Entrepreneurial Leadership in Small-Scale Smart City Transformations

Abstract

Background: The smart city concept has become a central framework in contemporary urban governance; however, empirical research predominantly focuses on large metropolitan areas, while small municipalities remain comparatively underexplored. This study examines how citizens of Ivanić-Grad perceive and use smart applications and how these patterns relate to its smart city transition. Methods: A quantitative survey was conducted in 2024 on a convenience sample of 100 residents of Ivanić-Grad. The questionnaire included closed-ended questions on sociodemographic characteristics, awareness and use of smart applications, and attitudes toward further digitalization of local public services. Descriptive statistics, χ² tests of independence, and correlation analysis based on composite indices were applied. Results: The findings reveal statistically significant associations between gender and the use of smart applications (χ² = 5.76; p = 0.016) and between age and the perceived importance of smart applications (χ² = 8.42; p = 0.014). No significant association was identified between education level and application use. Composite index analysis further demonstrated a moderate positive correlation between digital engagement and the perceived importance of smart services (ρ = 0.418; p < 0.001), indicating that experiential interaction with digital services is closely linked to their perceived relevance. Conclusions: The results suggest that smart service adoption in small urban contexts is shaped less by formal educational attainment and more by behavioral engagement and perceived usefulness. The case of Ivanić-Grad highlights the importance of citizen-oriented, accessibility-focused digital leadership strategies in sustaining inclusive smart city development.

1. Introduction

Today, an increasing number of cities in Croatia and worldwide are implementing innovative digital technologies to address pressing urban challenges, including service efficiency, environmental sustainability, and quality of life. Beyond these objectives, smart city initiatives are driven by broader structural trends such as population growth, urbanization, climate pressures, and rising demands on local governance capacity (UN-Habitat, 2024; European Commission, 2022). Over the past three decades, the concept of the “smart city” has evolved into a prominent global framework for rethinking urban development and governance.

Smart cities are commonly understood as urban systems that seek to integrate digital technologies with social and institutional arrangements in order to enhance urban performance and adaptability. Rather than representing a uniform model, smart city development varies considerably across local contexts, depending on institutional capacity, governance structures, and strategic leadership. A key distinction from traditional urban management lies in the use of digital infrastructures and data-driven services to support more responsive interaction between city authorities and citizens.

In this paper, a smart city is conceptualized as a form of urban development that employs advanced digital technologies to improve everyday life. At the same time, smart city development requires strategic coordination, citizen engagement, and entrepreneurial leadership at the local level. The study examines the dimensions of the smart city concept and selected European examples, such as Hartberg (Austria) and Kalasatama (Finland), as comparative reference points for the case of Ivanić-Grad. The main objective is to analyze the strategic development of Ivanić-Grad as a smart city between 2019 and 2024, taking into account socio-economic challenges, achieved outcomes, and opportunities for further development, with particular attention to citizen cooperation and local governance capacity.

Although the smart city literature has expanded substantially, empirical research has largely focused on large metropolitan areas or nationally visible flagship projects. Small and medium-sized cities—especially in Central and South-Eastern Europe—remain comparatively underexplored. In particular, limited attention has been paid to how sociodemographic factors shape the adoption and perceived relevance of smart applications in smaller urban contexts, or how these patterns relate to local smart city strategies and leadership practices. This gap constrains understanding of how citizen behavior and local decision-making jointly influence smart city transitions in small-scale settings such as Ivanić-Grad.

This paper contributes to the literature in three ways. First, it provides empirical evidence on the relationship between citizens’ sociodemographic characteristics and both the use and perceived importance of smart applications in a small Croatian city. Second, it links smart city research with the perspective of entrepreneurial leadership in local government by examining how local authorities can interpret citizen usage patterns as inputs for strategic decision-making. Third, it offers a case-based analysis of how a small city operationalizes its smart city strategy, complementing existing studies that predominantly focus on large and globally visible urban centers.

In response to this research gap, the study defines its objectives as an examination of citizens’ awareness, use, and perceived importance of smart applications in a small-city context, as well as an assessment of how these patterns relate to key sociodemographic characteristics. Rather than focusing on technological performance or system-level indicators, the study adopts a citizen-centered perspective that reflects the social and governance dimensions of smart city development. These objectives directly address the lack of empirical evidence on how smart city strategies are experienced and evaluated by citizens in smaller urban environments and inform the subsequent analytical framework. Guided by this research gap, the study addresses the following question: How do the sociodemographic characteristics and usage patterns of citizens in Ivanić-Grad shape the city’s digital transition, and what are the implications for entrepreneurial leadership in local government? To address this question, four hypotheses (H1–H4) are tested concerning the relationship between gender, age, and education; the use and perceived importance of smart applications; and citizens’ support for further digitalization.

Rather than treating sociodemographic differences in smart application usage as purely descriptive outcomes, this study interprets them as indicators of broader governance dynamics in small-city contexts. In municipalities with limited resources and institutional capacity, patterns of citizen adoption and perceived relevance of digital services can be understood as signals that inform entrepreneurial leadership, strategic prioritization, and adaptive governance. By linking citizen behavior to implications for local leadership and digital strategy, the study advances a process-oriented understanding of smart city development in small urban settings. In small-city contexts, entrepreneurial leadership plays a critical role in shaping not only the supply of digital solutions but also citizens’ willingness to adopt and use them. Patterns of citizen awareness, acceptance, and usage of smart applications can be understood as governance-relevant feedback signals. These signals inform adaptive, entrepreneurial, and digitally oriented leadership strategies at the local level. Specifically, the study pursues four objectives. It examines citizens’ awareness of the smart city concept and their use of existing smart applications in Ivanić-Grad. It further analyzes which types of digital services are most frequently used and explores whether sociodemographic characteristics are associated with differences in use and perceived importance. Finally, the study assesses citizens’ perceived need for further digitalization of local public services.

To address these objectives, the study is guided by the following research questions: (1) How familiar are citizens with the concept of a smart city and the digital services available in Ivanić-Grad? (2) Which smart applications are used most frequently by citizens? (3) Is there a relationship between selected sociodemographic characteristics and the use or perceived importance of smart services? (4) To what extent do citizens express support for further digitalization and the development of new smart applications?

Building on these research questions, four hypotheses are formulated. H1 posits that there is a statistically significant relationship between respondents’ gender and the use of smart applications. H2 assumes a statistically significant association between age and the perceived importance of smart applications in everyday life. H3 hypothesizes that there is no statistically significant relationship between respondents’ educational level and their willingness to use smart services, reflecting literature suggesting that basic digital service adoption in small urban contexts is increasingly shaped by accessibility and perceived relevance rather than formal educational attainment. Finally, H4 is formulated as a descriptive hypothesis, proposing that citizens express a perceived need for further digitalization of local public services and the development of new smart applications.

2. Theoretical Framework

2.1. Definition and Concept of a Smart City

The concept of a smart city refers to a strategic approach to urban development that integrates advanced digital technologies with social, institutional, and environmental dimensions in order to improve efficiency, sustainability, and quality of life. Rather than being defined solely by technological sophistication, smart cities are increasingly understood as complex socio-technical systems in which digital innovation supports broader urban development goals. According to Anđelić et al. (2018), smart cities represent a set of multidisciplinary measures and public policies aimed at the development of both human and technological resources, with sustainable economic growth as a central objective. Several influential frameworks conceptualize smart cities as multidimensional constructs. Nam and Pardo (2011) emphasize the interaction between technology, people, and institutions, highlighting the need to align technological solutions with social capacity and governance structures. Similarly, Caragliu et al. (2009) identify ICT infrastructure, human capital, and social inclusion as key drivers of smart urban development. Bibri (2018) further conceptualizes smart cities as data-driven and analytically enabled urban ecosystems that support resource efficiency, environmental sustainability, and resilience.

Beyond technological integration, contemporary smart city research increasingly emphasizes citizen-centered and inclusive approaches. UNESCO (2023) highlights the importance of media and information literacy as a prerequisite for meaningful citizen participation in smart city initiatives. From this perspective, smart cities are not only digitally enabled environments but also social systems that depend on informed, engaged, and capable citizens. However, the literature consistently points to persistent challenges, including digital inequality, data privacy concerns, and the need for robust legal and regulatory frameworks (Albino et al., 2015). Recent studies further expand the concept of the smart city by explicitly incorporating sustainability and governance considerations. Rudewicz (2023) argues that smart city development increasingly requires balancing technological advancement with environmental objectives, while UN-Habitat (2024) defines smart cities as people-centered systems in which digital technologies function as tools for participation, equity, and long-term resilience. Kumar (2024) cautions that formal participation mechanisms often fail to reach marginalized groups without targeted educational and motivational efforts, underscoring the social dimension of smart city governance.

Technological innovations such as the Internet of Things (IoT), data analytics, and artificial intelligence play an enabling role in smart cities by supporting real-time monitoring, optimization of urban services, and evidence-based decision-making (Gubbi et al., 2013; Rose et al., 2015; Mayer-Schönberger & Cukier, 2013). However, their effectiveness depends on institutional trust, political commitment, and strategic leadership that fosters genuine citizen engagement (Przeybilovicz et al., 2022; Portal & Fabrègue, 2022).

Critical perspectives also contribute to refining the smart city concept. Hollands (2020) warns against conceptual ambiguity and the uncritical use of the term “smart,” emphasizing the need for clarity regarding its social and political implications. Anthopoulos (2017) similarly stresses that smart city initiatives must be grounded in a clear understanding of the role of ICT in urban governance, rather than being driven solely by technological or industrial agendas.

These perspectives suggest that while no single definition of a smart city exists, the concept can be understood as an integrated urban development model that combines digital technologies, citizen participation, and institutional capacity to enhance sustainability, governance quality, and quality of life. This multidimensional understanding provides the conceptual foundation for examining how smart city strategies unfold in smaller urban contexts and how citizen adoption of smart applications interacts with local governance dynamics.

2.2. The Fundamental Pillars and Elements of a Smart City

The fundamental pillars of a smart city represent a structured framework for understanding how technological, social, economic, and environmental dimensions jointly contribute to sustainable and efficient urban development. Rather than functioning independently, these pillars interact to shape the overall performance and adaptability of urban systems. Huovila et al. (2016) emphasize that the success of smart cities can be assessed through multiple interrelated dimensions that together support sustainability, resilience, and effective governance.

One of the most widely used frameworks for conceptualizing smart city dimensions was proposed by Giffinger et al. (2007), who identify six core pillars: smart economy, smart people, smart governance, smart mobility, smart environment, and smart living. These dimensions provide a holistic structure for analyzing smart city development by capturing both technological and socio-institutional aspects.

Table 1. Basic pillars of a smart city and key elements.

Dimension	Key Elements
Smart Economy	Innovation, entrepreneurship, productivity, labor market flexibility, international networking
Smart People	Education, lifelong learning, social and ethnic diversity, creativity, participation in public life
Smart Governance	Citizen participation, transparency, quality of public services, political strategies
Smart Mobility	Accessibility, transport connectivity, ICT infrastructure, sustainable transport systems
Smart Environment	Resource management, environmental protection, pollution reduction, attractiveness of natural conditions
Smart Living	Quality of housing, safety, health, culture, education, social cohesion

Note: Adapted from Giffinger et al. (2007).

summarizes these pillars and their key elements.

Within this framework, the dimensions represent broad categories of urban development, while the elements refer to specific mechanisms and systems through which these dimensions are operationalized. For example, smart governance emphasizes transparency, citizen participation, and the quality of public services, while smart people highlight education, lifelong learning, creativity, and social inclusion. Similarly, smart mobility and smart environment focus on sustainable transport systems and responsible resource management, respectively, whereas smart living addresses quality-of-life aspects such as health, safety, culture, and social cohesion.

A cross-cutting condition enabling the effective functioning of all smart city pillars is infrastructure. Smart city infrastructure encompasses both physical and digital components that support communication, energy efficiency, traffic safety, and data-driven resource management. According to Caragliu et al. (2009), infrastructure has become a key success factor in contemporary smart city development, as it enables real-time data collection, analysis, and coordinated service delivery. Investments in smart infrastructure are also associated with economic development through innovation, job creation, and increased attractiveness for investment (Komninos, 2002). The application of advanced technologies within urban infrastructure improves the efficiency of public services such as transport, healthcare, and education, thereby enhancing overall urban competitiveness (Nam & Pardo, 2011). At the same time, infrastructure development faces challenges related to data privacy, network security, and financial and technical constraints (Anthopoulos, 2017). Angelidou (2016) therefore emphasizes the importance of interdisciplinary integration and collaboration between public authorities, private actors, and academic institutions to ensure that smart city infrastructure supports inclusive and sustainable development.

Recent studies further highlight that the effectiveness of smart city pillars depends not only on technological capacity but also on institutional cooperation and citizen engagement. Przeybilovicz et al. (2022) and Portal and Fabrègue (2022) emphasize that trust and coordination between stakeholders are essential for successful implementation, while Wirtz et al. (2022) show that citizens place greater value on digital services that are transparent and easy to use. From a sustainability perspective, Rudewicz (2023) stress the importance of aligning smart city initiatives with environmental and climate resilience goals. Similarly, UN-Habitat (2024) identifies infrastructure and energy management as central components of urban transformation aimed at reducing emissions and enhancing long-term resilience.

The pillar-based approach provides a comprehensive yet flexible framework for analyzing smart city development. By linking technological infrastructure with social participation, governance quality, and sustainability objectives, this framework offers a structured basis for examining how smart city strategies are implemented and adapted in different urban contexts.

2.3. Key Factors in the Digital Transformation of Cities

Smart city concepts vary considerably, but they all rely heavily on technology. Although a technologically smart city is not the only model, there are different combinations of technological infrastructure that form the basis of a smart city. Successful digital transformation of a smart city involves a clear vision of future development, creating a favorable business and investment environment, efficient use of data, resilience to change, supporting sustainable development, and focusing on the needs of residents. According to Wirtz et al. (2022), the digital transformation of cities requires synergy between technological infrastructure and digital literacy of citizens, because it is the users who give meaning and value to digital tools. New approaches emphasize change management and training citizens to use digital solutions in everyday life. Key factors and their significance and importance in the digital transformation of a city are shown in

Table 2. Three key factors in the digital transformation of a city.

Society	Processes	Technology
Defining the meaning of the community concept with regard to the specific needs and challenges of each identified community. Their coexistence and mutual interaction form the framework for smart city development.	Defining communication and collaboration processes and channels between communities, as well as the ways in which smart city governments communicate with citizens.	Defining technological frameworks represent a later stage in the development of technologically advanced and sustainable communities, based on an understanding of people and processes shaping the urban environment.

Note: Adapted from the Strategy of the Smart City of Grad Ivanić-Grad (2019).

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Every city consists of an invisible network of communication and interaction among its inhabitants and visitors, which depends on the basic city infrastructure, traffic, security, education, economy, trade, accommodation, entertainment, green areas, and the like. Successful interaction between residents, the city administration, and the economy enables successful coexistence and a pleasant life in the local community. In his work, Townsend (2013) explores the complex nature of digital transformation in urban environments. He emphasizes that this transformation is driven by the integration of advanced technologies such as sensors, wireless networks, and data analytics, which cities use to improve service delivery and civic engagement. Townsend’s analysis highlights the key role of technology in shaping the future of cities, suggesting that as mobile networks become more ubiquitous, they not only increase economic opportunity but also transform the way urban environments function and evolve.

Lim et al. (2019) further point out that successful digital transformation requires a “human component”—a combination of digital skills, a culture of trust, and the willingness of public administrations to adapt to open innovation models. Such approaches increase the efficiency and transparency of public services. A key component of a smart city is the ability to adapt and respond to the changing needs of its residents. The integration of smart technologies, such as IoT devices, big data analytics, and artificial intelligence, enables cities to proactively manage resources and services. This results in increased efficiency, reduced costs, and an improved quality of life. Smart cities also promote sustainability through environmental initiatives, such as smart waste management and the use of renewable energy sources, which contribute to environmental conservation and community health.

According to Rudewicz (2023), digital transformation cannot be successful if it is not accompanied by the development of appropriate institutional capacities, regulatory frameworks, and educational policies. He emphasizes the importance of public-private partnerships as a key mechanism for accelerating the implementation of innovative solutions in cities. Park and Fujii (2023) indicate that “living labs” play an increasingly important role in testing digital solutions, as they enable citizens to participate in the co-creation of digital services in a real environment. In this way, the legitimacy and sustainability of digital strategies are enhanced. Accordingly, John et al. (2025), in their study on the application of artificial intelligence in smart cities, highlight six key areas of digital transformation: governance, environment, mobility, economy, energy, and society. According to them, sustainable digital transformation requires technology to serve social goals—not the other way around.

UN-Habitat (2024) points out that 21st-century cities must integrate digital innovation with the Sustainable Development Goals (SDGs), especially through an inclusive digitization approach involving all social groups. Such an approach ensures that digital transformation does not deepen social inequalities but actively reduces them. Basu (2025) introduces the notion of “digitally intelligent communities” that not only use technology but also actively learn from data to improve social cohesion, sustainability, and citizen participation. Such communities shape digital policies that are sensitive to local contexts, not just technological advances.

2.4. Smart Applications and E-Services as One of the Key Tools of Smart Cities

Smart applications and e-services constitute a central operational layer of smart cities, as they translate digital infrastructure, governance frameworks, and strategic objectives into concrete services used by citizens in everyday life. Through smart applications, cities enable more efficient resource management, improved service delivery, and enhanced interaction between citizens, public administration, and other urban stakeholders. Lim et al. (2019) conceptualize digital platforms and applications as a “soft” form of smart city infrastructure, emphasizing their role in facilitating data exchange, coordination, and transparency across urban systems.

From a functional perspective, smart applications support the optimization of urban services in sectors such as mobility, energy, security, waste management, and health. Applications for real-time traffic monitoring, public transport information, and parking management contribute to greater efficiency and reduced congestion, while energy-related applications allow citizens to monitor consumption and manage costs, thereby supporting sustainability objectives and the reduction in CO₂ emissions. In the field of urban safety, digital applications enable faster responses to emergencies and more effective incident management through early warning systems and smart surveillance technologies. Beyond their technical functionality, smart applications play an important role in shaping citizen-government relations. By enabling two-way communication, these applications allow citizens to report infrastructure problems, provide feedback, and participate in urban initiatives. Such interaction contributes to increased transparency, accountability, and participatory governance. In this context, Leclercq and Rijshouwer (2022) link digital platforms to the realization of the “Right to the Smart City”, emphasizing citizen involvement in the co-creation of digital solutions. Bastos et al. (2023) similarly highlight that smart applications can function as instruments of digital inclusion and social cohesion, as they lower barriers to participation for groups previously excluded from decision-making processes.

At the same time, the literature cautions against viewing smart applications as purely technical solutions. Townsend (2013) argues that while data-driven applications and e-services enhance efficiency and civic engagement, they also raise concerns related to privacy, surveillance, and unequal power relations. Green (2019) critically emphasizes that smart applications alone cannot resolve complex urban problems and warns against technol-solutionism, stressing the importance of embedding digital tools within broader social and ethical frameworks. Batty (2018) reinforces this perspective by conceptualizing cities as complex systems in which technologies must be integrated with social, institutional, and spatial dimensions to ensure sustainable outcomes.

Recent studies further indicate that the societal impact of smart applications depends strongly on their design and governance. Wirtz et al. (2022) show that applications developed in accordance with principles of usability, transparency, and accessibility generate higher levels of trust, particularly among older and vulnerable groups. John et al. (2025) emphasize that artificial intelligence-based applications enable predictive planning in areas such as mobility, energy, and security, but only when aligned with clearly defined public objectives. Basu (2025) introduces the concept of “socially intelligent cities”, in which digital services strengthen community engagement and shared responsibility rather than merely increasing efficiency. In line with this view, UN-Habitat (2024) stresses that smart applications should promote inclusion, reduce the digital divide, and contribute to the achievement of Sustainable Development Goal 11.

In the context of this study, smart applications and e-services are therefore understood not only as technological tools but also as key interfaces through which citizens engage with smart city initiatives. Patterns of use and perceived importance of these applications reflect differences in sociodemographic characteristics and levels of digital inclusion, making them analytically relevant for examining citizen support for further digitalization and the role of entrepreneurial leadership in local government. This perspective directly informs the empirical hypotheses (H1–H4), which examine how gender, age, and education relate to the use and evaluation of smart applications in the case of Ivanić-Grad.

2.5. Summary of the Theoretical Framework and Conceptual Model

The reviewed literature suggests that smart city development can be understood along three interrelated axes: (1) the technological dimension, encompassing digital infrastructure and data-driven urban management; (2) the social dimension, including citizen participation, digital inclusion, and the right to the smart city; and (3) the governance dimension, referring to strategic leadership, coordination between stakeholders, and the institutional capacity to implement complex digital initiatives. These dimensions are not independent but mutually reinforcing, particularly in small and medium-sized cities where limited resources require coherent strategic alignment.

In this study, these three axes are integrated into an analytical model that focuses on citizens’ use and perceived importance of smart applications as a central interface between technology, society, and local governance. Building on the literature on smart cities, digital governance, and technology adoption, the model assumes that patterns of use, perceived relevance, and support for further digitalization vary according to sociodemographic characteristics, specifically gender, age, and education. These variations are interpreted not merely as adoption outcomes but as governance-relevant signals for local decision-makers.

In this context, entrepreneurial leadership in local government is understood as the capacity to interpret citizens’ patterns of use, willingness to use, and perceived importance of smart applications as strategic signals, enabling adaptive decision-making, prioritization of digital investments, and the alignment of technological initiatives with locally grounded societal needs. From this perspective, citizen behavior becomes an informational resource that shapes strategic choices in the digital transformation of small cities. Based on prior research suggesting the diffusion of basic digital skills across educational groups in mature digital environments, the study hypothesizes no statistically significant association between education level and the use of smart applications (H3).

Based on this conceptual framework, the study formulates four hypotheses. First, it is assumed that there is a statistically significant relationship between the gender of citizens and their use of smart applications (H1). Second, the model posits a statistically significant relationship between citizens’ age and their perception of the importance of smart applications (H2). Third, drawing on technology adoption and digital inclusion literature, it is hypothesized that there is no statistically significant relationship between citizens’ level of education and their willingness to use smart services (H3). Finally, the framework assumes that citizens express a perceived need for further digitalization of local public services and the development of new smart applications, reflecting broader expectations towards smart city governance (H4). These hypotheses operationalize the theoretical model and are empirically tested in the following methodological section.

3. Materials and Methods

3.1. Research Design and Context

This study employed a quantitative, cross-sectional survey design to explore citizens’ use and perceptions of smart applications in a small-city context. The research was conducted in Ivanić-Grad, Croatia, a small urban municipality that has gradually introduced digital and smart solutions in selected areas of local public services.

The study focuses on citizens as end users of smart and digital services and examines patterns of usage and the perceived importance of such services in everyday life. Given the exploratory nature of the research and its focus on a single local context, the study is designed to provide context-specific insights rather than statistically representative population estimates and should be interpreted analytically.

3.2. Questionnaire and Measures

Data were collected using a structured questionnaire administered online via the Google Forms platform in 2024. The questionnaire consisted of 12 closed-ended questions grouped into three sections: (1) sociodemographic characteristics (gender, age, and education level), (2) use of smart applications (types and frequency of use), and (3) attitudes toward the importance of digital services and the perceived need for further development of smart city solutions. All questionnaire items employed predefined categorical response options (nominal or ordinal), enabling the application of non-parametric statistical tests. The frequency of smart application use was recoded into a binary variable (use vs. non-use) where necessary to meet the assumptions of the chi-square test. The questionnaire was developed based on a review of relevant smart city and digital governance literature and adapted to the local context of Ivanić-Grad. Content validity was ensured by aligning questionnaire items with digital services available in the city and by reviewing item clarity prior to data collection. While the questionnaire primarily consisted of categorical items, selected variables were later combined into descriptive composite indices for exploratory correlation analysis. These indices were constructed as additive measures intended to capture general behavioral patterns rather than reflective latent constructs, and therefore, formal reliability coefficients such as Cronbach’s alpha were not treated as a primary analytical requirement. The full questionnaire is provided in the Supplementary Materials to ensure methodological transparency and replicability.

3.3. Sampling and Data Collection

The study was based on a convenience sample of 100 respondents residing in the area of Ivanić-Grad. Convenience sampling was selected due to the exploratory characteristic of the study and the limited availability of comprehensive sampling frames at the local level. While this approach may introduce sampling bias and limit statistical generalizability, it enables the collection of context-specific insights into citizens’ perceptions and usage patterns of smart applications in a small-city setting.

Participation in the survey was voluntary and anonymous. No personal or sensitive data were collected. Prior to accessing the questionnaire, respondents were informed about the purpose of the research, data confidentiality, and the anonymous nature of participation and provided informed consent. The study adhered to the ethical principles of the Declaration of Helsinki. As no identifiable or sensitive personal data were collected, formal institutional ethical approval was not required under applicable national and institutional research guidelines. The distribution of respondents by gender, age, and education is shown on

Table 3. Distribution of respondents by gender, age, and education.

Variable	Category	Number of Respondents	Share of Respondents (%)
Gender	Men	42	42%
	Women	58	58%
Age	18–30 years	31	31%
	31–50 years	45	45%
	51 and more years	24	24%
Education	High School	36	36%
	College/University	52	52%
	Postgraduate studies	12	12%

Note: Authors’ own research.

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3.4. Data Analysis

Descriptive statistics were used to summarize respondents’ sociodemographic characteristics and patterns of smart application use. To examine associations between categorical variables, chi-square (χ²) tests of independence were applied. The analyses were conducted using Microsoft Excel and SPSS (IBM SPSS Statistics, version 16.0.2 (IBM Corp., Armonk, NY, USA).), and statistical significance was assessed at the conventional threshold of p < 0.05. Prior to conducting the chi-square tests, standard statistical assumptions were verified. The independence of observations was ensured, as each respondent completed the questionnaire only once. Expected cell frequencies were examined to confirm compliance with recommended thresholds. Where necessary, response categories were aggregated to meet minimum expected frequency requirements and to ensure the validity of the statistical tests.

In addition to statistical significance testing, effect sizes were calculated using Cramér’s V coefficient. Reporting effect sizes provides information on the strength and practical relevance of observed associations and complements p-values, particularly in exploratory studies with relatively small samples. Given the categorical nature of the variables and the exploratory design of the study, the analysis focused on identifying patterns and tendencies rather than making strong inferential or causal claims.

4. Results

4.1. General Level of Citizens’ Awareness of Smart Cities

The results indicate that most respondents demonstrate a basic understanding of the smart city concept. When asked whether they were familiar with the term “smart city”, 68% of respondents answered affirmatively, while 32% reported that they had not heard of the term or were unsure of its meaning. These findings suggest a relatively high level of awareness of the smart city concept in Ivanić-Grad, although there remains potential for further public education and communication initiatives. The distribution of responses is presented in

Table 4. Awareness of the smart city concept.

Answer	Number of Respondents	Share of Respondents (%)
Yes	68	68%
No	32	32%

Note: Authors’ own research.

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4.2. Use of Smart Applications by Citizens

Respondents most frequently used applications related to mobility and energy, while a smaller number of citizens used city-specific applications. 54% of respondents use at least one smart app per week, while 17% of respondents reported that they do not use them at all. The structure of the responses is shown in

Table 5. Frequency of smart application use.

Frequency of Use	Number of Respondents	Share of Respondents (%)
Daily	22	22%
Occasional (weekly)	32	32%
Rare (monthly)	29	29%
Never	17	17%

Note: Authors’ own research.

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These data show that the majority of citizens use at least some smart applications, suggesting a generally positive orientation toward digital solutions.

4.3. Correlation Between Gender and the Use of Smart Applications

To test the H1 hypothesis, the χ² independence test was applied. The analysis found a statistically significant association between gender and the use of smart applications (χ² = 5.76; p = 0.016). The strength of this association, measured by Cramér’s V, was 0.24, indicating a weak-to-moderate relationship. Descriptive patterns suggest that women more often reported using applications related to communication and household management, while men more often used applications related to transport and energy. The structure of the responses is shown in

Table 6. Association between gender and smart application use.

Gender	Uses of Applications	Non-Users	Total
Men	34	8	42
Women	49	9	58
Total	83	17	100

Note: Authors’ own research.

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These results confirm Hypothesis H1 and suggest that gender is associated with patterns of smart application use in Ivanić-Grad.

4.4. Composite Indices and Correlation Analysis

In order to provide a more integrative assessment of the relationships between smart service usage and the perceived importance of digital public services, composite additive indices were constructed. These indices enabled a more nuanced interpretation of patterns observed in the descriptive and chi-square analyses. Three indices were developed. The Smart Services Usage Index was calculated as the mean score of reported frequency of use across selected smart city applications and digital public services. The Perceived Importance Index represents the mean of responses measuring the perceived importance of smart applications in everyday life. The Digital Engagement Index was constructed as the mean of items assessing citizens’ behavioral digital engagement, including frequency of use and willingness to interact with digital services. The indices were calculated as mean scores, which ensured comparability across variables measured on similar ordinal scales. Given that the composite indices were constructed as descriptive additive measures intended for exploratory analysis rather than as reflective latent constructs, reliability coefficients such as Cronbach’s alpha were not treated as a primary analytical requirement. The indices were used to capture general patterns of behavioral engagement and perceived importance, providing an integrative analytical perspective.

To further examine the relationship between behavioral digital engagement and the perceived importance of smart services, a Spearman rank-order correlation was conducted, given the ordinal nature of the variables and the exploratory design of the study. The analysis revealed a statistically significant moderate positive correlation (ρ = 0.418, p < 0.001), indicating that respondents who demonstrate higher levels of digital engagement also tend to attribute greater importance to the availability of mobile and digital public services. The correlation matrix is presented in

Table 7. Spearman correlation between digital engagement and perceived importance of smart services.

Variables	1	2
1. Digital Engagement Index	1.000
2. Perceived Importance Index	0.418 ***	1.000

Note: Spearman’s rho coefficients reported. *** p < 0.001. N = 100.

. These findings complement the earlier chi-square results and provide additional explanatory depth beyond simple group comparisons.

4.5. The Connection Between Age and the Perception of the Importance of Smart Applications

To test the H2 hypothesis, the association between age group and attitudes toward the importance of smart applications in everyday life was analyzed using χ² independence test. The results showed a statistically significant association (χ² = 8.42; p = 0.014). The calculated Cramér’s V value was 0.21, indicating a weak association. Younger respondents (18–30 years) more frequently perceived smart applications as an essential part of everyday life compared to older groups. The structure of responses is presented in

Table 8. Association between age and the perceived importance of smart applications.

Age Group	Important	Partially Important	Doesn’t Matter	Total
18–30 years	27	4	0	31
31–50 years	37	7	1	45
51+ years	11	9	4	24
Total	75	20	5	100

Note: Authors’ own research.

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The results support the H2 hypothesis and suggest that generational differences are associated with variation in the perceived importance of smart applications. Younger respondents perceive smart applications as an important part of everyday life, whereas older citizens tend to use them more selectively, which has important implications for the design of inclusive smart city policies.

4.6. Linking Education and the Use of Smart Services

The results of the χ² test for the H3 hypothesis (χ² = 1.92; p = 0.38) show that there is no statistically significant correlation between the level of education and the use of smart applications. Regardless of the level of education, respondents showed similar patterns in using digital services. The structure of the responses is shown in

Table 9. Association between education level and smart application use.

Educational Level	Use Apps	Doesn’t Use Apps	Total
High school	30	6	36
College/university	43	9	52
Postgraduate	10	2	12
Total	83	17	100

Note: Authors’ own research.

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This finding suggests that the use of smart applications does not significantly differ across education levels within the observed sample. In other words, formal educational attainment does not appear to be a decisive factor in explaining digital service adoption in the context of Ivanić-Grad.

When considered alongside the correlation analysis presented in Section 4.4, these results indicate that behavioral digital engagement and perceived importance may play a more substantial role in shaping smart service use than formal education. This pattern highlights the importance of accessibility, usability, and perceived relevance of digital services in small-city environments.

4.7. Citizens’ Recommendations for the Development of Smart Solutions in Ivanić-Grad

The majority of the respondents (72%) chose at least one of the three most frequently mentioned measures—an application for monitoring public transport, digital reporting of communal problems, or an e-payment system for local fees—indicating a relatively high level of interest of citizens in the development of additional digital services. The respondents also emphasized the need for better informing citizens about existing e-services and for greater cooperation between the city and educational institutions on the development of digital literacy.

Table 10. Citizens’ priorities for smart solution development.

Proposed Measures	Share of Respondents (%)
Application for public transport	61%
Digital reporting of communal problems	54%
e-Payment of local fees	47%
Digital guide to cultural events	32%
Education on digital skills	28%

Note: Authors’ own research.

shows the priorities of citizens for the development of smart solutions in Ivanić-Grad, according to the results of a survey conducted in 2024. The largest share of the respondents (61%) believes that the introduction of a mobile application for monitoring public transport is the most necessary digital solution. In second place is the digital reporting of communal problems (54%), which shows the expressed need of citizens for more efficient communal services and two-way communication with the city administration. The system for e-payment of local fees (47%) was also identified as a significant priority, especially due to the potential to increase transparency and save time in the performance of administrative tasks. A smaller, but still relevant, number of citizens highlighted the importance of a digital guide for cultural events (32%), indicating an interest in cultural offerings and information platforms that connect citizens and institutions. The lowest share of responses was recorded for digital skills education (28%), which may mean that citizens take digital literacy for granted or that they are more focused on practical services rather than learning processes.

The results provide partial descriptive support for H4, indicating that a majority of citizens express a clear perceived need for further digitalization and the development of additional smart services. Citizens’ priorities (public transport tracking applications, utility problem reporting, and e-payment services) indicate a practical orientation of user needs. These findings may assist local governments in planning and prioritizing the introduction of new digital services.

5. Discussion

5.1. Summary of Main Findings

This study contributes to the smart city and digital transformation literature by providing empirical evidence from a small-city context in South-Eastern Europe. By focusing on Ivanić-Grad, the research responds to calls for more context-sensitive analyses of smart city development beyond large metropolitan areas. The findings show that citizens demonstrate a relatively high level of awareness of the smart city concept and a generally positive orientation toward the use of digital services, although usage patterns vary across sociodemographic groups.

The results reveal that gender and age are associated with differences in how citizens engage with and perceive smart applications. Gender-related differences suggest that everyday practices and social roles shape patterns of digital service use, while generational differences indicate that younger citizens are more likely to perceive smart applications as an integral part of everyday life. At the same time, the absence of substantial differences related to education level indicates that formal educational attainment may not be a decisive predictor of smart application use in small-city contexts. Instead, factors such as accessibility, usability, and perceived relevance appear to play a more prominent role. Additionally, the correlation analysis between the Digital Engagement Index and the Perceived Importance Index revealed a statistically significant, moderate positive association. This finding demonstrates that experiential engagement with digital services is closely related to how citizens evaluate their relevance and importance. The relationship points to the possibility that direct behavioral exposure may reinforce perceived utility, thereby strengthening patterns of adoption in small-city environments.

In addition, the findings show that a majority of citizens express a clear interest in the further development of digital public services. Respondents prioritized practical and problem-oriented solutions, particularly applications related to public transport monitoring, reporting communal issues, and e-payment systems. These preferences indicate that citizens’ expectations toward smart city development are strongly oriented toward improving everyday interactions with local public administration rather than toward technologically complex or symbolic innovations.

The findings underscore that smart city development in small urban contexts is a socially embedded process shaped by citizen behavior, local needs, and institutional capacity. Rather than following a uniform or technology-driven trajectory, digital transformation in Ivanić-Grad reflects differentiated patterns of adoption and perception that provide valuable insights into local governance dynamics.

5.2. Theoretical Contribution and Practical Implications

The findings offer several theoretical and practical contributions to the smart city and public administration literature. From a theoretical perspective, the results support integrated smart city frameworks that emphasize the interdependence of technological innovation, social inclusion, and governance capacity (Angelidou, 2016; Bibri, 2018). The observed variations in smart application use and perceived importance across age and gender groups highlight the limitations of universalist smart city models and reinforce the need for citizen-centered and context-sensitive approaches, particularly in small and medium-sized municipalities.

Importantly, the absence of a strong association between education level and smart application use suggests that digital engagement in small-city contexts may increasingly reflect the normalization of basic digital competencies across the population. This finding aligns with arguments that digital literacy has become a broadly diffused social capability rather than a privilege tied to formal education, especially where digital services are designed for general use and embedded in everyday administrative practices. These results can also be interpreted through the lens of the Technology Acceptance Model (TAM), which conceptualizes technology adoption as a function of perceived usefulness and perceived ease of use. The observed absence of education-based differences, combined with the moderate association between digital engagement and perceived importance, suggests that perceived utility and experiential familiarity may operate as stronger predictors of smart service adoption in small urban contexts than formal educational background. This finding further refines TAM-based interpretations in small-city contexts by indicating that perceived usefulness may be socially shaped through everyday digital exposure rather than primarily determined by formal educational capital.

From a practical perspective, the results underscore the central role of entrepreneurial and digital leadership in local government. In small urban contexts, smart city development depends less on advanced technological infrastructures and more on the capacity of local authorities to interpret citizen behavior, communicate the value of digital services, and coordinate stakeholders across sectors. Patterns of citizen adoption and expressed needs function as governance-relevant signals that can guide strategic prioritization, service design, and resource allocation. For Ivanić-Grad, this implies that future smart city initiatives should focus on practical, user-oriented solutions that address clearly articulated citizen needs while also strengthening communication and participation mechanisms. Targeted awareness-building and support initiatives, particularly for older age groups, may further enhance inclusive digital transformation. More broadly, the findings contribute to the literature on entrepreneurial leadership in public administration by illustrating how citizen engagement and feedback can inform adaptive, relational, and learning-oriented leadership strategies in small-city settings. Rather than viewing leadership as a top-down driver of digital transformation, the study highlights a dynamic process in which citizen behavior actively shapes the direction and pace of smart city development.

5.3. Limitations and Directions for Future Research

Despite its contributions, this study has several limitations that should be considered when interpreting the findings. First, the research is based on a relatively small convenience sample drawn from a single small-city setting, which limits the generalizability of the results beyond Ivanić-Grad. Accordingly, the findings should be understood as exploratory and context-specific, providing analytical insights rather than statistically representative conclusions. Second, the study relies on self-reported, cross-sectional survey data, which may be subject to response bias and does not allow observation of changes in perceptions and behavior over time. In addition, while the analytical approach included descriptive statistics, χ² tests of independence, and correlation analysis based on composite indices, the exploratory design and reliance on categorical variables limit the explanatory depth of the findings. The study does not employ multivariate modeling or causal inference techniques and, therefore, cannot establish directional relationships or fully capture more complex interactions among variables. Although the additional correlation analysis provides greater analytical nuance, the results should be interpreted as indicative rather than causally explanatory. Although the inclusion of correlation analysis provides additional explanatory depth, the exploratory design and reliance on relatively simple statistical procedures limit the ability to model complex interactions among variables or establish directional causality. Additionally, the statistically significant relationships identified in the study demonstrated weak to moderate effect sizes, which further limits the strength of generalizable conclusions.

Future research could address these limitations by employing larger and more diverse samples, including comparative analyses across multiple small and medium-sized cities. Longitudinal research designs would allow for the examination of dynamic changes in citizens’ adoption and evaluation of smart applications over time. Furthermore, mixed-method approaches that combine quantitative surveys with qualitative interviews or focus groups could provide deeper insights into citizens’ motivations, experiences, and expectations, as well as into the role of entrepreneurial leadership in shaping inclusive and sustainable smart city strategies.

5.4. Relevance to Digital Leadership and Innovation in Public Administration

The empirical findings indicating differentiated adoption of smart applications across demographic groups highlight the importance of adaptive and citizen-oriented digital leadership. The findings directly relate to the thematic focus of the Special Issue on leadership and innovation in public administration. The case of Ivanić-Grad demonstrates that smart city development in small urban contexts depends primarily on institutional capacity, strategic leadership, and citizen engagement rather than on advanced technological solutions alone. Local leaders play a crucial role as facilitators of digital innovation, coordinators of cross-sector collaboration, and promoters of inclusive governance. The observed correlation between citizen engagement and perceived importance further implies that leadership strategies should prioritize visible, user-oriented benefits capable of reinforcing experiential adoption dynamics. This study, therefore, underscores the importance of leadership-oriented approaches to smart city development, highlighting the need for public administration to adopt proactive, entrepreneurial, and citizen-centered strategies in managing digital transformation.

6. Conclusions

The findings of this study demonstrate that citizens in Ivanić-Grad show a growing recognition of the importance of digital technologies and smart solutions in everyday life. While gender and age were associated with variations in smart service engagement, formal educational attainment was not a decisive predictor of digital application use. Instead, the results indicate that accessibility, usability, and perceived relevance of services play a more substantial role in shaping adoption patterns in small urban contexts.

Importantly, the correlation analysis between digital engagement and perceived importance revealed a moderate positive association, suggesting that experiential interaction with digital services reinforces their perceived value. This finding supports the interpretation that behavioral exposure and perceived usefulness—rather than structural sociodemographic characteristics alone—are central to understanding smart service adoption in smaller municipalities.

The expressed citizen priorities, particularly regarding public transport monitoring, digital reporting of communal issues, and e-payment systems, further confirm a practical and user-oriented orientation toward smart city development. These results highlight that digital transformation in small urban environments depends less on technological complexity and more on visible benefits, everyday functionality, and responsive governance.

Although based on a limited sample, the study contributes to the literature on smart cities and entrepreneurial leadership by demonstrating that citizen adoption patterns function as governance-relevant signals. In small-city contexts, digital leadership must therefore prioritize experiential value, accessibility, and continuous engagement in order to sustain inclusive and adaptive smart city development.

Future research should extend this analysis through larger samples, comparative case studies, and longitudinal designs to better understand the evolving relationship between citizen engagement, perceived usefulness, and digital governance strategies.