenance) costs. 0.878

The use of advanced technologies requires high costs for employee training. 0.673

Organisational Innovativeness (OI) The leadership accepts proposals, ideas, and initiatives for digital development. 0.825 0.945 0.757 0.897

The leadership actively seeks innovative ideas for digital development. 0.903

The leadership generates innovative ideas for digital development. 0.864

Perceived Government Pressure (PG) The government (ministry) provides policies on security and data protection in municipalities. 0.536 0.871 0.584 0.766

The government (ministry) has established regulations/laws regarding digital services for citizens. 0.847

Regulations/laws regarding digital services for citizens are understandable. 0.838

Perceived Citizen Pressure (PC) Citizens want us to provide services digitally. 0.728 0.895 0.503 0.841

Citizens regularly inquire about digital services. 0.754

We perceive that citizens prefer to use services digitally rather than physically. 0.740

Citizens regularly inquire about co-designing local policies. 0.511

We perceive that citizens trust the use of digital services. 0.593

We perceive that the use of digital services among citizens is becoming increasingly popular. 0.714

Government Incentives (GI) The government (ministry) provides sufficient recommendations to promote the use of advanced technologies. 0.931 0.922 0.700 0.863

The government (ministry) allocates sufficient financial resources (transfer revenues) to ensure the use of advanced technologies. 0.643

The government (ministry) provides sufficient strategic guidance to promote the use of advanced technologies. 0.904

Business Skills (BS) The leadership directs digital development towards addressing business challenges. 0.877 0.948 0.636 0.912

The leadership, together with stakeholders (employees and citizens), recognises the benefits of using advanced technologies. 0.726

The leadership is aware of the benefits of using advanced technologies. 0.681

The leadership anticipates the future needs of stakeholders (employees and citizens) and appropriately guides digital development. 0.860

The leadership coordinates digital development activities with stakeholders (employees and citizens). 0.860

The leadership is favourable towards the use of advanced technologies. 0.758

Technical Skills (TS) Our employees have adequate digital skills for the needs of existing digital operations. 0.833 0.890 0.616 0.798

Our employees in the field of IT or digitalisation are proficient in using advanced technologies (such as artificial intelligence, including machine learning, natural language processing, deep learning, predictive analytics). 0.595

Our employees are efficient in processing and analysing data. 0.894

Data (D) We have access to a wide range of various national registers and databases. 0.610 0.810 0.470 0.759

We integrate external data with internal data. 0.780

The municipality's operations are based on data-supported decision-making. 0.575

We integrate data from multiple internal sources into a data warehouse. 0.584

Batch querying is enabled in national registers and databases (querying data for multiple subjects simultaneously). 0.663

Basic Resources & Technology (BR) We have sufficient financial resources available for further digital development. 0.804 0.836 0.454 0.742

We have enough employees to implement advanced technologies. 0.571

We have enough available time for further digital development. 0.808

We have the necessary computer and internet capability to perform existing digital solutions/services. 0.427

Note: The items are presented in a condensed form. Source: Survey data.

Construct	Item	Std. Estimate	CR	AVE	Cα
Perceived Direct Benefits (PB)	We expect that the use of advanced technologies will help improve data accuracy (e.g., without incorrect and duplicate data).	0.871	0.961	0.770	0.928
We expect that the use of advanced technologies will help improve data security.	0.758
We expect that the use of advanced technologies will help improve the efficiency of the municipality's operations.	0.939
We expect that the use of advanced technologies will help expedite document processing (applications, contracts, etc.).	0.929
Perceived Financial Costs (PF)	The use of advanced technologies requires high implementation costs.	0.787	0.892	0.614	0.820
The use of advanced technologies requires high ongoing (maintenance) costs.	0.878
The use of advanced technologies requires high costs for employee training.	0.673
Organisational Innovativeness (OI)	The leadership accepts proposals, ideas, and initiatives for digital development.	0.825	0.945	0.757	0.897
The leadership actively seeks innovative ideas for digital development.	0.903
The leadership generates innovative ideas for digital development.	0.864
Perceived Government Pressure (PG)	The government (ministry) provides policies on security and data protection in municipalities.	0.536	0.871	0.584	0.766
The government (ministry) has established regulations/laws regarding digital services for citizens.	0.847
Regulations/laws regarding digital services for citizens are understandable.	0.838
Perceived Citizen Pressure (PC)	Citizens want us to provide services digitally.	0.728	0.895	0.503	0.841
Citizens regularly inquire about digital services.	0.754
We perceive that citizens prefer to use services digitally rather than physically.	0.740
Citizens regularly inquire about co-designing local policies.	0.511
We perceive that citizens trust the use of digital services.	0.593
We perceive that the use of digital services among citizens is becoming increasingly popular.	0.714
Government Incentives (GI)	The government (ministry) provides sufficient recommendations to promote the use of advanced technologies.	0.931	0.922	0.700	0.863
The government (ministry) allocates sufficient financial resources (transfer revenues) to ensure the use of advanced technologies.	0.643
The government (ministry) provides sufficient strategic guidance to promote the use of advanced technologies.	0.904
Business Skills (BS)	The leadership directs digital development towards addressing business challenges.	0.877	0.948	0.636	0.912
The leadership, together with stakeholders (employees and citizens), recognises the benefits of using advanced technologies.	0.726
The leadership is aware of the benefits of using advanced technologies.	0.681
The leadership anticipates the future needs of stakeholders (employees and citizens) and appropriately guides digital development.	0.860
The leadership coordinates digital development activities with stakeholders (employees and citizens).	0.860
The leadership is favourable towards the use of advanced technologies.	0.758
Technical Skills (TS)	Our employees have adequate digital skills for the needs of existing digital operations.	0.833	0.890	0.616	0.798
Our employees in the field of IT or digitalisation are proficient in using advanced technologies (such as artificial intelligence, including machine learning, natural language processing, deep learning, predictive analytics).	0.595
Our employees are efficient in processing and analysing data.	0.894
Data (D)	We have access to a wide range of various national registers and databases.	0.610	0.810	0.470	0.759
We integrate external data with internal data.	0.780
The municipality's operations are based on data-supported decision-making.	0.575
We integrate data from multiple internal sources into a data warehouse.	0.584
Batch querying is enabled in national registers and databases (querying data for multiple subjects simultaneously).	0.663
Basic Resources & Technology (BR)	We have sufficient financial resources available for further digital development.	0.804	0.836	0.454	0.742
We have enough employees to implement advanced technologies.	0.571
We have enough available time for further digital development.	0.808
We have the necessary computer and internet capability to perform existing digital solutions/services.	0.427

In order to assess discriminant validity, the AVE scores and the squared correlation between the latent variables were examined (Table 3). The AVE of each latent variable is greater than the squared correlation between latent variables in pairs, with the exception of the squared correlation between Organisational Innovativeness and Business Skills, which are highly (but not excessively highly) correlated.

Table 3.

Discriminant Validity.

Construct	PB	BS	PF	OI	PG	PC	GI	TS	D	BR
PB	0 . 770
BS	0.111	0.636
PF	0.001	0.126	0.614
OI	0.147	0.830	0.092	0.757
PG	0.143	0.088	0.008	0.150	0.584
PC	0.201	0.001	0.007	0.055	0.143	0.503
GI	0.001	0.058	0.081	0.080	0.159	0.013	0.700
TS	0.017	0.205	0.049	0.160	0.372	0.054	0.110	0.616
D	0.019	0.129	0.081	0.160	0.188	0.032	0.017	0.379	0.470
BR	0.011	0.091	0.008	0.097	0.097	0.025	0.294	0.179	0.048	0.454

Note: Non-bold values are squared correlations between constructs, and bold diagonal values are the AVE scores. Source: Survey data.

Additionally, commonly used fit indices were used to assess the goodness-of-fit of a model. It was found that a model demonstrates an acceptable fit based on several indices, such as CMIN/DF of 1.822, RMSEA of 0.076 and Parsimonious Normed Fit Index (PNFI) of 0.600, while relative indices (CFI = 0.847; TLI = 0.815) indicate borderline acceptable fit.

The validity and reliability of the measurement were evaluated from multiple perspectives. We confirmed an adequate level of internal consistency of the constructs, the presence of composite reliability, and both discriminant and convergent validity. Additionally, we achieved an appropriate data fit, enabling us to proceed with the development of the measurement model to test the proposed hypotheses.

4. Results

Due to the lack of adequate validity and reliability of an initial model found through confirmatory factor analysis (CFA), which was performed in the statistical software IBM SPSS AMOS 27.0 (Arbuckle, 2021) using the maximum likelihood estimation method, an adjusted model was created. Table 4 presents predicted correlations between constructs as part of the hypotheses presented in the conceptual model. Non-significant correlations are removed from the final version of the model.

Table 4.
Hypothesis Statuses.

Hypothesis Hypothesis Status

Perceived Direct Benefits → Technical Skills H1a: supported

Business Skills H1b: not supported

Data H1c: not supported

Basic Resources & Technology H1d, H1e: not supported

Perceived Financial Costs → Technical Skills H2a: not supported

Business Skills H2b: not supported

Data H2c: supported

Basic Resources & Technology H2d, H2e: not supported

Organisational Innovativeness → Technical Skills H3a: supported

Business Skills H3b: supported

Data H3c: not supported

Basic Resources & Technology H3d, H3e: not supported

Perceived Government Pressure → Technical Skills H4a: supported

Business Skills H4b: not supported

Data H4c: supported

Basic Resources & Technology H4d, H4e: not supported

Perceived Citizen Pressure → Technical Skills H5a: not supported

Business Skills H5b: supported

Data H5c: not supported

Basic Resources & Technology H5d, H5e: not supported

Government Incentives → Technical Skills H6a: not supported

Business Skills H6b: not supported

Data H6c: not supported

Basic Resources & Technology H6d, H6e: supported

Source: Survey data.

Hypothesis	Hypothesis Status
Perceived Direct Benefits	→	Technical Skills	H1a: supported
Business Skills	H1b: not supported
Data	H1c: not supported
Basic Resources & Technology	H1d, H1e: not supported
Perceived Financial Costs	→	Technical Skills	H2a: not supported
Business Skills	H2b: not supported
Data	H2c: supported
Basic Resources & Technology	H2d, H2e: not supported
Organisational Innovativeness	→	Technical Skills	H3a: supported
Business Skills	H3b: supported
Data	H3c: not supported
Basic Resources & Technology	H3d, H3e: not supported
Perceived Government Pressure	→	Technical Skills	H4a: supported
Business Skills	H4b: not supported
Data	H4c: supported
Basic Resources & Technology	H4d, H4e: not supported
Perceived Citizen Pressure	→	Technical Skills	H5a: not supported
Business Skills	H5b: supported
Data	H5c: not supported
Basic Resources & Technology	H5d, H5e: not supported
Government Incentives	→	Technical Skills	H6a: not supported
Business Skills	H6b: not supported
Data	H6c: not supported
Basic Resources & Technology	H6d, H6e: supported

Figure 3 and Table 5 present path coefficients that appear statistically significant in the analysis using structural equation modeling. Technical Skills of the public managers are highly positively impacted by perceived government pressure (β = 0.631; p < 0.001), while the impact of organisational innovativeness is weaker, although positive (β = 0.274; p = 0.003). However, the Technical Skills of employees are negatively impacted by the direct benefits of disruptive technologies usage (β = -0.223; p = 0.013), indicating that the more positive direct benefits of disruptive technologies usage are, the less technical skills of employees are perceived as adequate for disruptive technologies usage. Business skills of managers are greatly impacted by organisational innovativeness (β = 0.974; p < 0.001) but negatively impacted by citizen pressure (β = -0.191; p < 0.001). The data appears to be impacted by Perceived government pressure (β = 0.509; p < 0.001) much stronger than by perceived financial costs (β = 0.253; p = 0.011), while the Basic resources & Technology are impacted solely by Government Incentives, but the impact appears to be strong (β = 0.552; p < 0.001).

Figure 3.

Adjusted structural model. Note: Only statistically significant results are presented for simplicity. *p < 0.05, **p < 0.01 ***p < 0.001. Source: Survey data.

Table 5.

Unstandardised and Standardised Estimates and the Proportion of Variance Explained for Adjusted Structural Model.

DT Capability Determinants		DT Capability Components	Unstd. Estimates	S.E.	p	Std. Estimates	R²
Perceived Direct Benefits	→	Technical skills	−0.240	0.096	0.013	−0.223	0.508
Perceived Government Pressure	→		0.659	0.115	***	0.631
Organisational Innovativeness	→		0.250	0.084	0.003	0.274
Organisational innovativeness	→	Business skills	0.678	0.078	***	0.974	0.895
Perceived Citizen Pressure	→	Business skills	−0.159	0.048	***	−0.191	0.895
Perceived Government Pressure	→	Data	0.489	0.101	***	0.509	0.350
Perceived Financial Costs	→	Data	0.294	0.116	0.011	0.253	0.350
Government Incentives	→	Basic resources & Technology	0.614	0.11	***	0.552	0.305

Note: ***p < 0.001. Source: Survey data.

5. Discussion

This study set out to uncover the pivotal factors that either facilitate or impede the development of disruptive technology capabilities in Slovenian municipalities. Utilising a structural equation modeling approach, our empirical analysis juxtaposes our hypotheses against a backdrop of 143 responses from public managers. These hypotheses were grounded in existing theories and literature on the adoption and integration of disruptive technologies in public sector organisations.

Our analysis uncovers several critical insights. Although it was anticipated that Perceived Direct Benefits would positively influence DT capability components (H1), only the impact on Technical Skills (H1a) is significant. Surprisingly, this impact is negative, as indicated by a coefficient of −0.223, contradicting theoretical expectations, such as those proposed by Cruz-Jesus et al. (2019), who argued that recognizing direct benefits promotes technology readiness and adoption. The unexpected negative relationship between Perceived Direct Benefits and Technical Skills could be due to an “expectation-reality gap,” where anticipated benefits exceed current employee skills, revealing a deficit in technical competencies (Prashar et al., 2023; Zhao et al., 2021). The public sector is also known to lag behind the private sector regarding digital skills demand (Mankevich et al., 2023). This gap becomes more apparent as managers recognize the full potential of these technologies, leading to a heightened awareness of existing skills shortages. This increased awareness may reflect not a decline in skills but rather a reassessment of the competencies needed to effectively utilize new technologies.

However, Technical Skills are proven to be positively influenced by Organisational Innovativeness, with a coefficient of 0.274 (H3a). This suggests that innovative organisational cultures, with their openness to learning, are more conducive to enhancing technical skills (Lin et al., 2020). Furthermore, Perceived Government Pressure has an even stronger effect (H4a), with a coefficient of 0.631, highlighting how government initiatives are instrumental in driving technical skill development. Programs such as Slovenia's Digital Strategy for 2030 (NIO, 2023) and the Administration Academy's training in data science and business intelligence are pivotal in preparing civil servants for digital tasks (OECD, 2021). These programs are crucial for meeting government demands for digital transformation. The initial negative impact of Perceived Direct Benefits on Technical Skills actually underscores a critical adoption phase where recognising benefits highlights existing skill gaps, prompting further investment in skills and innovation, as supported by the positive effects of Organisational Innovativeness and Perceived Government Pressure.

Since Perceived Financial Costs are often viewed as barriers to technology adoption, especially in public organizations with tight budget constraints (Baker, 2012; Misuraca et al., 2020), we expected them to hinder DT capability development (H2). However, it was surprisingly supported in the context of Data (H2c), with a positive coefficient of 0.253. One explanation for this result could be that when municipalities anticipate high financial costs, they may prioritize investments in data management as a critical step toward leveraging digital transformation. Robust data management, crucial for AI and the Internet of Things (IoT), has become a priority since many digital transformation barriers revolve around data integration, standards, and sharing (Campion et al., 2022; Mikalef et al., 2019). As Campion et al. (2022) noted, resistance to data sharing remains a key challenge for AI adoption.

Nevertheless, Data capability is positively impacted by Perceived Government Pressure (H4c), as demonstrated by a coefficient of 0.509. Supporting results in Technical Skills and Data (H4a and H4c) confirm that Public Government Pressure drives certain aspects of DT capability, consistent with literature indicating that government policies are key drivers for technological advancements in the public sector (Kuan & Chau, 2001; Mikalef et al., 2019). Municipalities respond to this pressure by enhancing their technical and data capabilities, often aligning with national digitalization strategies (Andreasson & Stende, 2019). The analysis shows that Public Government Pressure significantly boosts data capabilities, aligning with initiatives like the EU’s Regulation 2023/138, which mandates the management of high-value datasets (European Commission, 2023), and Slovenia's Digital Strategy for 2030 (NIO, 2023), emphasizing data as a strategic asset and promoting a dynamic data ecosystem. These regulations require comprehensive data management and promote innovation, ensuring municipalities align with broader digital transformation goals.

The influence of Organisational Innovativeness on DT capability components (H3) shows two positive impacts. While the first one, on Technical Skills (H3a), has already been discussed, the second, on Business Skills (H3b), receives strong support with a high coefficient of 0.974. This implies that an innovative organisational culture significantly enhances the business skills necessary for implementing disruptive technologies. Specifically, such environments enhance leadership's ability to steer digital development toward addressing business challenges, recognize and leverage the benefits of advanced technologies alongside stakeholders, anticipate future needs, and coordinate digital initiatives effectively. Innovative organisations tend to have flexible, dynamic decision-making processes that can rapidly adapt to new business models and environmental demands, which is crucial for navigating the complexities of disruptive technologies (Smit et al., 2020).

Business Skills are also influenced by the Perceived Citizen Pressure (H5). Contrary to expectations that such pressure would enhance DT capabilities, the results indicated a negative impact, with a coefficient of −0.191 for Business Skills. This finding is, however, not surprising for Slovenian municipalities and is also aligned with insights from a recent study by Aristovnik et al. (2024), which highlighted a notable hesitancy among Slovene municipalities, especially in rural areas, to embrace digital transformation. This reluctance is also attributed to the preference of local citizens for in-person services over digital alternatives, thereby reducing the motivation for public managers to advance digitalisation. This scenario is further exacerbated by a significant digital skills gap among the population in Slovenia, as evidenced by the Digital Economy and Society Index (2022), which reports that nearly half of Slovenia's workforce possesses insufficient digital skills.

Lastly, the results reveal that Government Incentives play a critical role in supporting Basic Resources and Technology (H6a) within municipalities. The positive influence, reflected in a coefficient of 0.552 for these areas, underscores targeted government support's pivotal role in providing the essential infrastructure and technological foundations necessary for DT capability. This substantiation aligns with the literature that emphasises the necessity of a robust infrastructural base as a precursor to wider technological adoption and capability enhancement in public organisations (Niehaves et al., 2019; Schaefer et al., 2021). The result suggests that when municipalities receive government incentives specifically allocated for technological advancements and basic resource acquisition, there is a tangible improvement in their capacity to integrate and leverage new technologies.

6. Conclusion

The urgency for municipalities to adopt disruptive technologies is highlighted by the rapid technological evolution that has reshaped economies and societies over the last two decades. Innovations like AI, IoT, blockchain, and others hold the potential to transform operational processes and service delivery in the public sector. To effectively leverage these technologies, municipalities must navigate a complex array of factors, including technological readiness, data governance, leadership styles, and organisational readiness. The study aimed to explore how the identified factors influence municipalities in developing their disruptive technology capabilities. Prompted by the increasing need for municipalities to transition toward disruptive technology-based solutions, the study emphasises the importance of external and in-house competencies for deploying such solutions. This emphasis is rooted in the overarching goal of enhancing the quality and efficiency of services provided by these public entities. We adapted the enablers and inhibitors to disruptive technology capability based on Mikalef et al. (2022) and the Technology-Organization-Environment (TOE) framework (Tornatzky & Fleischer, 1990) by extending the model to all organisational elements (people, culture, structure, processes, and technological infrastructure). Our empirical investigation, based on a sample of 143 responses from public managers in Slovenia, yielded notable insights regarding key enablers and inhibitors of DT capabilities. These findings carry significant implications for researchers exploring the value-generation potential of novel disruptive technologies in public organisations, as well as for practitioners and policymakers steering the transition into the disruptive technology era.

The results of the study reveal interesting findings. Perceived direct benefits negatively impact the assessment of technical skills in disruptive technology capabilities. Higher expectations by public managers regarding technology outcomes lead to a harsher evaluation of employees’ technical competencies. This phenomenon occurs because managers, recognising the potential of disruptive technologies, become increasingly aware of the existing skills gap among their staff. Moreover, the impact of perceived financial costs on disruptive technology capability is also unexpectedly positive regarding data capability. This suggests that public managers view financial costs as an investment rather than a burden. In other words, they may prioritise spending on data management capabilities as a foundational step towards leveraging DT. Furthermore, organisational innovativeness impacts technical and especially business skills. The impact is positive, suggesting that the more the organisation shows innovative qualities, the better the technical skills of employees and the better the business skills of managers. Additionally, government pressure significantly affects the employees’ technical skills and data capabilities. Results suggest that the greater the pressure from the government, the more employees are technically skilled in using technologies, and data maturity is also higher. Moreover, perceived citizen pressure has a negative impact on business skills, which is not so unexpected for Slovenian municipalities, especially rural ones. This reluctance can be attributed to the preference of local citizens for in-person services over digital alternatives, thereby reducing the motivation for public managers to advance digitalisation. Finally, government incentives (financial resources allocation, recommendations, and directions) positively affect basic resources and technology.

This study, while extensive, is not without limitations that future research could address. First, the reliance on subjective evaluations from public managers may introduce biases that could affect the interpretation of the findings. Additionally, the study focuses on municipalities oriented towards public service provision rather than policymaking (Aristovnik et al., 2021), which limits the generalisability of the results to other types of public administrations or governance levels. Furthermore, while the sample of the study is methodologically sufficient to reveal general patterns of DT capabilities in municipalities, it is too limited to conduct segmental analyses between different types of municipalities.Finally, the cross-sectional nature of this study captures only a snapshot in time, which may not fully represent the dynamic process of technology adoption and capability development.

Future research could address these limitations by employing longitudinal study designs to track changes and developments over time, providing a more dynamic view of how disruptive technology capabilities evolve. Expanding the scope of the study to include a wider range of public organisations would help to generalise the findings and broaden the understanding of disruptive technology determinants and components across different governmental contexts. Moreover, increasing the sample size or using stratified sampling would allow for better representation of various types of municipalities (e.g., urban vs. rural, large vs. small) and enable more robust and reliable segmental analyses. Finally, qualitative methods could be integrated to offer richer, more contextual insights into managerial perceptions and on-the-ground challenges and drivers associated with adopting disruptive technologies in public administration.

Overall, this study contributes to the ongoing dialogue among scholars, practitioners, and policymakers focused on the digital transformation of public administrations. By providing empirical evidence and nuanced analyses, it aids in shaping policies and practices that foster an innovative, efficient, and responsive public sector ready to meet the demands of a rapidly advancing technological landscape.

Footnotes

Acknowledgements

The authors acknowledge the financial support from the Slovenian Research and Innovation Agency (research core funding No. P5-0093, project No. J5-2560 and project No. J5-50165). In preparing this manuscript, the authors utilised ChatGPT, version 4o, developed by OpenAI, for limited and supplementary purposes. Specifically, ChatGPT was employed to check the grammar, enhance clarity, and polish the language in certain sections of the manuscript. It must be stressed that the role of the ChatGPT was minor and purely supportive in nature. The core content of the manuscript, including all scientific interpretations, conclusions, and critical revisions, is the exclusive output of the human authors. ChatGPT did not contribute to the intellectual content or scientific insights of the manuscript.

ORCID iDs

Aleksander Aristovnik

Eva Murko

Nina Kristl

Dejan Ravšelj

Author Contributions/CRediT

Conceptualisation: A.A.; Methodology: N.K.; Software: N.K.; Validation: N.K., E.M.; Formal analysis: N.K.; Investigation: A.A., D.R., E.M.; Resources: D.R., E.M.; Data Curation: D.R., E.M., N.K.; Writing—Original Draft: E.M., D.R., N.K.; Writing—Review & Editing: A.A., E.M.; Visualisation: D.R.; Supervision: A.A.; Project administration: A.A., D.R.; Funding acquisition: A.A.

Funding

Conflicting Interests

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

Author Biographies

Aleksander Aristovnik (PhD) is a professor from the Faculty of Public Administration, University of Ljubljana, Slovenia. His research interests are public sector economics, public sector management, macroeconomics, and economics of the European integration. In recent years, his research focus has extended to digitalisation issues in the public sector and public administration, including disruptive technologies and artificial intelligence. He has published a couple of papers in refereed academic journals, including Learning and Instruction, Journal of Business Economics, Plos One, Interactive Technology and Smart Education, Police Practice and Research, Total Quality Management & Business Excellence, Socio-Economic Planning Sciences, etc.

Eva Murko is a young researcher at the Economics and Public Sector Management unit at the Faculty of Public Administration, University of Ljubljana. Her research focus has intertwined public governance, digitalisation, and the use of artificial intelligence in public administration. In 2024, she has completed the Oxford Artificial Intelligence Programme. She is particularly interested in understanding the potential impact of artificial intelligence on socio-economic welfare, and her dissertation topic for a doctoral degree delves deeper into this area.

Nina Kristl is an assistant for methodology and statistics at the Faculty of Public Administration, University of Ljubljana, Slovenia. She is a doctoral student in Statistics, focusing on the field of psychometrics. Her work includes the use of predictive models in social science research, with an emphasis on measuring the quality of public sector services. As an experienced researcher, she has a strong background in applying statistical methods to evaluate public service delivery and is currently developing a methodology for measuring public service excellence.

Dejan Ravšelj (PhD) is an assistant professor from the Faculty of Public Administration, University of Ljubljana, Slovenia. His research interests are public sector economics, public sector management, and performance measurement. In recent years, his research focus has extended to digitalisation issues in the public sector and public administration, including disruptive technologies and artificial intelligence. He has published a couple of papers in refereed academic journals, including Learning and Instruction, Journal of Business Economics, Plos One, Socio-Economic Planning Sciences etc.

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Disruptive Technology Capabilities in Local Governments: An Empirical Study

Abstract

Keywords

1. Introduction

2. Literature Review and Hypotheses Development

2.2 Perceived Financial Costs (PF)

2.3 Organisational Innovativeness (OI)

2.4 Perceived Government Pressure (PG)

2.5 Perceived Citizen Pressure (PC)

2.6 Government Incentives (GI)

3.1. Data Collection and Sample

3.3. Scale Reliability and Validity

6. Conclusion

Footnotes

Acknowledgements

ORCID iDs

Author Contributions/CRediT

Funding

Conflicting Interests

Author Biographies

References