Innovative Transitions: Exploring Demand for Smart City Development in Novi Sad as a European Capital of Culture

Abstract

This study investigates the factors influencing the acceptance and implementation of smart city solutions, with a particular focus on smart mobility and digital services in Novi Sad, one of the leading urban centers in Serbia. Employing a quantitative methodology, the research encompasses citizens’ perceptions of the benefits of smart technologies, their level of awareness regarding smart solutions, the degree of engagement in using digital services, and their interest in smart mobility. The results indicate that these factors are crucial for the successful integration of smart technologies. Notably, awareness of smart city initiatives and the perceived benefits, such as improved mobility, reduced traffic congestion, increased energy efficiency, and enhanced quality of life, are highlighted as key prerequisites for the adoption of these solutions. Novi Sad, as the European Capital of Culture in 2022, presents a unique opportunity for the implementation of these technologies. Our findings point to the need for strategic campaigns aimed at educating and raising public awareness. The practical implications of this study could contribute to shaping policies that encourage the development of smart cities, not only in Novi Sad but also in other urban areas across Serbia and the region. This study confirms the importance of citizen engagement and technological literacy in the transformation of urban environments through smart solutions, underscoring the potential of these technologies to improve everyday life and achieve sustainable urban development.

1. Introduction

The city of Novi Sad, which was awarded the title of European Capital of Culture in 2022 (ECoC), is rapidly developing and becoming an ideal location for the implementation of innovative smart city solutions [1]. This status allows the city to integrate cutting-edge technologies, not only enhancing infrastructure but also raising public awareness of the benefits that smart cities offer, such as more efficient urban services, improved traffic solutions, and sustainability [2,3]. The development of digital services and smart mobility further contributes to making Novi Sad a model of sustainable urban development [4]. Mohanty [5] emphasizes the pivotal role of information and communication technologies (ICT) in improving urban infrastructure and efficiency. Technologies such as the Internet of Things (IoT) and big data analytics enable cities to manage resources more effectively, enhancing sustainability and reducing environmental impact [6]. In the context of Novi Sad, the successful application of these technologies may depend on how willing citizens and local authorities are to embrace new technologies [7]. In the context of the aforementioned discussion, the following hypothesis is proposed:

H1. 

The acceptance of new technologies (ANT) has a positive and significant impact on the level of use and integration of smart city solutions.

Key aspects of smart city development include smart infrastructure, advanced transportation systems, energy efficiency, and innovations in healthcare, all aimed at improving quality of life and reducing environmental burdens [8]. These components enable cities to become more sustainable and better suited to long-term urban development, particularly in response to increasing population demands for resources [9]. For instance, digital networks and the Internet of Things (IoT) enable better coordination and resource management, leading to greater efficiency in city operations [10]. Conversely, Augusto [11] highlights that the integration of digital technologies, particularly in data management, plays a crucial role in achieving sustainability in urban areas. He emphasizes that technological solutions alone are insufficient if citizens are not involved in the process or aware of the benefits these technologies can bring to everyday life. Therefore, awareness of smart solutions becomes essential for successful integration. Yin et al. [12] and Wu et al. [13] suggest that a high level of citizen awareness about the technological advantages of smart cities is necessary for the successful implementation of these solutions. When citizens understand how technology can directly improve their quality of life, such as reducing traffic congestion or increasing energy efficiency, they are more willing to adopt and use these technologies. On the other hand, Azevedo Guedes et al. [14] point out that the long-term integration of smart solutions relies on continuous awareness-raising, which increases citizen engagement.

Haque et al. [15] and Sorri et al. [16] argue that continuous education about smart technologies is a significant factor in achieving sustainable cities. They assert that without adequate awareness, even the most innovative technological solutions will not reach their full potential. Similarly, Ullah et al. [17] suggest that citizen engagement and active participation in these solutions are critical for the long-term success of smart city projects.

H2. 

Awareness of smart cities (ASC) positively influences the level of use and integration of smart city solutions.

Tamariz-Flores and Torrealba-Meléndez [18] investigate advanced vehicle systems in smart cities, particularly emphasizing how digital technologies enhance the efficiency of urban transportation and reduce traffic congestion. Their research shows that these systems, when integrated with city infrastructure, contribute to safety and sustainability, while also improving the overall functionality of transport networks, which is crucial for achieving sustainability in smart cities. Linking these technologies with citizens’ everyday needs proves to be key for the efficiency of city services. In contrast, Aihara and Law and Linch [19] highlight a participatory approach through which citizens can directly engage in managing city resources via crowdsourcing platforms. This engagement leads to better resource allocation and improves the quality of life, especially in developing cities. Active citizen participation increases their willingness to accept and utilize advanced technologies, accelerating the implementation of smart solutions [20,21]. Amine [22] draws attention to the energy efficiency of smart technologies as a key component of sustainable urban development. His research demonstrates that resource optimization through these technologies helps cities become more resilient to environmental challenges. ELKheshin and Saleeb [23] emphasize that effective resource management is critical for long-term sustainability, not only in urban areas like Novi Sad but also more broadly. Ringel [24] underscores that technologies must be adapted to the specific needs of local communities. This is particularly important for cities in the process of developing smart solutions, where local challenges and needs can shape the direction of development. On the other hand, Berawi [25] and Campisi et al. [26] argue that smart cities are successful only when technological innovations are integrated into key urban sectors such as transportation, energy, and healthcare, optimizing resource use and improving service delivery efficiency. The key to the success of these technologies lies in citizens recognizing their benefits. When digital services are utilized in everyday life, the level of acceptance and integration of smart solutions increases, ensuring greater efficiency in urban systems. In this context, Fadhel et al. [27] point out that different approaches to data fusion in smart cities enable more efficient technology applications and real-time resource optimization. The Internet of Things (IoT) plays a crucial role in connecting various sectors, from transportation to energy, further contributing to the sustainability and efficiency of smart cities [28].

H3. 

Engagement in the use of digital services (EUDS) positively influences the level of use and integration of smart city solutions.

The development of smart cities depends on various factors, including innovation and technological advancement, but also social inclusion and sustainable leadership [20]. While cities such as London, Singapore, and Barcelona demonstrate successful examples of renewable energy applications, their approach is significantly focused on reducing the environmental footprint and improving the quality of life [29]. However, due to various challenges, including data protection and privacy, cross-sector collaboration is necessary to ensure the full implementation of technologies [30]. In this context, energy efficiency emerges as a leading pillar for the long-term success of smart cities. Evans et al. [31] point out that the optimization of energy resources not only contributes to reducing environmental impact but also directly improves living standards. Suartika and Cuthbert [32] suggest that technological progress, in itself, is insufficient to achieve sustainable cities without integrating social and environmental factors into planning. Kuru and Ansell [33] developed the TCitySmartF model, which offers cities a framework for transformation through smart governance, active civic engagement, and technological advancement. This framework allows cities to integrate advanced technologies such as the IoT and artificial intelligence, enhancing key aspects such as energy efficiency and mobility [34]. Ahada et al. [35] added that the integration of the IoT with renewable energy sources is crucial for efficient resource utilization, and process automation helps achieve sustainable goals. Smart homes are becoming an integral part of smart cities, contributing to improved living standards for citizens [36]. These systems increase citizens’ interest in technologies such as smart mobility, encouraging greater engagement in the use of urban solutions. Technologies such as electric vehicles and automated transportation systems not only improve efficiency but also reduce pollution [21]. When citizens recognize the benefits of smart mobility, such as reduced traffic congestion and improved environmental conditions, their interest in using these technologies increases, contributing to faster integration of smart city solutions into everyday life [30]. This citizen readiness to engage with and utilize new technologies represents a key prerequisite for the sustainable development of urban environments [37].

H4. 

Interest in the development of smart mobility (ISMD) has a positive and significant impact on the level of use of smart city solutions.

Lee, Hunter, and Chung [38] emphasize the importance of integrating advanced technologies, such as the IoT and big data, in smart tourist cities, which not only enhances the tourist experience but also contributes to the sustainable development of cities. These technologies provide personalized services to tourists and improve resource efficiency while reducing the environmental footprint of cities. On the other hand, Bibri [39] highlights the importance of an interdisciplinary approach to using big data to address sustainability challenges in smart cities, noting that optimizing technological resources can significantly improve cities’ environmental performance. Lai et al. [40] view the development of smart cities through the lens of standardization, arguing that technical standards enable faster and more efficient implementation of advanced technologies. They emphasize that standardization is key to achieving energy efficiency and better infrastructure integration. Evans et al. [31], however, point to practical challenges, such as data security and cross-sector collaboration, which must be overcome to fully realize the potential of smart cities. Silva et al. [41] study smart city architectures and underscore the importance of properly designing technological solutions in the context of long-term sustainability. Their approach shows that efficient resource management can strengthen cities’ resilience to environmental challenges and enable more flexible infrastructure. In this sense, citizens’ perceptions of the benefits brought by smart cities become crucial for their acceptance and successful integration. Cities that effectively communicate the benefits of smart technologies, such as increased mobility, reduced traffic congestion, and improved quality of life, achieve greater success in implementing smart solutions. Sharifi et al. [42] suggest that smart cities have great potential to contribute to achieving sustainable development goals but emphasize that citizens must be aware of the advantages these technologies offer. Only through active citizen participation can smart cities achieve sustainability and enhance the efficiency of urban environments.

H5. 

Perception of the benefits of smart cities (PSCB) positively influences the level of use and integration of smart city solutions.

It is important to emphasize that smart cities are not isolated from their rural surroundings. The development of smart technologies that include both cities and neighboring villages enables a more comprehensive approach to sustainable development at the regional level [43]. Smart solutions implemented in villages, such as the digitalization of public services, optimization of energy resources, and improvement of communication infrastructure, can significantly enhance the quality of life for rural populations while also relying on connectivity with urban centers [44]. This balanced approach contributes to the social, economic, and environmental sustainability of the entire region, enabling integrated development that improves living conditions in both cities and villages [45].

The aim of this research is to examine the acceptance and integration of smart city solutions among the citizens of Novi Sad, with a particular focus on smart mobility and digital services, utilizing the Technology Acceptance Model (TAM). The study addresses the factors that influence citizens’ willingness to adopt smart technologies, such as awareness of smart cities, perception of benefits, and engagement in digital services. Novi Sad, as the European Capital of Culture for 2022, has a unique opportunity to position itself as a leader in the integration of smart solutions, and this research fills the existing gap in the literature regarding technology acceptance in smaller cities, providing valuable insights into citizens’ attitudes. The results will contribute to the development of strategies to enhance ecological efficiency, reduce traffic congestion, and improve service quality, which is crucial for sustainable urban development and quality of life in Novi Sad and the broader region. Previous research has demonstrated the application of the Technology Acceptance Model (TAM) in various contexts. For example, works such as those by Yin et al. [12] and Davis [46] utilize this model to explain the acceptance of new technologies. Additionally, the research by Venkatesh et al. [47] further extends the TAM through the development of the Unified Theory of Acceptance and Use of Technology (UTAUT), emphasizing the importance of social and contextual factors. The application of the Technology Acceptance Model (TAM) is becoming increasingly important in understanding the adoption of innovations in smart cities. Robles-Gómez et al. [48] explored the acceptance of an IoT cloud platform using a hybrid UTAUT model/TAM, revealing that the perception of ease of use and usefulness significantly influences user behavior. Prakosa and Sumantika [49] analyzed the acceptance of electronic marketplaces using the TAM, indicating that user trust plays a key role in the success of smart systems. Alsharafat [50] linked the IS Success Model with the TAM to identify factors influencing the acceptance of mobile learning, emphasizing the importance of system and service quality. In the context of smart cities, the quality of infrastructure can significantly impact citizen engagement. Mailizar, Almanthari, and Maulina [51] extended the TAM to e-learning, incorporating factors such as facilitating conditions and social influence, further highlighting the importance of community engagement for the acceptance of solutions in smart cities.

The title of the European Capital of Culture (ECoC—European Capital of Culture) held by Novi Sad in 2022 represented not only recognition of the city’s cultural significance but also an opportunity for the city to present itself as a modern center open to innovations and technological solutions that improve citizens’ daily lives. Although the smart city theme was not central to the official ECoC program, the idea of “culture as a way of life” encompasses modernization through smart technologies that can contribute to resource preservation, improvement of urban services, and better quality of life. Digital innovation and smart city solutions enable transformation aligned with the modern needs of the community, supporting ecological, cultural, and economic aspects of sustainability. This research aims to explore citizens’ readiness to adopt innovative solutions within the smart city concept, using the ECoC title as a foundation for the city’s long-term development through the implementation of smart technologies. Although the research primarily focuses on the urban area, there is also a recognized need to integrate smart solutions in surrounding rural communities, where similar solutions can enhance the quality of life. In this way, the development of Novi Sad through smart solutions becomes a natural continuation of the cultural and technological progress initiated during the ECoC year, further strengthening the connection between cultural significance and technological advancement for the entire community.

2. Methodology

This research aimed to provide insight into the attitudes and perceptions of the citizens of Novi Sad regarding the “smart city” concept and the smart mobility system. The focus of the study was on identifying the level of awareness and interest and evaluating the key aspects that citizens consider most important in the context of Novi Sad’s development as a smart city.

2.1. Procedure and Sample

The data were collected using a quantitative method through the TAPI technique (tablet-assisted personal interviewing). This method relies on face-to-face interviews conducted by trained interviewers, with respondents’ answers being directly entered via tablets. The TAPI technique was chosen due to its advantages over traditional paper surveys, as it reduces the possibility of data entry errors and allows for direct interaction with respondents [52]. Compared to online surveys, this method ensures higher data reliability, as interviewers have direct control over the accuracy of the information collected [53]. The use of tablets allowed interviewers to ask questions from a pre-prepared questionnaire, while responses were automatically entered into an electronic format, reducing the likelihood of input errors and speeding up the data collection process. This approach not only increased efficiency but also enabled faster and more accurate data processing. Data were collected between 29 August and 5 September 2024, providing ample time for interviews and ensuring a high response rate. The research included 1242 respondents, with a stratified random sample ensuring proportional representation of key demographic characteristics such as gender, age, education level, and income. Respondents were carefully selected using a random sampling method to ensure the sample was representative according to criteria such as gender, age (over 18 years), education level, and place of residence, thereby ensuring the accuracy and relevance of the results. To minimize subjectivity and avoid moral hazard, the questionnaire was structured with standardized, closed-ended questions, which reduced the impact of researcher bias on respondents’ answers. The use of objective scales, such as Likert scales (1—strongly disagree, 5—strongly agree) [54], ensured consistency in the collected data, facilitating quantitative analysis of attitudes and perceptions. Respondents were thoroughly informed about the research objectives and their rights, with guaranteed anonymity, allowing them the freedom to express their views without fear of potential consequences.

The sample for this study consists of 1242 participants, with a nearly equal gender distribution of 48.31% male and 51.69% female respondents. The age distribution shows that the majority of participants are between 25 and 34 years old (24.15%), followed by those aged 35–44 years (20.13%) and 45–54 years (16.10%), indicating a balanced representation across different adult age groups. In terms of education, most participants have completed high school (32.21%), while a significant portion holds a university degree (24.15%), with 15.46% having obtained a master’s or PhD degree. The employment status reveals that 40.26% of participants are employed, 16.10% are students, and 35.59% are retired. Household income is largely concentrated in the EUR 500–1000 range (32.21%), with 24.15% earning between EUR 1001 and EUR. Regarding location, 35.59% of participants reside in medium-sized towns, while 28.17% live in small towns, and 24.15% are from large cities. This demographic profile offers a comprehensive overview of a diverse sample across various socio-demographic characteristics (

Table 1. Socio-demographic characteristics of respondents.

Characteristic	Categories	Frequency	Percentage (%)
Gender	Male	600	48.31%
	Female	642	51.69%
Age Group	18–24 years	150	12.08%
	25–34 years	300	24.15%
	35–44 years	250	20.13%
	45–54 years	200	16.10%
	55–64 years	150	12.08%
Education	Elementary School	100	8.05%
	High School	400	32.21%
	College	250	20.13%
	University Degree	300	24.15%
	Master’s/PhD	192	15.46%
Employment	Employed	500	40.26%
	Unemployed	100	8.05%
	Student	200	16.10%
	Retired	442	35.59%
Household Income	Less than 500 EUR	200	16.10%
	500–1000 EUR	400	32.21%
	1001–1500 EUR	300	24.15%
	1501–2000 EUR	242	19.48%
	More than 2000 EUR	100	8.05%
Location Size	Village	150	12.08%
	Small town	350	28.17%
	Medium town	442	35.59%
	Large city	300	24.15%

).

2.2. Questionnaire Design

The questionnaire was designed to capture key aspects of citizens’ perceptions of the smart city concept. Before conducting the main research, the validity and reliability of the questionnaire were tested on a pilot sample of 50 respondents. The obtained Cronbach’s alpha was 0.705, indicating a high level of internal consistency, thereby confirming the reliability of the questionnaire for further use. The quality of the collected data was verified by analyzing a random sample of 20% of the data, ensuring the accuracy and consistency of the results. This process was crucial for eliminating potential discrepancies and reducing the risk of errors that could have occurred during data entry and collection.

The questionnaire was based on previous relevant research on smart cities, including studies by Mohanty [5], Berawi [25], and Tamariz-Flores [18]. The focus was on factors such as citizens’ awareness, perceived benefits of smart solutions, interest in smart mobility, acceptance of new technologies, and engagement in using digital services. Special attention was given to examining how citizens use and integrate smart solutions into their daily lives, with an emphasis on their contribution to improving quality of life and sustainability (

Table 2. Overview of factors, items, and abbreviations.

Factor	Items	Abbreviations
Awareness of Smart Cities (ASC)	I am aware of the technologies that improve mobility in my city.	ASC1
	I have sufficient information about the contribution of smart cities to energy efficiency.	ASC2
	I am aware of smart city initiatives in my city.	ASC3
Perception of Smart City Benefits (PSCB)	I believe that smart cities will enhance public safety through technology.	PSCB1
	Smart cities can reduce greenhouse gas emissions.	PSCB2
	Smart waste management will contribute to a cleaner environment.	PSCB3
Interest in Smart Mobility Development (ISMD)	I support the development of electric scooter and bicycle systems.	ISMD1
	Traffic-related digital applications would facilitate mobility.	ISMD2
	I am interested in using smart parking systems.	ISMD3
Acceptance of New Technologies (ANT)	I am willing to use applications for household energy optimization.	ANT1
	I regularly use the city’s digital services for public utilities.	ANT2
	Smart devices improve daily household tasks.	ANT3
Engagement in Using Digital Services (EUDS)	I use applications to interact with city authorities.	EUDS1
	I use digital platforms for planning public transportation.	EUDS2
	I use systems for monitoring energy consumption.	EUDS3
Level of Usage and Integration of Smart City Solutions (Output Variable) (LUISCS)	I follow traffic and public transportation through smart systems.	LUISCS1
	Smart technologies have made my daily life easier.	LUISCS2
	I use city applications to monitor water and electricity consumption.	LUISCS3
	Smart city services have become part of my daily life.	LUISCS4

). This study utilizes the Technology Acceptance Model (TAM) as a fundamental theoretical framework to explore how citizens in Novi Sad adopt and integrate smart city solutions. Developed by Davis [46], this well-established model explains the acceptance of new technologies through two key factors: perceived usefulness (PU) and perceived ease of use (PEOU). These factors are closely tied to the primary components of our research, including awareness of smart cities (ASC), acceptance of new technologies (ANT), and engagement in using digital services (EUDS). In the context of our study, perceived usefulness relates to the belief among citizens that smart solutions, such as advanced mobility options and digital services, will improve their quality of life. Meanwhile, perceived ease of use refers to how easily citizens can access and utilize these technologies, which is critical for their willingness to accept them. By incorporating this model, the research enhances the understanding of the factors that influence the effective implementation and integration of smart city solutions into the daily lives of residents.

2.3. Data Processing

Data analysis was conducted using SPSS 23.0 and SmartPLS 3, which offer comprehensive data processing and modeling capabilities. In SPSS, descriptive statistics were created and sample adequacy tests and an analysis of data distribution normality were performed. The normality of the distribution was assessed through skewness and kurtosis analyses, where skewness values were close to zero, indicating symmetric data distribution [55]. The kurtosis values fell within the recommended range (−1 to +1), suggesting no significant deviations from normal distribution. Additionally, the Kolmogorov–Smirnov and Shapiro–Wilk tests for normality confirmed statistically significant p-values, further strengthening the assessment of normality. To examine multicollinearity among variables, the variance inflation factor (VIF) was used, with all VIF values below 3, indicating the absence of multicollinearity [56]. The tolerance index was also above the recommended threshold of 0.2, confirming no overlap among variables. The reliability of the questionnaire was assessed using Cronbach’s alpha coefficient, with an overall alpha value of 0.705, and all individual factors exceeding 0.7, indicating a high level of internal consistency in the measurement scale.

An exploratory factor analysis (EFA) was conducted to identify latent structures in the data, enabling a reduction in variables and the elimination of redundant items, with key items identified as the most representative for latent factors [57]. The Kaiser–Meyer–Olkin (KMO) measure of sample adequacy was 0.695, indicating satisfactory adequacy for conducting factor analysis, while Bartlett’s test of sphericity was statistically significant (χ² = 1.529, p < 0.001), confirming a sufficiently high correlation among variables [58]. Cluster analysis was performed to group respondents based on similarities in their responses. The k-means method was used to identify homogeneous groups within the data, facilitating easier interpretation of results [59].

In SmartPLS, the PLS algorithm was applied for structural model analysis. The Fornell–Larcker criterion was used to verify that all average variance extracted (AVE) values exceeded 0.5, confirming satisfactory convergent validity. Furthermore, the AVE values were greater than the intercorrelations among latent variables, which confirmed adequate discriminant validity. The evaluation of model fit showed that all indicators were within acceptable ranges, with SRMR (0.013) and NFI (0.976) indicating a high level of model fit. Additionally, the VIF values for the manifest variables were below 5 (ranging from 1.008 to 1.181), indicating the absence of multicollinearity among variables [60] (Figure 1).

All key reliability and validity indicators, including AVE, CR, Cronbach’s alpha, and rho_A, were above the recommended thresholds, confirming good convergent validity and high internal consistency of the constructs [60] (Figure 2).

3. Results

3.1. Results of Descriptive and Factor Analysis

The research results indicate moderate to high average scores regarding awareness, perceived benefits, and acceptance of smart technologies among the citizens of Novi Sad. The factor of awareness of smart cities (ASC) recorded average scores ranging from 3.08 to 3.67, with standard deviations indicating moderate variations in responses, while the internal consistency of this factor was high (α = 0.872). The perception of smart city benefits (PSCB) showed stable results with average scores between 2.97 and 3.60 and a high reliability coefficient (α = 0.881), indicating consistent respondent opinions about the advantages of smart technologies. Interest in smart mobility development (ISMD) displayed close average values (3.29–3.61), with narrower variations in responses and a high level of reliability (α = 0.864). The acceptance of new technologies (ANT) factor revealed positive attitudes, with average scores ranging from 3.92 to 4.30, while higher variations in standard deviations reflected differing levels of acceptance among respondents. The reliability of this factor remained high (α = 0.877). Engagement in the use of digital services (EUDS) recorded average scores between 3.71 and 4.12, indicating significant acceptance of digital solutions, while internal consistency remained stable (α = 0.869). Finally, the level of use and integration of smart city solutions (LUISCS) had average values from 3.54 to 3.86, but with somewhat lower reliability (α = 0.677), which may suggest varying levels of involvement with these technologies (

Table 3. Descriptive statistics and alpha coefficients for constructs.

	m	SD	α	λ
ASC1	3.67	0.933	0.705	0.725
ASC2	3.08	1.409	0.696	0.881
ASC3	3.50	1.155	0.695	0.876
PSCB1	2.97	1.420	0.698	0.893
PSCB2	3.60	1.133	0.701	0.849
PSCB3	3.60	1.067	0.699	0.816
ISMD1	3.61	1.120	0.698	0.825
ISMD2	3.29	1.272	0.694	0.818
ISMD3	3.58	1.087	0.701	0.895
ANT1	4.30	2.035	0.679	0.774
ANT2	3.92	1.838	0.696	0.751
ANT3	4.00	1.772	0.682	0.843
EUDS1	3.75	1.694	0.686	0.835
EUDS2	3.71	1.803	0.666	0.761
EUDS3	4.12	1.804	0.676	0.743
LUISCS1	3.74	1.593	0.692	0.779
LUISCS2	3.86	1.739	0.688	0.853
LUISCS3	3.56	1.708	0.689	0.805
LUISCS4	3.54	1.691	0.677	0.858

Note: m—arithmetic mean, SD—standard deviation, α—Cronbach’s alpha, λ—factor loading.

).

The results of the statistical measures indicate that all factors are stable and reliable. The mean values (M) range from 3.31 to 4.07, reflecting positive ratings from respondents across all factors. The standard deviations (SD) demonstrate moderate variability in responses, while the Cronbach’s alpha (α) values for all factors exceed 0.7, confirming high internal consistency of the scales. Composite reliability (CR) also surpasses 0.8 for all factors, further confirming the reliability of the model. The average variance extracted (AVE) values are above the recommended threshold of 0.5, indicating good convergent validity. The cumulative variance explained by the factors is 52.853%, suggesting satisfactory model informativeness (

Table 4. Statistical measures of factors.

Factor	m	SD	α	IE	% Variance	Cumulative %	CR	AVE
ASC	3.42	1.255	0.872	2.709	14.255	14.255	0.869	0.69
PSCB	3.31	1.412	0.881	2.358	12.412	26.668	0.889	0.728
ISMD	3.49	0.147	0.864	1.548	8.147	34.815	0.884	0.717
ANT	4.07	0.297	0.877	1.196	6.297	41.112	0.833	0.625
EUDS	3.86	0.176	0.869	1.174	6.176	47.288	0.824	0.609
LUISCS	3.62	0.691	0.677	1.057	5.564	52.853	0.800	0.615

Note: m—arithmetic mean, SD—standard deviation, α—Cronbach’s alpha, IE—initial eigenvalues CR—composite reliability, AVE—average variance extracted.

).

3.2. Cluster Analysis Results

The chart illustrates the differences between two clusters regarding key variables related to smart city solutions. Respondents in Cluster 1 generally display moderate values for most variables, with particularly lower scores for awareness of smart cities (ASC) and the perception of smart city benefits (PSCB). In contrast, Cluster 2 exhibits higher values across nearly all variables, indicating a greater level of engagement in the use of digital services (EUDS) as well as higher acceptance of new technologies (ANT). These results suggest that respondents in Cluster 2 are better informed, more engaged, and more inclined to adopt and integrate smart city solutions compared to those in Cluster 1 (Figure 3).

Figure 4 displays the distances between the two clusters, with both groups (1 and 2) showing identical distance values of 4.461. This suggests that the clusters are significantly separated, indicating a clear distinction between these groups in the context of the analyzed variables [52]. The different clusters likely represent substantially different profiles of respondents or entities in the study. The distance of 4.461 indicates a certain degree of separation between the two clusters, but since the distance values are identical for both clusters, it suggests that this difference is symmetrical in both directions.

Although the distances between clusters are identical, the results of the ANOVA test show significant differences between the clusters for a large number of variables, such as awareness of smart cities, perceived benefits, interest in smart mobility, acceptance of new technologies, and engagement in digital services (

Table 5. ANOVA results for cluster differences.

	Cluster		Error		F	p
	Mean Square	df	Mean Square	df
ASC1	0.935	1	0.871	105	1.074	0.300
ASC2	34.765	1	1.932	105	17.996	0.000
ASC3	4.491	1	1.331	105	3.374	0.067
PSCB1	46.877	1	1.945	105	24.102	0.000
PSCB2	2.993	1	1.282	105	2.335	0.127
PSCB3	1.179	1	1.140	105	1.034	0.310
ISMD1	15.920	1	1.232	105	12.925	0.000
ISMD2	30.846	1	1.571	105	19.633	0.000
ISMD3	7.430	1	1.173	105	6.335	0.012
ANT1	673.836	1	3.038	105	221.809	0.000
ANT2	193.948	1	3.065	105	63.281	0.000
ANT3	305.287	1	2.643	105	115.529	0.000
EUDS1	203.186	1	2.539	105	80.018	0.000
EUDS2	412.526	1	2.577	105	160.071	0.000
EUDS3	411.248	1	2.582	105	159.288	0.000
LUISCS1	72.294	1	2.423	105	29.842	0.000
LUISCS2	135.361	1	2.792	105	48.486	0.000
LUISCS3	213.105	1	2.573	105	82.835	0.000
LUISCS4	219.451	1	2.505	105	87.618	0.000

).

The results of the ANOVA analysis indicate significant differences between the clusters for most of the examined variables. The largest differences were observed in items related to the acceptance of new technologies (ANT) and engagement in the use of digital services (EUDS), where the F-values are very high and statistically significant (p < 0.001). For the variable ANT1, the F-value is 221.809 (p = 0.000), indicating a pronounced difference between the clusters in terms of technology acceptance. Similarly, significant differences between the clusters were also observed in items related to the perception of smart city benefits (PSCB), such as PSCB1 with an F-value of 24.102 (p = 0.000). Variables related to interest in the development of smart mobility (ISMD), like ISMD1 (F = 12.925, p = 0.000) and ISMD3 (F = 6.335, p = 0.012), also show significant differences. However, for variables such as ASC1 (awareness of smart cities) and PSCB3, the F-values are not statistically significant (p > 0.05), suggesting no significant differences between the clusters for these items.

3.3. Results of SEM Analysis and Hypothesis Testing

All values from the SEM analysis indicate that the model for the level of usage and integration of smart city solutions demonstrates a good balance between complexity and data fit, considering the lower values of the AIC and BIC criteria. These results suggest that the model is well-suited to the data, while maintaining an appropriate level of simplicity without overfitting (

Table 6. Model selection criteria.

Factor	AIC	AICu	AICc	BIC	HQ	HQc
Level of usage and integration of smart city solutions	−124.412	−118.385	543.758	−97.405	−113.948	−113.675

Note: AIC (Akaike Information Criterion), AICu (Akaike Information Criterion with Unit Correction), AICc (Corrected Akaike Information Criterion), BIC (Bayesian Information Criterion), HQ (Hannan–Quinn Criterion), HQc (Corrected Hannan–Quinn Criterion).

).

The analysis revealed statistically significant relationships between the examined latent variables, confirming the importance of various factors in the usage and integration of smart city solutions. A notable finding was the positive effect of acceptance of new technologies (ANT) on the level of usage and integration of smart city solutions (LUISCS) (β = 0.224, t = 5.34, p = 0.000). This result indicates that individuals who are more open to adopting new technologies are also more likely to integrate smart city solutions into their daily lives. Additionally, awareness of smart cities (ASC) plays a crucial role, as higher awareness significantly contributes to increased usage of smart city technologies (β = 0.548, t = 7.61, p = 0.005). This suggests that when citizens are more informed about smart city initiatives and their benefits, they are more likely to embrace these innovations. Moreover, engagement in the use of digital services (EUDS) was found to have a strong impact on the adoption of smart solutions (β = 0.280, t = 6.02, p = 0.000). The more citizens actively engage with digital city services, the more likely they are to integrate other smart city technologies, further enhancing their daily urban experience. Interest in smart mobility development (ISMD) also emerged as a significant driver of smart city integration (β = 0.304, t = 4.54, p = 0.003). This finding highlights that individuals who show interest in innovative mobility solutions, such as electric scooters or smart public transportation systems, tend to be more proactive in incorporating smart technologies into their routines. The perception of smart city benefits (PSCB) was shown to have the strongest effect on smart city solution integration (β = 0.414, t = 8.81, p = 0.002). When citizens clearly recognize the benefits of smart city technologies—such as improved mobility, energy efficiency, and enhanced quality of life—they are more likely to adopt and integrate these solutions (

Table 7. Results of structural equation modeling and hypothesis testing.

Hypothesis	Path	β	m	SD	t	p	Confirmation
H1	ANT → LUISCS	0.224	0.224	0.042	5.338	0.000	supported
H2	ASC → LUISCS	0.548	0.527	0.072	7.610	0.005	supported
H3	EUDS → LUISCS	0.280	0.289	0.047	6.016	0.000	supported
H4	ISMD → LUISCS	0.304	0.306	0.067	4.541	0.003	supported
H5	PSCB → LUISCS	0.414	0.403	0.047	8.810	0.002	supported

Note: β—estimate, m—arithmetic mean, SD—standard deviation, t—t value, p—p value.

).

Figure 5 illustrates that the model explains 68.4% of the variance in the level of usage of smart city solutions, indicating strong predictive power. This substantial proportion of explained variance suggests that the model effectively captures the key factors influencing the integration and adoption of smart city technologies, providing a robust framework for understanding how citizens engage with and utilize these solutions in urban environments.

4. Discussion

Smart cities develop not only through technical innovations but also through active citizen participation, which is a crucial factor for the successful implementation of these solutions. Our findings confirm that awareness of smart cities (ASC) is essential for the acceptance and integration of new technologies, aligning with the work of Yin et al. [12], who emphasize that citizens must be well-informed and involved in the process for smart cities to become a sustainable reality. In terms of technical solutions, city architecture and sustainability are inseparable elements, as highlighted by Silva et al. [41]. Our study corroborates that the perception of smart city benefits (PSCB) plays a key role in the acceptance of these solutions, especially in areas like energy efficiency and mobility improvement. Citizens who recognize these benefits are more likely to adopt these technologies, which is vital for the long-term success of smart cities.

Effective data management and integration, as emphasized by Fadhel et al. [27], are crucial for the functionality of smart cities. Our results indicate that greater engagement in digital services (EUDS) enables better connectivity between technologies, ensuring more efficient implementation of smart solutions. This underscores the importance of integrating various systems, such as transport, energy, and healthcare, which can only be achieved through active citizen participation. Furthermore, the development of intelligent systems in smart homes, as noted by Huda et al. [36], demonstrates how individual solutions, such as smart homes, can be expanded to the broader urban environment, contributing to city sustainability. Our findings support this idea, showing that younger generations, with a higher interest in smart technologies, are more willing to adopt and use them, further promoting the development of sustainable smart cities.

Nassereddine and Khang [28] highlight that IoT technologies play a key role in connecting different segments of smart cities, enabling more efficient real-time resource management. Our study shows that interest in smart mobility is a significant factor in the adoption of smart transport solutions, suggesting that citizens recognize the value of the IoT in daily urban services, including transportation and energy management. Moreover, Lnenicka et al. [61] emphasize the importance of open data as the foundation for better city management. Our results reveal that citizen engagement in digital services is beneficial not only for individual but also for the sustainability and transparency of urban systems. This confirms the need for open access to data in smart cities, which contributes to greater citizen trust and more effective decision-making.

Transportation systems based on machine learning, as Prakash et al. [21] point out, play a crucial role in reducing traffic congestion and improving transport efficiency. Our study aligns with these findings, showing that citizens who recognize these benefits actively support the implementation of smart solutions in transportation, directly improving the quality of life in urban areas. Energy efficiency, researched by Cui and Cao [62], is also critical for the success of smart cities. Our results confirm that citizens who recognize the importance of energy efficiency are more inclined to accept smart solutions, which is essential for the long-term sustainable development of smart cities. The connection between perceived benefits and their actual application highlights the need for continuous education and informing citizens about the advantages these technologies bring. Lim et al. [63] emphasize the positive impact of smart city development on quality of life, but they stress that citizen awareness is key to successful implementation. Our study supports this, showing that awareness of smart cities is one of the most important prerequisites for the integration of technologies and the realization of the full potential of these urban environments.

Our results suggest that the integration of smart solutions into urban areas can directly contribute to better resource utilization and more efficient service delivery, which is particularly relevant for neighboring villages that rely on urban resources and infrastructure. This finding aligns with the research of Komorowski and Stanny [64], who emphasize that smart villages can emerge near urban centers where they benefit from access to city resources, further enhancing public service efficiency and fostering social and economic connectivity between urban and rural communities. By introducing smart technologies into villages, a synergistic effect can be achieved in which both communities—urban and rural—benefit from improved social, economic, and environmental sustainability [65]. Meyn [66] highlights that digitalization can significantly enhance life in rural areas by providing better connectivity and access to resources, thereby strengthening sustainability and economic growth across the entire region, as our findings also confirm. Such a comprehensive approach contributes to cohesive regional development, allowing long-term benefits for both urban and rural communities.

5. Conclusions

This study makes a significant contribution to understanding the factors that influence the successful adoption and implementation of smart city solutions, with a particular focus on the areas of smart mobility and digital services in Novi Sad. Unlike traditional approaches, our findings show that awareness of smart cities, readiness to adopt new technologies, active engagement in digital services, and interest in smart mobility form the foundation for successful integration of these solutions. Compared to similar research, this study emphasizes the importance of technological literacy as a key factor in the urban context, where citizens’ willingness to adopt technologies becomes one of the most critical prerequisites for the success of smart cities.

5.1. Theoretical and Practical Implications

The findings of this study expand the existing literature on smart cities and urban innovations, offering specific insights into the factors that affect the adoption of smart city solutions. By introducing the concept of technological literacy as a decisive factor, this work confirms theoretical models of technology adoption, suggesting that these models are not limited to technologically advanced cities but can be successfully applied in environments like Novi Sad. Moreover, the study highlights the positive impact of perceived benefits of smart technologies on citizens’ motivation to actively engage in their use, emphasizing the importance of creating a clear narrative about the advantages of these solutions in everyday life.

The practical implications of this study are significant, not only for Novi Sad but also for the broader region. As the European Capital of Culture in 2022, Novi Sad has the opportunity to become a leader in implementing smart city solutions in the Balkans. Our findings suggest that strategies focused on educating and informing citizens about the tangible benefits of smart cities, such as improved mobility and enhanced public services, could significantly increase the level of engagement and acceptance of these solutions. Implementing innovative smart mobility solutions, such as digital platforms for ride-sharing and improving public transportation, could directly reduce traffic congestion, resulting in a higher quality of life for citizens.

5.2. Limitations

One of the main limitations of this study is the reliance on self-reported data, which may lead to subjective biases from respondents. Additionally, the research was conducted in a single city, limiting the generalizability of the findings to other contexts. Future studies should include more cities and regions to test the similarities and differences in the adoption of smart city solutions. Furthermore, the methodology relying on face-to-face interviews may have excluded respondents who were unavailable or unwilling to participate, potentially introducing sampling bias. While the results provide valuable insights into the attitudes of Novi Sad citizens, caution should be exercised when generalizing these findings to other settings due to the specific characteristics of the respondents and the local context. Future research could expand the scope to different cities to test the applicability of the results in various urban and rural environments. The Technology Acceptance Model (TAM) has certain limitations that are important to consider in the context of this study. This model primarily focuses on perceived usefulness and perceived ease of use, which can be overly simplistic for the complex situations and variables influencing technology adoption in urban settings, as it does not encompass a range of other social, cultural, and economic factors. Additionally, the TAM’s findings may not be universally applicable to all cities, especially those with differing socio-economic characteristics or technological infrastructure.

Moreover, the model does not account for changes in users’ attitudes toward technology over time, making it challenging to understand the long-term acceptance of smart solutions. Furthermore, the potential for bias in respondents’ answers may affect the accuracy of measuring perceived usefulness and ease of use. The TAM also does not consider the level of technological literacy among users, which can significantly impact their ability to accurately assess the usefulness and ease of using new solutions. Due to these limitations, it is crucial to approach the research findings with caution and consider a broader range of factors that may influence the acceptance of smart city solutions.

One limitation of this study is that it focused exclusively on Novi Sad and its urban characteristics, without a broader inclusion of rural areas in the analysis. Although some respondents are from rural areas, the research did not address specific aspects of smart solutions tailored to rural communities. Future research should expand to include surrounding rural areas to gain a more comprehensive understanding of the potential for integrating smart technologies within regional development, including differences in needs and infrastructural capacities between urban and rural communities.

5.3. Recommendations for Future Research

These results also have broader applicability, as cities in Serbia and the region planning to introduce similar projects could use our findings as a basis for developing successful strategies. Increasing awareness and educating citizens about the benefits of smart technologies have proven to be key steps toward improving the ecological efficiency and sustainability of cities, which directly impacts the improvement of residents’ daily lives. Future research could focus on tracking the long-term effects of implementing smart city solutions through longitudinal studies that monitor changes in citizen engagement and technology adoption. It is also recommended to investigate the effects of specific educational and promotional campaigns aimed at raising awareness of smart cities, as well as analyzing the success of various policies that support their implementation. Furthermore, expanding research to rural areas is important, where the application of smart technologies may have different effects due to challenges such as technological literacy and access to digital infrastructure. Considering the limitations of the Technology Acceptance Model (TAM), future research could focus on incorporating additional factors such as social influences, cultural values, and economic conditions to provide a more comprehensive understanding of the acceptance of smart city solutions. Conducting longitudinal studies that track changes in user attitudes over time can help understand the dynamics of technology acceptance. Qualitative methods, such as interviews and focus groups, would enable a deeper understanding of citizens’ perceptions of smart technologies.

Investigating the impact of technological literacy on acceptance could identify barriers and provide guidance for developing educational programs. Expanding research to various urban and rural settings could test the applicability of results in different contexts, while examining the influence of open data could shed light on how transparency affects citizen engagement. These directions could significantly contribute to a better understanding of the acceptance of smart technologies and strengthen strategies for their successful integration into urban environments.

Although smart cities are the primary focus of this research, it is essential to consider the broader context of applying smart technologies in regional development, particularly in rural areas. Smart villages, which represent an innovative use of technology in rural communities, are becoming an increasingly important aspect of sustainable development. The development of smart villages provides rural areas with access to resources and services similar to those found in urban centers, including the digitalization of public services, optimization of energy resources, and enhancement of communication infrastructure. These solutions significantly improve the quality of life for rural populations while simultaneously leveraging connectivity with cities to support sustainable regional development.