1. Introduction

Smart cities have emerged as one of the most important urban development paradigms in contemporary cities (Caragliu et al., 2011; Batty et al., 2012). Advances in information and communication technologies (ICT), artificial intelligence (AI), big data, and the Internet of Things (IoT) have accelerated the adoption of smart city initiatives across the world (Kitchin, 2014; Albino et al., 2015). Governments and local authorities increasingly promote smart city policies as strategies for improving urban efficiency, sustainability, public service delivery, environmental management, and regional competitiveness (Harrison et al., 2010; Nam and Pardo, 2011). In South Korea, smart city development has become a major national and local policy agenda associated with digital transformation, urban innovation, and future-oriented regional development (Yigitcanlar et al., 2018; Ministry of Land, Infrastructure and Transport [MOLIT], 2019; Han and Kim, 2022). Recent Korean geographical studies have similarly emphasized the importance of regional competitiveness, industrial transformation, innovation clusters, and spatially differentiated development processes in shaping future regional development trajectories (Son et al., 2022; Gu et al., 2024).

Existing smart city research has largely emphasized technological infrastructure, data-driven governance, urban efficiency, and sustainability outcomes (Batty et al., 2012; Kitchin, 2014). Many studies have focused on topics such as ICT integration, transportation systems, environmental monitoring, digital public services, and smart governance mechanisms (Caragliu et al., 2011; Nam and Pardo, 2011). More recent studies have further emphasized citizen participation, collaborative governance, stakeholder interaction, and the socio-technical nature of smart city development processes (Grossi and Welinder, 2024; Kumar, 2024; Kummitha, 2025). Other studies have examined policy implementation, technology adoption, or administrative effectiveness in smart city development (Albino et al., 2015). While these studies have contributed substantially to understanding technological and institutional aspects of smart cities, comparatively less attention has been devoted to how citizens themselves interpret and evaluate smart city expansion.

In particular, many smart city studies have primarily focused on technological systems, administrative arrangements, or policy implementation (Kitchin, 2014), while comparatively less attention has been given to understanding smart cities as socially interpreted urban spaces (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). Recent smart city governance research increasingly argues that citizens should be viewed not merely as passive users of urban technologies but as active co-creators of public value who participate in shaping urban development processes (Kummitha, 2025). However, citizens’ support for smart city policies may depend not only on technological expectations but also on broader perceptions regarding quality of life, urban governance, institutional trust, participation opportunities, social values, and regional development (Vanolo, 2014; Kummitha, 2025). From a geographic perspective, urban space is socially constructed through political, institutional, and everyday social relationships rather than being merely a physical or technological environment (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). Drawing on the concept of the production of space developed by Lefebvre (1974/Nicholson-Smith (Trans.), 1991) and Massey’s (2005) relational understanding of place, this study views smart city development as a socially interpreted urban process rather than merely a technological project. Lefebvre (1974/Nicholson-Smith (Trans.), 1991) argued that urban space is continuously produced and reproduced through interactions among social actors, institutions, and power relations, while Massey (2005) emphasized that places are constituted through ongoing social relationships and networks rather than existing as fixed geographical entities. Citizens’ support for smart city expansion therefore reflects not only evaluations of technological innovation but also broader interpretations of how urban space, governance arrangements, and everyday experiences are organized and transformed through smart city development. Accordingly, smart city acceptance may reflect broader spatial and relational interpretations regarding how cities should be organized, governed, and experienced in everyday urban life (Healey, 1997; Massey, 2005).

Previous research has generated valuable insights into technology acceptance, service satisfaction, innovation perceptions, and citizen evaluations of smart cities. However, comparatively less attention has been devoted to examining how these diverse perceptions may be integrated into broader multidimensional value structures associated with support for smart city expansion (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018). In reality, citizens may simultaneously evaluate smart cities through multiple dimensions, including technological innovation, economic revitalization, governance participation, social trust, public services, and institutional improvement (Nam and Pardo, 2011; Yigitcanlar et al., 2018; Mora et al., 2019). Recent studies have similarly emphasized that smart city governance increasingly depends on citizen engagement, participatory governance, collaborative institutional coordination, and the co-creation of public value through active citizen involvement (Das, 2024; Grossi and Welinder, 2024; Kummitha, 2025). Although prior studies have examined citizen preferences, acceptance, and evaluations of smart city services (e.g., Wirtz et al., 2022), most of these studies focus on specific services or acceptance factors. While existing studies have contributed important insights into citizen preferences, service evaluations, and governance processes, less attention has been devoted to examining how broader latent value dimensions collectively influence support for smart city policies.

In addition, existing smart city studies have generally focused on technology adoption, service evaluation, governance, or citizen perceptions, while relatively less attention has been devoted to examining the ordinal nature of policy support and the hierarchical relationships among demographic characteristics, awareness variables, and broader value perceptions (Albino et al., 2015; Yigitcanlar et al., 2018; Mora et al., 2019). Furthermore, previous studies have rarely examined these factors simultaneously within a single analytical framework. As a result, the relative and incremental contributions of demographic characteristics, awareness and perception variables, and broader multidimensional value structures to support for smart city expansion remain insufficiently understood (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018).

To address these gaps, this study examines factors influencing citizens’ support for smart city expansion by focusing on demographic characteristics, awareness and perception variables, and broader smart city value dimensions (Albino et al., 2015; Yigitcanlar et al., 2018). Using survey data collected from citizens in South Korea, this study employs exploratory factor analysis and hierarchical ordered logistic regression analyses to identify the multidimensional value structures underlying support for smart city expansion and to examine their incremental explanatory effects beyond demographic characteristics and awareness/perception variables.

More specifically, this study makes several contributions to the literature. First, it extends existing smart city research by conceptualizing smart cities not merely as technological infrastructures but as socially interpreted urban spaces associated with governance, institutional trust, quality of life, and regional development (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005; Nam and Pardo, 2011; Yigitcanlar et al., 2018). From this perspective, citizens’ support for smart city expansion may reflect not only evaluations of technology and governance but also spatial perceptions regarding urban identity, place experience, and everyday urban interactions. Second, this study identifies broader latent value dimensions of smart city perceptions through exploratory factor analysis, including Innovation & Economic Value, Quality of Life & Experience, Open Governance, and Social & Institutional Value, which reflect the multidimensional nature of smart city discourse discussed in prior studies (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018). Third, by employing hierarchical ordered logistic regression analyses, this study examines the extent to which broader smart city value dimensions provide additional explanatory power beyond demographic characteristics and awareness/perception variables in explaining support for smart city expansion. Finally, the study contributes to geographic and urban governance literature by highlighting the importance of spatial perceptions, participatory governance, and socially embedded urban experiences in shaping smart city acceptance (Harvey, 1989; Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Healey, 1997; Massey, 2005). This perspective is consistent with recent Korean geographical research emphasizing regional development, innovation environments, and spatially differentiated development processes (Son et al., 2022; Gu et al., 2024), demonstrating how citizens’ evaluations of smart city expansion are connected to broader geographical concerns regarding place, regional development, governance, and spatial relationships.

The findings of this study provide important theoretical, managerial, policy, and geographic implications for future smart city development. In particular, the study suggests that successful smart city policies require not only technological advancement but also practical everyday usability, citizen-centered governance, institutional legitimacy, participatory urban development, and socially meaningful urban experiences.

2. Literature Review

1) Smart cities as socio-technical urban development

Smart cities have emerged as a major urban development model as cities increasingly utilize ICT, big data, AI, IoT, and digital platforms to enhance urban services, sustainability, and competitiveness (Batty et al., 2012; Kitchin, 2014; Albino et al., 2015). Early smart city research often emphasized technological infrastructure and data-driven efficiency, conceptualizing smart cities as systems for optimizing transportation, public services, environmental management, and urban administration (Harrison et al., 2010; Batty et al., 2012). However, more recent approaches argue that smart cities should not be understood solely as technological systems, but rather as socio-technical urban arrangements involving people, institutions, governance processes, and everyday urban experiences (Nam and Pardo, 2011; Kitchin, 2014; Albino et al., 2015; Grossi and Welinder, 2024; Kummitha, 2025).

From this perspective, the success of smart city development depends not only on technological advancement but also on how citizens interpret and experience smart city policies (Healey, 1997; Nam and Pardo, 2011; Kitchin, 2014; Albino et al., 2015). Smart cities may improve quality of life, enhance public service delivery, support urban sustainability, and promote regional competitiveness, but these outcomes depend on citizens’ perceptions of usefulness, institutional trust, governance openness, institutional legitimacy, and broader social value (Caragliu et al., 2011; Nam and Pardo, 2011; Albino et al., 2015). Therefore, examining citizens’ support for smart city expansion is important because policy implementation ultimately requires public acceptance, perceived legitimacy, and social embeddedness (Suchman, 1995; Healey, 1997; Nam and Pardo, 2011).

2) Smart cities, urban space, and geographic perspectives

Geographic and urban theory provides an important framework for understanding smart cities beyond technological infrastructure (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Healey, 1997; Massey, 2005; Kitchin, 2014). Lefebvre (1974/Nicholson-Smith (Trans.), 1991 argued that space is not a neutral physical container but a socially produced outcome shaped through interactions among political power, institutional arrangements, economic forces, and everyday practices. From this perspective, smart cities should not be viewed merely as technological infrastructures installed within urban environments, but as socially produced spaces in which technological systems, governance arrangements, and citizens’ everyday experiences interact (Nam and Pardo, 2011; Kitchin, 2014). Similarly, Massey (2005) conceptualized space as relational and dynamic. Rather than treating places as fixed geographical entities, Massey (2005) argued that places are constituted through ongoing interactions among people, institutions, networks, and social processes occurring across multiple spatial scales. In her relational view of space, places are continuously shaped and reshaped through social relations and connections extending beyond local boundaries (Massey, 2005). This relational perspective suggests that citizens’ evaluations of smart city development are likely to reflect not only assessments of technological performance but also broader perceptions regarding governance, participation, institutional trust, and local development (Healey, 1997; Massey, 2005).

Applying these geographic perspectives to smart city development implies that support for smart city expansion cannot be understood solely through technological efficiency or infrastructure provision (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). Rather, citizens’ support may reflect how they interpret the social meaning of smart city initiatives, the governance structures through which they are implemented, and the ways in which they reshape everyday urban experiences and spatial relationships. Healey (1997) further argued that urban development outcomes are shaped through collaborative planning and stakeholder interaction rather than solely through top-down decision making. This perspective suggests that citizens’ support for smart city expansion may also depend on opportunities for participation, communication, and collaborative governance.

This geographic perspective is important because citizens may evaluate smart cities not only based on technological convenience but also based on how smart city policies reshape urban life, regional identity, spatial relationships, social relations, and governance structures (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Healey, 1997; Massey, 2005; Nam and Pardo, 2011). Smart city expansion may therefore reflect broader spatial perceptions regarding urban competitiveness, local development, public service accessibility, and community sustainability (Massey, 2005; Caragliu et al., 2011; Yigitcanlar et al., 2018). Recent geographical research in Korea similarly suggests that technological innovation, regional development, and spatial meanings are shaped by local industrial structures, innovation environments, and socially constructed relationships among actors and institutions (Son et al., 2022; Gu et al., 2024; Hong, 2026). In this sense, support for smart city expansion is closely related to how citizens imagine, interpret, and evaluate future urban spaces (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Healey, 1997; Massey, 2005). Accordingly, citizens’ support for smart city expansion may be interpreted not merely as a reaction to technological innovation, but as an evaluation of broader urban development trajectories, governance arrangements, and everyday spatial experiences.

3) Smart city governance, legitimacy, and citizens’ support

Smart city development also requires attention to urban governance (Healey, 1997; Nam and Pardo, 2011; Albino et al., 2015). Previous studies emphasize that smart cities involve dynamic interaction among technology, people, and institutions (Nam and Pardo, 2011). This means that smart city policies cannot be fully explained by technological capacity alone; they also require institutional coordination, citizen participation, and collaborative governance (Healey, 1997; Nam and Pardo, 2011; Albino et al., 2015; Das, 2024; Grossi and Welinder, 2024; Kummitha, 2025). Healey’s (1997) collaborative planning perspective suggests that urban development is increasingly shaped through communication, negotiation, and cooperation among diverse stakeholders. Castells (2010) also emphasizes the importance of networked social and institutional relations in shaping contemporary urban development and informational urbanism.

In smart city contexts, governance includes cooperation among local governments, central government, private firms, experts, civil society organizations, and residents (Healey, 1997; Castells, 2010; Nam and Pardo, 2011; Albino et al., 2015). Open governance and stakeholder participation may increase public trust and legitimacy by allowing citizens to perceive smart cities as shared urban projects rather than top-down technological interventions (Suchman, 1995; Healey, 1997; Nam and Pardo, 2011; Das, 2024; Kumar, 2024). Conversely, if smart city development is perceived as overly technology-driven or institutionally distant from citizens’ everyday needs, public support may weaken (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Suchman, 1995; Nam and Pardo, 2011; Kitchin, 2014). These discussions suggest that open governance constitutes an important dimension for understanding citizens’ support for smart city expansion (Suchman, 1995; Healey, 1997; Nam and Pardo, 2011).

4) Smart city value dimensions

Smart city support is likely to be shaped by multiple value dimensions (Albino et al., 2015; Nam and Pardo, 2011; Yigitcanlar et al., 2018). First, innovation and economic value are central to smart city development (Harrison et al., 2010; Caragliu et al., 2011; Yigitcanlar et al., 2018). Smart cities are often promoted as strategies for technological innovation, regional competitiveness, economic revitalization, and city branding (Harrison et al., 2010; Caragliu et al., 2011; Yigitcanlar et al., 2018). Harvey’s (1989) discussion of entrepreneurial urban governance also suggests that cities increasingly adopt development strategies linked to competitiveness, investment, and technological innovation. Citizens who perceive smart cities as contributing to economic growth and technological advancement are likely to express stronger support for smart city expansion (Harrison et al., 2010; Caragliu et al., 2011; Yigitcanlar et al., 2018).

Second, quality of life and experiential public services represent important dimensions of smart city acceptance (Caragliu et al., 2011; Albino et al., 2015; Yigitcanlar et al., 2018). Smart city initiatives are often justified by their potential to improve daily life through more efficient transportation, enhanced public spaces, better public services, and more responsive urban management (Nam and Pardo, 2011; Albino et al., 2015). Since citizens experience smart cities through everyday urban services, their support may depend on whether smart city policies are perceived as practically useful and relevant to daily life and everyday urban experiences (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Nam and Pardo, 2011; Kitchin, 2014).

Third, open governance and stakeholder participation constitute important dimensions of smart city support (Healey, 1997; Nam and Pardo, 2011; Albino et al., 2015). Smart city governance increasingly involves cooperation among governments, private firms, experts, civil society organizations, and citizens in urban planning and policy implementation (Castells, 2010; Nam and Pardo, 2011; Das, 2024; Kummitha, 2025). Open governance and participatory decision-making may strengthen policy legitimacy and increase citizens’ support for smart city expansion by encouraging perceptions of inclusion and shared urban development processes (Healey, 1997; Suchman, 1995; Grossi and Welinder, 2024). Conversely, when smart city initiatives are perceived as institutionally closed, technocratic, or distant from citizens’ everyday needs, public support may weaken (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Kitchin, 2014). Therefore, citizens who perceive smart city governance as open, participatory, and collaborative are likely to express stronger support for smart city expansion (Suchman, 1995; Healey, 1997; Nam and Pardo, 2011).

Fourth, social and institutional value are also important (Nam and Pardo, 2011; Albino et al., 2015). Smart cities may contribute to institutional reform, social trust, community development, and improved governance capacity (Caragliu et al., 2011; Albino et al., 2015; Yigitcanlar et al., 2018). However, critical smart city literature warns that smart city initiatives can become technocratic or disciplinary when they prioritize surveillance, efficiency, and control over democratic participation and social inclusion (Kitchin, 2014; Vanolo, 2014). Therefore, citizens’ support for smart city expansion may depend on whether smart cities are perceived as socially meaningful, institutionally trustworthy, and conducive to community development and socially embedded urban experiences (Suchman, 1995; Nam and Pardo, 2011; Kitchin, 2014; Vanolo, 2014).

5) Smart city expansion and citizen support in South Korea

In South Korea, smart city development has become a major national and local policy agenda associated with digital transformation, urban innovation, and future-oriented regional development (Yigitcanlar et al., 2018; Ministry of Land, Infrastructure and Transport [MOLIT], 2019). The South Korean government has actively promoted smart city policies through national smart city strategies, large-scale urban innovation projects, and smart city pilot programs in areas such as Sejong and Busan (MOLIT, 2019). Smart city initiatives have increasingly expanded beyond experimental technological projects toward broader urban governance systems integrating transportation, environmental management, digital public services, energy systems, and data-driven urban administration (Harrison et al., 2010; Batty et al., 2012; MOLIT, 2019; Grossi and Welinder, 2024).

South Korea’s early smart city initiatives began with the U-City phase, which focused on applying ICT-based infrastructure, networks, and sensor technologies to connect urban facilities and services (World Bank, 2022). However, gaps between policy expectations and the actual public services provided by early U-City projects contributed to limitations in citizen satisfaction and slowed the overall development of smart cities (World Bank, 2022). Despite these limitations, smart cities have remained a major national development priority under South Korea’s broader Fourth Industrial Revolution strategy, particularly through more recent phases emphasizing citizen-centered services, sustainability, digital governance, and the expansion of smart city policies to existing urban areas (MOLIT, 2019; World Bank, 2022; Das, 2024; Kummitha, 2025). Most recently, MOLIT (2025) announced additional smart city development initiatives, including the Smart City Solution Expansion Project, which aims to deliver proven smart city solutions to smaller cities and local communities. The project seeks to expand citizens’ access to smart city services by applying successful smart city technologies and public service systems across a wider range of urban areas (MOLIT, 2025).

As smart city policies expand into broader areas of urban governance and everyday urban life, citizens’ support becomes increasingly important for policy implementation, legitimacy, and long-term sustainability (Suchman, 1995; Healey, 1997; Nam and Pardo, 2011). Smart city expansion may influence not only technological infrastructure but also urban lifestyles, spatial experiences, governance relationships, and citizens’ perceptions of local development and urban space (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). From a geographic perspective, regional development initiatives are often shaped by place-based value creation and networked relationships among local actors, suggesting that citizens may evaluate smart city expansion not only in terms of technological advancement but also in relation to broader expectations regarding local development and regional competitiveness (Han, 2023). Consequently, the success of smart city expansion depends not only on technological efficiency but also on whether citizens perceive smart city policies as beneficial, trustworthy, participatory, and relevant to their everyday urban lives (Nam and Pardo, 2011; Kitchin, 2014; Albino et al., 2015; Grossi and Welinder, 2024; Kumar, 2024).

Existing international studies have examined citizens’ preferences, acceptance, and evaluations of smart city services. For example, Wirtz et al. (2022) analyzed citizen preferences for various smart city services and demonstrated that citizens evaluate smart city initiatives differently depending on perceived service benefits and policy priorities. These studies suggest that citizen perceptions constitute an important component of successful smart city development. Building on these studies, the present research further examines how broader smart city value dimensions are associated with citizens’ support for smart city expansion in the South Korean context.

Existing discussions of smart city development have often focused on technological infrastructure, administrative implementation, and service efficiency (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018), with comparatively less attention devoted to how broader value dimensions—including innovation, quality of life, open governance, social trust, and institutional legitimacy—shape citizens’ support for smart city expansion (Nam and Pardo, 2011; Kitchin, 2014; Vanolo, 2014; Albino et al., 2015; Das, 2024; Kummitha, 2025). Therefore, examining citizens’ support for smart city expansion in the South Korean context is important for understanding how smart city policies gain social acceptance and legitimacy within rapidly changing urban environments (Suchman, 1995; Healey, 1997).

6) Research gap

Prior smart city research has generated important insights into technological infrastructure, policy implementation, citizen preferences, service evaluations, governance processes, and urban efficiency (Harrison et al., 2010; Batty et al., 2012; Albino et al., 2015; Wirtz et al., 2022). However, opportunities remain for further research on how broader multidimensional value structures shape citizens’ support for smart city expansion. First, although existing studies have examined technological aspects of smart cities, citizen preferences, service evaluations, and governance processes (e.g., Wirtz et al., 2022; Grossi and Welinder, 2024; Kummitha, 2025), comparatively less attention has been devoted to understanding citizens’ support for smart city expansion from broader social, governance-related, and spatial perspectives (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005; Kitchin, 2014). Second, prior research has frequently examined individual-level factors such as technology acceptance, satisfaction, and awareness, while comparatively less attention has been devoted to exploring broader latent value dimensions underlying citizens’ support for smart city policies (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018). Third, comparatively less attention has been devoted to examining the relative and incremental contributions of demographic characteristics, awareness and perception variables, and broader smart city value dimensions in explaining citizens’ support for smart city expansion (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018).

This study addresses these gaps by conceptualizing support for smart city expansion as a multidimensional and value-driven form of urban policy acceptance (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018). Rather than assuming that citizen support for smart city expansion is determined by a single technological or attitudinal factor, this study examines smart city expansion from broader social, governance-related, and spatial perspectives and investigates whether broader latent value dimensions derived from citizens’ evaluations of smart city development provide additional explanatory power beyond demographic characteristics and individual perception variables. By combining exploratory factor analysis and hierarchical ordered logistic regression analyses, this study identifies key smart city value dimensions and examines their incremental explanatory effects on support for smart city expansion beyond demographic characteristics and awareness/perception variables.

In doing so, the study contributes to the smart city, urban governance, and geographic literature in three ways. First, it extends existing research by examining citizens’ support for smart city expansion from broader social, governance-related, and spatial perspectives. Second, it identifies key latent value dimensions underlying citizens’ evaluations of smart city development through exploratory factor analysis. Third, it demonstrates the relative and incremental contributions of demographic characteristics, awareness and perception variables, and broader smart city value dimensions in explaining support for smart city expansion. Taken together, this study argues that support for smart city expansion is likely to be shaped not only by technological expectations but also by broader perceptions of innovation and economic development, participatory governance, social and institutional value, and spatial interpretations regarding urban governance, regional development, and everyday urban experiences (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Healey, 1997; Nam and Pardo, 2011; Yigitcanlar et al., 2018; Grossi and Welinder, 2024; Kummitha, 2025).

3. Hypothesis Development

1) Demographic characteristics

Citizens’ support for smart city expansion may vary according to demographic characteristics because smart city policies are closely related to access to digital services, urban resources, and everyday urban infrastructure (Nam and Pardo, 2011; Yigitcanlar et al., 2018). Previous smart city studies emphasize that smart cities are not merely technological systems, but broader socio-technical urban arrangements involving technology, people, institutions, and governance processes (Nam and Pardo, 2011; Albino et al., 2015; Grossi and Welinder, 2024; Kummitha, 2025). From this perspective, demographic characteristics such as age, education, income, and marital status may influence how citizens understand and evaluate smart city policies (Nam and Pardo, 2011; Yigitcanlar et al., 2018). Younger and more educated citizens may be more receptive to digital technologies and urban innovation, while citizens with different socioeconomic backgrounds may evaluate smart city expansion differently depending on expected benefits, accessibility, and perceived relevance to everyday urban experiences (Caragliu et al., 2011; Nam and Pardo, 2011). Therefore, demographic characteristics are expected to be associated with variations in citizens’ support for smart city expansion.

H1: Demographic characteristics are associated with citizens’ support for smart city expansion.

2) Awareness & perception

Awareness and perception are central to citizens’ support for smart city expansion because smart cities are often evaluated through citizens’ understanding of their meaning, usefulness, satisfaction, and expected benefits (Nam and Pardo, 2011; Albino et al., 2015). Existing studies suggest that smart cities rely not only on ICT infrastructure but also on people-centered innovation, institutional coordination, and perceived improvements in urban life (Nam and Pardo, 2011; Albino et al., 2015; Grossi and Welinder, 2024). If citizens perceive smart cities as useful, meaningful, satisfying, and beneficial for public services and urban problem-solving, they are more likely to support smart city expansion (Nam and Pardo, 2011; Albino et al., 2015). Conversely, limited awareness or unclear understanding of smart city policies may weaken policy support, even when smart city infrastructure is technologically advanced (Nam and Pardo, 2011; Kitchin, 2014). Prior work suggests that citizens may interpret smart city initiatives differently depending on their perceptions of technology, governance arrangements, and participation processes (Kitchin, 2014; Vanolo, 2014; Das, 2024; Kummitha, 2025). From a geographic perspective, these interpretations are also shaped by citizens’ everyday urban experiences and the ways in which urban spaces are socially produced and relationally experienced (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). Thus, awareness and perception variables are expected to be associated with citizens’ support for smart city expansion.

H2: Awareness and perception variables are associated with support for smart city expansion.

H2a: Having heard of smart cities is associated with support for smart city expansion.

H2b: Smart city awareness is associated with support for smart city expansion.

H2c: Satisfaction with smart cities is associated with support for smart city expansion.

H2d: Positive meaning perceptions of smart cities are positively associated with support for smart city expansion.

H2e: Positive utilization perceptions of smart cities are positively associated with support for smart city expansion.

3) Value dimensions

Smart city support is likely to be shaped by broader value dimensions rather than by technological expectations alone (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018). Smart city research has emphasized that smart cities involve multiple dimensions, including technology, people, institutions, sustainability, governance, and urban competitiveness (Caragliu et al., 2011; Nam and Pardo, 2011; Albino et al., 2015; Grossi and Welinder, 2024; Kummitha, 2025). From a geographic perspective, urban space is socially produced and relationally experienced, meaning that citizens’ support for smart city expansion may depend on how they interpret smart cities as urban spaces associated with innovation, quality of life, participation, institutional trust, and social relations (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). Therefore, citizens who perceive smart cities as generating broader public, economic, governance, and social value are expected to show stronger support for smart city expansion. These value dimensions are expected to capture broader latent structures underlying citizens’ evaluations of smart city development beyond individual perception variables.

H3: Smart city value dimensions are positively associated with support for smart city expansion.

Innovation and economic value constitute one of the most prominent dimensions of smart city development (Harrison et al., 2010; Caragliu et al., 2011; Yigitcanlar et al., 2018). Smart cities are frequently promoted as strategies for technological innovation, economic revitalization, regional competitiveness, and urban branding (Harrison et al., 2010; Caragliu et al., 2011; Albino et al., 2015). Urban governance literature further suggests that contemporary cities increasingly compete through innovation-oriented and entrepreneurial development strategies (Harvey, 1989). Therefore, when citizens perceive smart cities as contributing to technological advancement, local economic growth, and urban competitiveness, they are likely to show stronger support for smart city expansion.

H3a: Perceptions emphasizing Innovation & Economic Value, including technological innovation, economic revitalization, and urban competitiveness, are positively associated with citizens’ support for smart city expansion.

Quality of life and experiential public services are also central dimensions of smart city acceptance (Nam and Pardo, 2011; Albino et al., 2015; Yigitcanlar et al., 2018). Smart city initiatives are often justified by their potential to improve public services, urban efficiency, sustainability, and citizens’ everyday quality of life (Caragliu et al., 2011; Nam and Pardo, 2011; Albino et al., 2015). From a spatial perspective, citizens experience smart cities through everyday urban practices, including mobility, public services, safety, and access to urban resources (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). Accordingly, perceptions regarding quality-of-life improvement and everyday urban experiences may contribute to citizens’ support for smart city expansion, although their influence may differ from other governance- or innovation-oriented value dimensions.

H3b: Perceptions emphasizing Quality of Life & Experience, including quality-of-life improvement and experiential public services, are positively associated with citizens’ support for smart city expansion.

Open governance and stakeholder participation constitute important dimensions of smart city development becaus e smart cities require institutional coordination among citizens, government, private firms, experts, and civil society actors (Healey, 1997; Nam and Pardo, 2011). Smart city research emphasizes that smart cities involve not only technological infrastructure but also people-centered governance and institutional arrangements (Nam and Pardo, 2011; Albino et al., 2015; Das, 2024; Grossi and Welinder, 2024). Research on collaborative planning and urban governance literature further highlights the importance of stakeholder participation, communication, and networked governance in shaping urban development processes (Healey, 1997; Castells, 2010; Kummitha, 2025). Therefore, citizens who perceive smart cities as open, participatory, and communicative governance spaces are expected to show stronger support for smart city expansion.

H3c: Perceptions emphasizing Open Governance, including stakeholder participation, communication, and collaborative governance, are positively associated with citizens’ support for smart city expansion.

Finally, social and institutional value may influence citizens’ support for smart city expansion because smart cities are embedded within broader social relations, institutional arrangements, and community development processes (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Healey, 1997; Nam and Pardo, 2011). Smart cities have been discussed as urban systems that depend not only on technology but also on institutional capacity, governance structures, and social trust (Nam and Pardo, 2011; Kitchin, 2014; Vanolo, 2014; Grossi and Welinder, 2024). Geographic perspectives similarly suggest that urban space is shaped by institutional practices, social relations, and everyday meanings (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). Accordingly, citizens who perceive smart cities as contributing to institutional improvement, social trust, and sustainable community development are expected to be more supportive of smart city expansion.

H3d: Perceptions emphasizing Social & Institutional Value, including institutional improvement, social trust, and sustainable community development, are positively associated with citizens’ support for smart city expansion.

4. Methodology

1) Data collection and survey design

Data for this study were collected through an online survey administered by a professional research agency in South Korea between September and October 2023. Respondents were recruited from the firm's nationwide online panel using quota sampling based on gender, age group, and region. To improve sample representativeness, quotas were established across demographic groups and major metropolitan areas. Eligibility criteria required respondents to be adults residing in South Korea. The survey was designed to capture public perceptions of smart city expansion among the general population rather than residents of a specific smart city project area. A total of 4,393 panel members received survey invitations. Among them, 673 individuals initiated the survey, and 385 completed responses were obtained after applying quota controls and data screening procedures. The final sample size was considered sufficient for exploratory factor analysis and regression analysis, exceeding commonly recommended minimum sample size requirements in multivariate statistical analyses (Hair et al., 2019).

The survey was conducted in accordance with ethical research guidelines, including voluntary participation, anonymous response procedures, and informed consent. Prior to participation, respondents were informed about the purpose of the study, the voluntary nature of participation, confidentiality protections, and their right to withdraw at any time. The study protocol was reviewed and approved by the Institutional Review Board (IRB).

The survey instrument was developed based on prior smart city literature and included items measuring perceptions of technological innovation, quality of life, governance, institutional change, public services, stakeholder participation, environmental sustainability, and social value associated with smart city development (Albino et al., 2015; Nam and Pardo, 2011; Yigitcanlar et al., 2018). Rather than adopting a single existing scale, the instrument integrated items drawn from prior smart city, governance, and urban development studies and was subsequently subjected to exploratory factor analysis to identify broader latent value dimensions underlying citizens’ support for smart city expansion. Most major variables were measured using five-point Likert-type scales ranging from low to high levels of agreement or support.

2) Sample characteristics

The demographic characteristics of the respondents are presented in Table 1. Regarding gender, 49.1% of respondents were male and 50.9% were female, indicating a relatively balanced gender distribution. In terms of age, 22.3% of respondents were in their 20s, 26.0% were in their 30s, 19.5% were in their 40s, and 32.2% were aged 50 or older. Regarding education, 22.9% of respondents had a high school education or lower, 67.3% had a college-level education, and 9.9% had graduate-level education. Regarding income, 45.5% belonged to the low-income group, 33.5% to the middle-income group, and 21.0% to the high-income group. In terms of marital status, 60.0% of respondents were married, while 40.0% were unmarried. Regarding occupation, the largest proportion of respondents worked in private companies (36.4%), followed by homemakers (14.0%), self-employed individuals (9.1%), respondents in other occupations (8.3%), and students (8.1%). Public-sector-related occupations included education professionals (6.2%), employees of public enterprises (6.0%), non-profit organization workers (5.5%), central government officials (2.9%), local government officials (1.6%), and research institute employees (2.1%). Overall, the sample reflected diverse demographic, socioeconomic, and occupational backgrounds across private-sector, public-sector, non-profit, and non-working groups, allowing the study to examine variations in support for smart city expansion across different population groups.

In addition to demographic characteristics, several awareness and perception variables were measured using single-item indicators. Awareness of smart cities was measured by asking respondents whether they had heard about smart cities. Smart city awareness was measured by asking respondents how much they knew about smart cities. Satisfaction was measured by respondents’ overall satisfaction with smart cities. Meaning perception was measured using the statement, “I think I know the meaning of smart cities.” Utilization perception was measured using the statement, “I think the utilization of smart cities is important in our society.” Except for the binary variable “Heard of Smart City,” all variables were measured using five-point scales, with higher values indicating higher levels of the corresponding construct. Table 2 summarizes the measurement of awareness and perception variables.

3) Exploratory factor analysis (EFA)

To identify the latent value dimensions associated with smart city perceptions, an exploratory factor analysis (EFA) was conducted using principal component analysis with varimax rotation. Variables related to perceptions of smart city innovation, quality of life, governance, institutional change, social value, public services, environmental sustainability, and stakeholder participation were included in the analysis. Factors with eigenvalues greater than 1.0 were retained following the Kaiser criterion. The analysis identified four distinct value dimensions, collectively explaining 57.75% of the total variance. Factor loadings ranged from .456 to .729. The extracted factor scores were subsequently used as continuous independent variables in the hierarchical ordered logistic regression analyses.

4) Hierarchical ordered logistic regression analysis

To examine factors influencing support for smart city expansion, hierarchical ordered logistic regression analyses were conducted. Since the dependent variable was measured using a five-point ordinal scale ranging from low to high levels of support for smart city expansion, ordered logistic regression was employed to appropriately reflect the ordinal nature of the outcome variable. Variables were entered sequentially across four models to assess the incremental explanatory power of demographic characteristics, awareness and perception variables, and smart city value dimensions. Model 1 included demographic variables, including gender, age, education, income, and marital status. Model 2 additionally incorporated awareness and perception variables, including whether respondents had heard of smart cities, smart city awareness, satisfaction, meaning perception, and utilization perception. Model 3 further added the value dimensions of Innovation & Economic Value and Quality of Life & Experience derived from the exploratory factor analysis while retaining all variables included in the previous models. Finally, Model 4 additionally incorporated Open Governance and Social & Institutional Value dimensions while retaining all variables from Models 1 through 3. The four value dimensions were introduced in stages to assess whether governance- and institution-related dimensions provided additional explanatory power beyond innovation- and quality-of-life-related dimensions. Accordingly, the hierarchical modeling strategy followed a cumulative specification in which variables entered at earlier stages were retained in subsequent models. This approach enabled the study to examine the incremental contribution of broader smart city value dimensions beyond demographic characteristics and awareness/perception variables.

The hierarchical modeling strategy enabled the study to examine how the explanatory power of the models changed as higher-order perception and value dimensions were progressively introduced. Model fit was evaluated using the model chi-square statistic, -2 log likelihood, and Nagelkerke pseudo-R² values. Regression coefficients (B) and odds ratios (Exp(B)) were reported for interpretation of the ordered logistic regression results.

5) Reliability and multicollinearity diagnostics

Reliability analyses were also conducted. Cronbach’s alpha values were 0.91 for the Innovation & Economic Value factor, 0.88 for the Quality of Life & Experience factor, 0.83 for the Open Governance factor, and 0.82 for the Social & Institutional Value factor, indicating acceptable to high internal consistency across the extracted dimensions.

Multicollinearity diagnostics were also assessed using tolerance and variance inflation factor (VIF) statistics. The results indicated no evidence of multicollinearity among the factor-score variables, as all tolerance values and VIF values were approximately 1.00. This result is consistent with the use of varimax rotation in the exploratory factor analysis, which produces relatively independent factor dimensions.

5. Data Analysis

1) Results of Exploratory factor analysis (EFA)

An exploratory factor analysis (EFA) was conducted to identify the underlying value dimensions associated with smart city perceptions. Prior to factor extraction, the suitability of the data for factor analysis was assessed using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett's test of sphericity. The results indicated a KMO value of .958, suggesting excellent sampling adequacy. In addition, Bartlett's test of sphericity was statistically significant (χ² = 6841.644, df = 496, p < .01), indicating that the correlation matrix was appropriate for factor analysis. These results confirm that the data were suitable for factor extraction.

The first factor, labeled “Innovation & Economic Value,” reflected perceptions emphasizing technological innovation, economic revitalization, city branding, and corporate participation in smart city development. Representative items included perceptions that smart cities contribute to local economic revitalization, technological advancement, and urban brand enhancement. Factor loadings for this dimension ranged from .456 to .688, explaining 19.74% of the variance.

The second factor, “Quality of Life & Experience,” captured perceptions related to quality-of-life improvement and experiential public services. Representative items included beliefs that smart cities improve citizens’ quality of life and provide convenient and experiential public services. Factor loadings ranged from .593 to .703, accounting for 16.17% of the variance.

The third factor, “Open Governance,” represented perceptions emphasizing stakeholder participation, communication, and collaborative governance within smart city development. Representative items included communication among diverse stakeholders and citizen participation in smart city decision-making processes. Factor loadings ranged from .663 to .729, explaining 11.30% of the variance.

The fourth factor, “Social & Institutional Value,” reflected perceptions associated with social trust, institutional reform, and sustainable community development. Representative items included institutional improvement, social trust formation, and sustainable community revitalization through smart city initiatives. Factor loadings ranged from .614 to .719, accounting for 10.53% of the variance.

Overall, the factor structure demonstrated conceptually coherent dimensions of smart city perceptions. The extracted factor scores were subsequently used as continuous variables in the hierarchical ordered logistic regression analyses to examine how broader smart city value dimensions influence support for smart city expansion. Table 3 summarizes the exploratory factor analysis results.

2) Results of hierarchical ordered logistic regression analysis

Table 4 presents the results of the hierarchical ordered logistic regression analyses examining factors influencing support for smart city expansion. Across all models, the explanatory power of the models increased substantially as additional perception and value-related variables were introduced, indicating that support for smart city expansion is shaped more strongly by value perceptions than by demographic characteristics alone. The progressive improvement in model fit across the four models further suggests that broader smart city value dimensions provide substantially greater explanatory power than demographic characteristics and awareness-related variables.

Model 1 included only demographic variables. Among these variables, respondents in their twenties were significantly more likely than those aged 50 and older to support smart city expansion (OR = 2.136, p < .05). In addition, respondents with a high school education or lower were significantly less likely than respondents with graduate-level education to support smart city expansion (OR = .396, p < .05). Respondents with a college education also showed lower levels of support than respondents with graduate-level education (OR = .519, p < .10). However, the overall explanatory power of Model 1 was relatively low (Nagelkerke R² = .042). These findings suggest that demographic characteristics alone provide only limited insight into citizens’ support for smart city expansion.

Model 2 additionally introduced awareness and perception variables related to smart cities. The explanatory power increased substantially (Nagelkerke R² = .282), indicating that awareness and perception variables contributed significantly to explaining support for smart city expansion. Among these variables, Utilization Perception exerted the strongest positive effect (OR = 3.310, p < .01), suggesting that respondents who perceived smart cities as practically useful and beneficial in everyday life were substantially more likely to support expansion. Meaning Perception also showed a positive effect (OR = 1.458, p < .05), while higher levels of smart city awareness were negatively associated with support (OR = .664, p < .05). Satisfaction and whether respondents had heard of smart cities were not statistically significant. This pattern indicates that citizens’ evaluations of the practical usefulness of smart cities play a more important role in shaping policy support than simple awareness or familiarity with smart city initiatives.

Model 3 introduced the smart city value dimensions derived from the exploratory factor analysis, specifically Innovation & Economic Value and Quality of Life & Experience. Innovation & Economic Value emerged as the strongest predictor in the model (OR = 2.497, p < .01), indicating that respondents who perceived smart cities as contributing to technological innovation, economic revitalization, and urban competitiveness were significantly more likely to support smart city expansion initiatives. This finding suggests that perceptions of innovation and economic competitiveness are more strongly associated with support for smart city expansion than perceptions related to quality of life and everyday urban services. In contrast, Quality of Life & Experience did not exhibit a statistically significant effect. The explanatory power increased further (Nagelkerke R² = .388). The notable increase in explanatory power from Model 2 to Model 3 further indicates that broader value perceptions explain citizens’ support more effectively than awareness and perception variables alone. Among the awareness and perception variables, Utilization Perception remained the strongest and statistically significant awareness-related predictor in model 3 (OR = 3.310, p < .01). Satisfaction exhibited a marginally significant positive effect (OR = 1.339, p < .10), whereas Meaning Perception was not statistically significant.

Finally, Model 4 additionally incorporated Open Governance and Social & Institutional Value dimensions. The inclusion of these variables substantially improved the model fit and explanatory power (Nagelkerke R² = .497). Innovation & Economic Value remained the strongest predictor (OR = 3.034, p < .01), while Social & Institutional Value (OR = 2.314, p < .01), Utilization Perception (OR = 2.071, p < .01), and Open Governance (OR = 1.775, p < .01) also exhibited significant positive associations with support for smart city expansion. Quality of Life & Experience exhibited only a marginally significant positive effect (OR = 1.270, p < .10). These findings suggest that Innovation & Economic Value plays a more influential role than Quality of Life & Experience in shaping support for smart city expansion. The significant effects of Open Governance and Social & Institutional Value are consistent with the interpretation that support for smart city expansion is associated not only with technological advancement but also with perceptions of participatory governance, institutional legitimacy, and social trust. In addition, higher levels of Smart City Awareness remained negatively associated with support for smart city expansion (OR = .651, p < .05), suggesting that respondents who reported greater familiarity with smart cities may also be more aware of potential limitations or challenges associated with smart city development. This finding may indicate that greater awareness is accompanied not only by increased knowledge of potential benefits but also by greater recognition of practical limitations and implementation challenges associated with smart city initiatives.

Taken together, these findings indicate that support for smart city expansion is shaped not only by expectations regarding technological innovation and quality of life improvements, but also by perceptions related to participatory governance, institutional improvement, social trust, and sustainable community development. In the final model, Satisfaction and Meaning Perception were no longer statistically significant after the value dimensions were introduced, suggesting that their explanatory effects may be subsumed by broader latent value perceptions. This result implies that broader latent value dimensions capture much of the explanatory information contained in these more specific perception variables. Respondents in their twenties continued to show significantly higher levels of support for smart city expansion than respondents aged 50 and older (OR = 2.563, p < .05).

Overall, the results indicate that demographic characteristics alone provide limited explanatory power for understanding support for smart city expansion. Instead, broader value perceptions—particularly Innovation & Economic Value, Open Governance, and Social & Institutional Value—played substantially more important roles in explaining support for smart city expansion. Among these factors, Innovation & Economic Value emerged as the most influential predictor of support for smart city expansion, highlighting the importance of citizens’ expectations regarding technological advancement, economic development, and urban competitiveness. Open Governance and Social & Institutional Value also exhibited strong positive effects, highlighting the importance of governance arrangements, institutional legitimacy, and social trust in shaping citizen support for smart city development. Overall, the hierarchical modeling results support the study’s central argument that citizens’ support for smart city expansion is fundamentally value-driven. Innovation & Economic Value, Open Governance, and Social & Institutional Value emerged as the principal value dimensions associated with support for smart city expansion, highlighting the importance of multidimensional value perceptions in future smart city policy design.

3) Robustness check: Hierarchical OLS regression model

Additional hierarchical ordinary least squares (OLS) regression analyses were conducted as robustness checks. Unlike the primary ordered logistic regression models, the hierarchical OLS models cumulatively retained variables across model stages in order to examine the robustness of the findings under an alternative model specification. The results were largely consistent with the ordered logistic regression analyses. Demographic variables alone showed limited explanatory power, whereas awareness/perception variables and broader smart city value dimensions substantially increased explanatory power across the models.

In the final OLS model, Innovation & Economic Value, Open Governance, and Social & Institutional Value remained statistically significant positive predictors of support for smart city expansion. Utilization Perception also consistently showed a significant positive effect, whereas Smart City Awareness remained negatively associated with support. Overall, the robustness check supports the stability and consistency of the study’s main findings across alternative model specifications. The direction, statistical significance, and relative importance of the key predictors remained largely consistent across both the hierarchical ordered logistic regression and hierarchical OLS models.

6. Conclusion

1) Findings

This study examined factors influencing citizens’ support for smart city expansion by focusing on demographic characteristics, awareness and perception variables, and broader smart city value dimensions. Using exploratory factor analysis and hierarchical ordered logistic regression analyses, the study identified how different layers of perception contribute to support for smart city policies. Additional robustness analyses, including hierarchical OLS regression models, were also conducted to examine the stability of the findings across alternative model specifications.

The findings demonstrate that demographic characteristics alone provide relatively limited explanatory power for support for smart city expansion. Although younger respondents showed consistently higher levels of support than older respondents, the explanatory power of the demographic model remained low. The progressive increase in explanatory power across the hierarchical models further indicates that citizens’ support for smart city expansion is explained more strongly by perceived value dimensions than by demographic characteristics alone. The robustness analyses were consistent with this pattern, with demographic variables explaining only a limited proportion of the variation in support for smart city expansion.

The study further found that awareness and perception variables significantly increased explanatory power. In particular, Utilization Perception emerged as one of the strongest predictors of support for smart city expansion. Respondents who perceived smart cities as practically useful and beneficial in everyday life were substantially more likely to support their expansion. Meaning Perception initially exhibited a positive association with support for smart city expansion. However, its effect diminished after broader smart city value dimensions were introduced, suggesting that its explanatory contribution may be subsumed within more comprehensive evaluations of smart city value. Interestingly, higher levels of Smart City Awareness were negatively associated with support for smart city expansion, implying that greater familiarity with smart city policies does not necessarily translate into stronger policy support but may instead be accompanied by a more critical assessment of their potential limitations and implementation challenges. Importantly, this negative association remained consistent across alternative model specifications, providing additional support for the stability of the finding.

Most importantly, the results showed that broader smart city value dimensions provided the greatest additional explanatory power. This finding supports the study’s central argument that citizens’ support for smart city expansion is structured around broader latent value dimensions rather than isolated perceptions of specific smart city attributes. Among these dimensions, Innovation & Economic Value emerged as the most influential factor, suggesting that citizens are more likely to support smart city expansion when they perceive smart cities as contributing to technological innovation, economic revitalization, urban competitiveness, and city branding. The result further suggests that perceptions of innovation and economic competitiveness are more strongly associated with support for smart city expansion than perceptions related to quality of life and everyday urban services. In addition, Open Governance and Social & Institutional Value showed statistically significant positive effects, while Quality of Life & Experience exhibited only a marginally significant positive association with support for smart city expansion. These results indicate that support for smart city expansion is associated not only with perceptions of technological advancement but also with perceptions of participatory governance, institutional legitimacy, social trust, and sustainable community development. The positive effects of Open Governance and Social & Institutional Value particularly highlight the importance of governance quality and institutional trust in shaping citizens’ support for smart city policies.

The robustness analyses also showed that Innovation & Economic Value, Open Governance, and Social & Institutional Value remained statistically significant positive predictors in the alternative OLS specifications. Furthermore, the effects of satisfaction and meaning perception diminished after broader smart city value dimensions were incorporated into the hierarchical models, suggesting that the explanatory contributions of these specific perception variables may be captured by the broader value dimensions. Overall, the findings indicate that support for smart city expansion is multidimensional, reflecting not only technological considerations but also broader governance, institutional, social, and experiential evaluations of urban development.

2) Managerial and policy implications

From a managerial and policy perspective, the findings suggest that local governments and policymakers should move beyond narrowly technology-centered smart city approaches. Citizens are more likely to support smart city expansion when smart cities are perceived as generating broader social, institutional, and experiential benefits. Accordingly, policymakers should strengthen citizen-oriented public services, participatory governance mechanisms, and transparent communication processes in smart city planning and implementation. Policies emphasizing only technological infrastructure without addressing governance, institutional trust, and quality-of-life concerns may face limitations in gaining long-term public support (Nam and Pardo, 2011; Kitchin, 2014; Vanolo, 2014). These results indicate that governance quality, institutional legitimacy, and citizen participation are integral components of long-term public support for smart city expansion. The findings further suggest that smart city policies should be framed not merely as technological modernization projects, but as broader urban development strategies connected to governance quality, social trust, and citizens’ everyday urban experiences (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Healey, 1997; Massey, 2005).

The findings also suggest that smart city policies should place greater emphasis on experiential and everyday usability dimensions. Since perceived utilization exerted a particularly strong influence on support, practical smart city services directly experienced by citizens—such as transportation convenience, digital public services, environmental management, and accessible smart infrastructure—may play a crucial role in enhancing policy acceptance. In particular, policymakers may improve public support by prioritizing visible and practically useful smart city services that citizens can directly experience in their daily lives rather than emphasizing abstract technological innovation alone. These findings suggest that support for smart city policies is closely associated with citizens’ perceptions of tangible benefits experienced in everyday urban life, in addition to technological sophistication.

In addition, the consistently strong effects of Innovation & Economic Value across the primary and robustness analyses suggest that citizens are more likely to support smart city expansion when smart cities are linked to economic revitalization, technological competitiveness, and urban development opportunities. At the same time, the significant effects of Open Governance and Social & Institutional Value indicate that long-term policy support also depends on participatory governance, institutional transparency, communication among stakeholders, and social trust. These findings highlight that technological innovation alone is unlikely to sustain long-term citizen support unless it is accompanied by effective governance and trusted public institutions. Notably, Open Governance and Social & Institutional Value remained significant even after awareness, satisfaction, meaning perception, and utilization variables were controlled, highlighting the independent importance of governance and institutional legitimacy in shaping citizen support. By contrast, Quality of Life & Experience exhibited only a marginally significant positive effect in the final model. This finding suggests that, after accounting for multiple value dimensions simultaneously, innovation- and governance-related perceptions were more strongly associated with support for smart city expansion than quality-of-life perceptions. Accordingly, successful smart city policies are likely to benefit from balancing technological innovation and economic competitiveness with inclusive governance and citizen-centered urban development strategies (Caragliu et al., 2011; Nam and Pardo, 2011; Albino et al., 2015; Grossi and Welinder, 2024; Kummitha, 2025).

3) Geographic implication

The findings also have important geographic implications. The findings indicate that smart city support is closely associated with how citizens interpret urban space, governance structures, and regional development processes. Smart cities may be understood not merely as technological infrastructures but also as socially constructed urban spaces shaped by perceptions of innovation, institutional trust, participation, and community sustainability. Korean smart city research similarly suggests that smart city development increasingly depends on the integration of technological infrastructures, governance arrangements, collaborative processes, and urban platforms designed to enhance citizens’ quality of life (Jang and Kim, 2020). This interpretation is consistent with recent geographical discussions emphasizing that spatial meanings and socio-cultural characteristics emerge through ongoing interactions among actors and institutions within specific places (Hong, 2026). From a geographic perspective, urban space is not simply a physical setting but a socially produced and relational space shaped through political, institutional, and social interactions (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Massey, 2005). In this sense, citizens’ support for smart city expansion reflects broader spatial interpretations of how urban environments are organized, governed, and experienced. Consistent with the notion of the production of space developed by Lefebvre (1974/Nicholson-Smith (Trans.), 1991), smart city development may be understood as a process through which technological systems become embedded within broader social, institutional, and political relationships. Likewise, Massey’s (2005) relational understanding of space suggests that citizens’ evaluations of smart cities are shaped not only by technological outcomes but also by how smart city initiatives influence governance relationships, social interactions, and regional development processes.

The findings also suggest that smart cities function as spaces of urban governance in which technological systems, institutional arrangements, and citizen participation become intertwined. Previous geographic and urban governance studies have emphasized that contemporary urban development increasingly depends on collaborative governance, stakeholder participation, and networked institutional coordination rather than solely top-down planning approaches. In particular, Healey (1997) argued that urban development outcomes are shaped through collaborative planning processes involving multiple stakeholders and institutional actors (Castells, 2010; Das, 2024; Grossi and Welinder, 2024; Kummitha, 2025). The significant effects of Open Governance and Social & Institutional Value are consistent with these perspectives, suggesting that support for smart city development is associated not only with perceptions of technological outcomes but also with perceptions of governance quality, institutional trust, and collaborative relationships.

This interpretation is also consistent with recent Korean geographical research emphasizing the importance of regional competitiveness, innovation environments, and place-based development processes in shaping future urban trajectories (Gu et al., 2024; Son et al., 2022). The strong effects of Innovation & Economic Value, Open Governance, and Social & Institutional Value further suggest that support for smart city expansion is associated not only with perceptions of technological projects but also with perceptions of regional competitiveness, institutional quality, participatory governance, social trust, and economic revitalization. These findings are consistent with geographic discussions emphasizing the increasing role of knowledge-based urban development and entrepreneurial urban governance in shaping contemporary cities (Harvey, 1989).

Accordingly, smart city development should be understood not only through technological efficiency or infrastructure provision but also through the spatial relationships, governance networks, and socially embedded urban experiences that shape citizens’ perceptions of cities and communities (Lefebvre, 1974/Nicholson-Smith (Trans.), 1991; Healey, 1997; Massey, 2005).

Overall, this study contributes to the literature by demonstrating that support for smart city expansion is multidimensional and value-driven rather than purely technology-oriented. The findings suggest that successful smart city development is likely to depend not only on technological advancement but also on citizen-centered governance, institutional legitimacy, practical everyday usability, and socially meaningful urban experiences. This interpretation is consistent with recent discussions emphasizing participatory and human-centered approaches to smart city development (Healey, 1997; Grossi and Welinder, 2024; Kummitha, 2025).

4) Limitations and future research

This study has several limitations that should be acknowledged. First, the study relied on cross-sectional survey data collected at a single point in time. Accordingly, the findings primarily identify statistical associations rather than definitive causal relationships between smart city perceptions and support for smart city expansion. Future research may benefit from longitudinal designs examining how citizens’ perceptions and policy support change over time as smart city projects develop and become more institutionalized within urban environments.

Second, the study focused primarily on citizens’ subjective perceptions and evaluative interpretations of smart city development. Although perceptions are important for understanding policy support and legitimacy, future studies could incorporate additional objective indicators related to smart city infrastructure, urban services, environmental performance, digital accessibility, and regional economic conditions in order to examine how material urban conditions interact with citizens’ perceptions. Future studies may also benefit from incorporating objective and governance-related indicators associated with citizen participation, collaborative governance, digital inclusion, and institutional transparency (Das, 2024; Grossi and Welinder, 2024; Kummitha, 2025).

In addition, both the independent variables (smart city value perceptions) and the dependent variable (support for smart city expansion) were measured using the same self-reported survey instrument and respondents. Therefore, the possibility of common method bias cannot be completely ruled out. Although the study employed multiple latent value dimensions and alternative model specifications to assess the consistency of the findings, future research may benefit from using longitudinal designs, multi-source data, behavioral measures, or objective indicators to further reduce potential common method bias and strengthen causal inference.

Third, the study was conducted within the South Korean context. Since smart city development is influenced by different political systems, governance structures, technological environments, and cultural contexts, the findings may not be fully generalizable to other countries or regions. Comparative cross-national studies may provide additional insights into how institutional and cultural differences shape citizens’ support for smart city expansion. Future research may also examine whether the relative importance of innovation, governance, and institutional value dimensions differs across national and regional contexts.

Fourth, although the study identified several important smart city value dimensions through exploratory factor analysis, the factor structure may vary depending on regional characteristics, survey design, and policy contexts. Future studies may further validate and refine the identified value dimensions using confirmatory factor analysis or structural equation modeling approaches. In particular, future research could examine more complex relationships among governance perceptions, institutional trust, technological expectations, and policy support. Future studies may also explore whether governance- and participation-related dimensions operate differently across diverse urban and regional contexts.

Finally, while this study emphasized governance, institutional, and experiential dimensions of smart city support, additional research may further explore whether and how specific social groups interpret smart city development differently. Differences related to age, digital literacy, socioeconomic inequality, regional disparities, and access to smart technologies may influence citizens’ perceptions and policy acceptance in distinct ways. Future research may therefore contribute to a more nuanced understanding of inclusive and citizen-centered smart city development and geographically differentiated urban experiences.