Building Smart Urban Areas: Case Study in Pleiku City, Vietnam

Abstract

Constructing smart cities is currently a pressing concern in many nations in an effort to address issues including environmental pollution, climate change, and the growing urban population. This study aims to understand the factors that influence the development of smart urban areas in a Vietnamese class I city. We collected research data by conducting a survey with 200 representative samples from Pleiku City. Research results have shown that the group of organizational and implementation factors has the greatest influence on smart urban construction in Pleiku City, with a rate of 30.23%. The human resource factor group has a rate of 27.77%, and the policy mechanism group has a rate of 23.36%. Finally, the planning factor group has a contribution rate of 18.65%. Policymakers can use this research result as a guide to implement smart urban construction projects in other cities in Vietnam. We also highlight some policy implications for various solutions, including human resources, finance, policy mechanisms, and planning.

1. Introduction

A United Nations report states that 3.6 billion people, or 54.6% of the world’s population, reside in metropolitan areas. More than 70% of people on the planet will live in cities by 2050 (of which 64.1% will do so in developing nations and 85.9% in developed ones) [1]. Population growth will have a significant impact on city development due to environmental pollution [2] and a lack of resources such as clean water [3], land [4], and energy [5]. In other words, for city planners, this shift in the population presents serious difficulties. It is up to them to come up with plans for the expanding urban population’s sustainable living standards. In this context, some developed countries have begun to learn and research core technologies and information technology for urban planning [6]. The goal is to develop solutions to control difficult problems that arise during the development of a modern city. Therefore, smart urban construction is expected to help solve the problems that urban areas and urban consequences encounter in order to bring a better life to urban residents [7].

The phrase “smart city” initially arose in the 1990s, primarily referring to the incorporation of Information and Communication Technology (ICT) and infrastructure inside urban areas [8]. Since then, researchers have conducted numerous studies and reviews to better define and understand the concepts involved [9,10,11]. Smart cities are defined as cities with invested human and social capital [12]. A smart city is one that has a developed economy and an interconnected transportation network supported by ICT [13]. It is also recognized as an urban area characterized by sustainable economic development, a high quality of life, and well-managed environmental resources, managed by a government apparatus that allows citizens to participate in referendums [14]. Along with promoting traits like a smart economy, environment, lifestyle, and management, smart cities also combine technology, governance, and society [15,16]. In other words, smart cities are cities for people, creating many opportunities to exploit human potential and encourage innovation.

In Vietnam, urban regions account for 70% of the country’s GDP. Despite making up only 2.9% of the total territory and roughly 22% of the people, these five centrally managed cities provided 46.8% of the nation’s GDP in 2020 [17]. As of September 2022, Vietnam has 888 urban areas, and the urbanization rate is 41.5% [18]. However, as urban areas develop, they gradually encounter limitations, particularly when their development lacks sustainability [19]. To solve this problem, cities need to research, choose, and change development directions to ensure urban connectivity. From there, cities can solve urban problems such as environmental pollution, flooding, air quality, and traffic congestion [20]. Therefore, building smart cities is considered an important method towards sustainable development in the current context in Vietnam [21]. Smart urban development is one of the important driving forces to realize the goal of turning Vietnam into a modern, high-income industrial country by 2045 [22].

Currently, there are not many studies in Vietnam that deeply address the issue of building smart cities. However, according to those that do, the biggest challenge is the attention and awareness of leaders at all levels, as well as people and businesses [23,24]. The perception of smart cities is also being understood in many different ways. There is a question as to whether building smart cities is just a way to deploy the model or develop services [25]. In simpler terms, it involves enhancing the use of information technology in administration and management. The above questions make it difficult to implement smart urban projects in Vietnam. Therefore, studying some factors affecting smart urban construction will help make smart urban construction projects in Vietnam more feasible. It will also contribute to increased interaction between people and the government, as well as improving the quality of life for sustainable development. Pleiku City, a class I urban area and the second largest city in the Central Highlands, will host this research. The research findings will add to the body of knowledge on smart cities. Policymakers can use them as a reference to implement smart urban construction projects in other Vietnamese cities, particularly in class I cities with significant development potential. This study aimed to determine the relationship between policy mechanisms, human resources, planning, and implementation organization in the context of the Central Highlands, which is a unique area in Vietnam.

2. Some Factors Affecting the Construction of Smart Cities: A Literature Review

The construction of smart cities is important for regional economic development [26,27], and it is influenced by many factors [28,29]. Numerous studies have examined various factors and their impact on the development of smart urban areas. Meijer and Boloviar’s demonstrated that the construction of a smart city hinges on three key elements: smart technology, people, and governance [30]. Smart mobility, environment, economics, people, and governance are all indicators of a successful smart city [31]. Other reports have expressed similar views regarding these factors [32,33]. Al Nuaimi also identified some similar factors in their study [34]. Recently, the research team has pointed out a number of important factors in building smart cities through the definition of smart cities, such as human and social factors, life activities, and physical infrastructure [35].

Building smart cities also involves making smarter policies for transparent governance [36], co-creating innovation [37], and encouraging citizen participation [38]. Li et al. (2022) also pointed out the importance of factors related to policy mechanisms in forming a smart city [39]. The research team made three policy recommendations to accelerate smart city development. The implementation of smart city construction policies significantly impacts the level of urban innovation, especially in medium-sized cities [40]. However, the indirect effects of policy implementation are larger than the direct effects [41]. The effectiveness of smart urban policies will be greater in cities with excellent financial conditions and a strong digital economy [42].

Additionally, the nature of planning is to determine urban development models and methods of moving and operating in urban areas [43]. As a result, planning ensures effective land use suitable for production methods and the culture of the people [44]. In each historical period, many urban development models were proposed and implemented. These included the garden city model [45], the vertical urban model [46], and some other modern models [47,48]. In particular, smart urban planning has its own unique implementation method. Smart urban planning starts with surveying and evaluating real-world human problems and needs. The planning process will then provide practical solutions. Planners accurately create forecasting models and test their effectiveness before deciding on the most optimal planning option based on the collected data. To put it another way, planning a smart city entails the deployment of a variety of ICT resources to collect, analyze, visualize, and act on the collected data [49].

Based on a limited number of studies and the research context in Vietnam, we have categorized the factors that influence the construction of smart cities into distinct groups. These include policy mechanisms, human resources, planning, and implementation organization. Figure 1 shows our research model. It was suggested that to overcome the above limitation, this study should apply exploratory factor analysis (EFA) to identify the five groups of factors affecting land use planning in the Dan Phuong district, including the groups of politics (Po), economy (Ec), society (Soc), environment (En), and other (Ot) [27].

3. Research Location and Methods

3.1. Research Location

Pleiku City is located in the north of the Central Highlands, with a total area of 26,076.86 ha. This is the second-largest city in the Central Highlands in terms of population and urban area. Pleiku City is divided into 22 commune-level administrative units, including 14 wards—Chi Lang, Dien Hong, Dong Da, Hoa Lu, Hoi Phu, Hoi Thuong, Ia Kring, Phu Dong, Tay Son, Thang Loi, Thong Nhat, Tra Ba, Yen Do, and Yen The—and 8 communes—An Phu, Bien Ho, Chu A, Dien Phu, Gao, Ia Kenh, Tan Son, and Tra Da. Pleiku City offers several benefits and prospects for the development of green urban areas and infrastructure due to the region’s typical terrain and various landscape values. Furthermore, the city benefits from arranging its urban structure in accordance with topographic and landscape characteristics, resulting in the creation of significant and distinctive areas. In 2023, the average rate of economic growth was 12%. The average annual income per person reached VND 52 million, with the services sector increasing by 12.8%, the industry–construction sector increasing by 18.92%, and the agriculture–forestry sector increasing by 4.65% [50]. Pleiku’s metropolis is gradually catching up to the requirements of a modern, civilized metropolis.

In accordance with the Provincial People’s Committee’s decision to approve the Project “Building Pleiku city towards a smart urban period 2020–2025, with an orientation to 2030”, the city has proactively constructed and developed a Smart Operations Center and related applications, with urban residents serving as the focal point. From there, the city aims to enable all sectors of society to enjoy the benefits and participate in investment in construction, supervision, and smart urban management. Essential infrastructure investment projects must include research and analysis content to consider and supplement application items, such as connecting to the Internet of Things (IoT), sensor integration, and digital technology applications. The application of information technology and IoT infrastructure development must ensure efficiency in urban management and construction. Infrastructure development also needs to avoid duplicate investments in accordance with the National Digital Transformation Program, with a focus on 2030.

3.2. Research Model and Data Analysis

The survey direction prioritized selecting households living along urban streets in the core urban area, suburban areas, and outlying areas. Households on these streets are involved in business, trade, and local civil servants. With a sample size of 235, the survey method was stratified and random. After screening and eliminating some incomplete and unsatisfactory questionnaires, the remaining sample size was 200. Using available survey forms to randomly survey 200 households and individuals, the survey direction prioritized selecting households living along urban streets in the urban core, the urban fringe, and areas far from the urban center; households on these streets are involved in business, trading, the real estate market, the civil service, the public sector, etc., according to the level of urban development and the main streets. The survey gathered 80 forms from the urban core sub-region in the Tay Son and Dien Hong wards (according to urban residential areas along the central streets passing through these 2 wards), 60 forms from the urban fringe area in the Tra Ba and Thang Loi wards (according to urban residential areas along the central streets passing through these 2 wards), and 60 forms in the area far from the urban center in the An Phu and Dien Phu communes (according to urban residential areas along the central streets passing through these 2 communes). Part 2 of the survey, based on the results of the literature review and the research model, focused on four factors. These factors were evaluated using a 5-point Likert scale, which was used to assess the level of influence of the factors on smart urban construction in Pleiku City. During the survey, the authors also encountered some difficulties, especially in approaching the survey participants. The survey participants exhibited varying levels of education. People accessing and contributing ideas for the implementation of smart urban construction still faced many difficulties due to a lack of uniform understanding.

The data analysis of the survey questionnaires followed five steps (Figure 2).

Step 1. Compile and input data from 200 survey forms into Excel, filling in numbers according to each response from residents based on the specified point scale: (1 point) very little impact, (2 points) little impact, (3 points) moderate impact, (4 points) significant impact, (5 points) very significant impact.

Step 2. Reliability analysis: Use Cronbach’s Alpha coefficient to test the reliability of the scale. If the Cronbach’s Alpha coefficient is not within [0.6–0.95], eliminate the variable; next, examine the item–total correlation coefficient. If the item–total correlation coefficient is ≤0.30, eliminate the variable.

Step 3. Exploratory factor analysis: If the KMO (Kaiser–Meyer–Olkin) coefficient is not within the following range: 0.5 < KMO < 1, then the factors are to be eliminated and considered insignificant. Check if Bartlett’s test has a sig. value > 0.05; if so, then the model is insignificant.

Step 4. Factor loading of the rotated matrix: If the extracted variance of the independent variable is ≤50%, the variable is insignificant. This step contributes to the stratification between factors and the degree of influence of each factor.

Step 5. Linear regression analysis to determine the standardized regression coefficient (β), standard error (Sig.), and VIF, forming the regression equation. Use the standardized regression coefficient (β) to evaluate the % level of influence of each factor.

After collecting the survey data, the authors entered data from 200 questionnaires into Excel. The authors scored each survey participant’s responses based on the degree of influence of various factors on smart urban construction. We entered the data into Excel and then transferred it to SPSS 22 software, which processed it through four steps, as illustrated in Figure 2. Finally, the regression equation took the following form:

Yi = β0 + β1X1 + … + βnXn + Ei

(1)

where Yi demonstrates smart urban construction; X1, …, Xn represent factors affecting the construction of smart cities; β0 is a constant and takes the value of Y when all values of X = 0; β1 is the regression coefficient; and Ei is the standard error.

4. Research Results

4.1. Reliability of Factors

Researchers look for Cronbach’s Alpha as a means of determining whether or not a scale is reliable, and they use the variable–total correlation coefficient to weed out irrelevant factors. The findings demonstrate that the Cronbach’s Alpha coefficients for each group of components range from [0.6–0.95]. This demonstrates how reliability is ensured by study data. The 15 observed variables all exhibit total variable correlation coefficients larger than 0.3, satisfying reliability standards and being eligible for additional study, according to the results of the total variable correlation coefficient analysis (

Table 1. Reliability analysis.

No.	Factors	Corrected Item–Total Correlation	Cronbach’s Alpha
I	CS		0.787
1	CS1	0.543	0.758
2	CS2	0.666	0.814
3	CS3	0.649	0.818
II	NL		0.852
4	NL1	0.599	0.845
5	NL2	0.662	0.801
6	NL3	0.668	0.867
7	NL4	0.486	0.823
III	QH		0.797
8	QH1	0.435	0.815
9	QH2	0.428	0.728
10	QH3	0.526	0.787
IV	TH		0.825
11	TH1	0.593	0.753
12	TH2	0.531	0.748
13	TH3	0.566	0.835
14	TH4	0.636	0.802
15	TH5	0.458	0.867

Source: Authors’ calculations, 2024.

).

In the process of approaching citizens to participate in the survey, there may have been some uncertainties. The level of public awareness is uneven. Citizens still face many challenges in accessing and contributing opinions for the implementation of smart city development due to inconsistent understanding (smart refers to the application of digital technology in management, operation, application, and implementation). Smart city development involves developing digital data infrastructure, digital technology, and digital transformation, which are essential measures that help “smarten” other technical and socio-economic infrastructures depending on the application of technology based on the approved general urban planning of Pleiku City.

4.2. Factors Influencing Smart Urban Construction

Instead of focusing on 15 individual factors that influence smart urban construction, this study examined four major groups of factors. Each of these large groups of factors exhibited correlations with small factors. This helps save time and funding when researching. The results in

Table 2. KMO and Bartlett’s test.

No.	Targets		Value
1	KMO		0.895
2	Bartlett’s Test	Approx. Chi-Square	2542.248
		Df	200
		Sig.	0.001

Source: Authors’ calculations, 2024.

show that the coefficient KMO = 0.895, so EFA is suitable for real data. Barlett’s test is valued at sig. = 0.001. As a result, we can see that the observed variables have a linear relationship with the representative factor, and thus the real data agree with the EFA analysis.

4.3. Factor Loading

Table 3. TVE and eigenvalues.

Factors	Eigenvalues			TVE
	Total	Variance (%)	Cumulative (%)	Total	Variance (%)	Cumulative (%)
1	8.4863	39.004	39.004	3.092	12.367	12.367
2	2.815	12.938	51.943	3.061	12.246	24.612
3	1.771	8.140	60.082	3.034	12.135	36.747
4	1.624	7.464	67.547	2.974	11.895	48.642
5	1.298	5.966	73.512
6	1.12	5.148	78.660
7	0.767	3.525	82.185
8	0.737	3.387	85.573
9	0.658	3.024	88.597
10	0.628	2.886	91.483
11	0.592	2.721	94.204
12	0.557	2.560	96.764
13	0.303	1.393	98.157
14	0.216	0.993	99.150
15	0.185	0.850	100.000

Source: Authors’ calculations, 2024.

displays the evaluation results for the level of explanation of the observed variables in the model with the outcome factor. The independent variable’s total variance explained (TVE) is 67,547, so the EFA analysis meets the requirements. The data reveal that the model’s factors (variables) account for 67.547% of the change in the outcome factor. To be more precise, the study’s observed variables accounted for 67.547% of the variance in the factors impacting smart urban construction in Pleiku.

Table 4. Rotated component matrix.

No.	Variable	Components
		1	2	3	4
1	TH1	0.805
2	TH3	0.728
3	TH2	0.697
4	TH5	0.696
5	TH4	0.688
6	NL3		0.846
7	NL2		0.807
8	NL4		0.788
9	NL1		0.780
10	CS2			0.715
11	CS1			0.692
12	CS3			0.672
13	QH1				0.783
14	QH3				0.741
15	QH2				0.722

Source: Authors’ calculations, 2024.

displays the results of the rotation matrix that determined the factor loading. The results reveal that the original order does not apply to the four groups of factors with 15 observed variables. Every variable’s loading factor has a value > 0.5. Therefore, we can confirm the correlation between each factor and its component, indicating the practical significance of EFA analysis.

Thus, EFA exploratory factor analysis introduces all four groups of factors into regression analysis.

4.4. Multivariate Regression Analysis Determines the Influence of Factors on Smart Urban Construction

Regarding the multivariate regression analysis, the coefficient Sig. = 0.00 is displayed in

Table 5. Linear regression analysis.

Groups of Factors	β	t	Multicollinearity Statistics		Influence Rate (%)	Order of Influence
			Sig.	VIF
Constant	−0.038	−0.221
CS	0.238	3.446	0.001	1.618	23.36	3
NL	0.283	50,175	0.000	1.383	27.77	2
QH	0.190	3.743	0.000	1.405	18.65	4
TH	0.308	6.014	0.000	1.261	30.23	1

Source: Authors’ calculations, 2024.

. As a result, the independent variables impact the dependent variable (Y), making the regression model relevant. There is no multicollinearity in the research model because the VIF of every variable is less than 2. In Table 5, the coefficient (β) exhibits a positive value, signifying the significance of factor groups in the study model. This demonstrates that there is a linear relationship between each of the four sets of characteristics and the development of smart urban zones in Pleiku City.

The shape of the regression equation was ascertained from the standardized regression coefficient: Y = −0.038 + 0.238 × CS + 0.283 × NL + 0.190 × QH + 0.308 × TH + Ei.

The factors of the standardized regression coefficient can be converted to percentage form and sorted in order of priority from high to low as follows. The TH factor group has the greatest influence on smart urban construction in Pleiku City with a rate of 30.23%. Next is the NL factor group at 27.77%, followed by the CS factor group at 23.36%. Finally, there is the group of QH factors with a contribution rate of 18.65%.

5. Discussion

Building smart urban areas in Pleiku City contributes to improving people’s quality of life and streamlines urban management. This construction also aims to protect the environment and improve competitiveness with other urban areas. It ensures that all people enjoy convenient public services, enhances security, and promotes social order and safety. However, some reports have shown that building smart urban areas still faces some difficulties in Vietnam in general and Pleiku City in particular [51,52]. Planning and implementation issues are worth mentioning. The legal corridor lacks clarity, especially in procedures related to investment in and hiring of information technology services. Because there is no connection standard, data processing is difficult. Moreover, urban management has not yet caught up with the latest trends in urban development. The absence of landscape architecture management regulations and strict urban environmental management has hindered the implementation of technical infrastructure and urban space articulation. Research shows that 15 main factors play an important role in building smart cities. We divide these factors into four groups based on their importance: implementation organization, human resources, policy mechanism, and planning. The authors will delve deeper into the effects of these factor groups below.

The first aspect to consider is the connection between implementing organizations and the construction of smart cities. This implementation brings together smart solutions based on factors such as smart management, living, economy, environment, and technology. The ability to deploy the combination of these factors in the city space is a complex form of transformation and requires a certain rigor [53]. When combined, these factors will contribute to sustainable economic development and improve operational efficiency and quality of life in urban areas [54,55]. They also help improve interactions between city governments, businesses, and citizens [56]. Effective resource management and use helps minimize impacts on environmental issues.

Second, the study also highlights the relationship between human resources and smart cities. Technology is not the only factor that creates a smart city. More important is the role of human capital. In other words, human resources and education play an important role in building and developing smart cities [57]. Research reveals that Vietnam’s urban management staff lacks proper training [58]. Furthermore, awareness of urban management content is not unified, so smart urban management requires management staff to have high capacity and skills [59]. Smart cities always prioritize the human factor [60]. Smart cities are not solely shaped by advanced technology, but also by the innovative ways in which people apply this technology. It takes time to train high-quality human resources who can master technology, especially leaders, and elevate the intellectual level of urban residents.

Thirdly, government support and governance policies serve as the foundation for the implementation of smart city initiatives [61]. To enable smart city initiatives, policy institutions need to be transparent and strategic. Smarter governments will do more than just control the products of social and economic structures [62]. For mechanisms to promote growth, innovation, and advancement, they must establish connections with enterprises, communities, and citizens [63]. This not only gives citizens access to knowledge about decisions that impact their lives, but it also helps to manage resources more effectively. In Vietnam today, smart urban policies mainly prioritize solving urgent problems without paying attention to long-term orientations [64]. This can reduce the efficiency and sustainability of smart urban projects. At the same time, it also affects residents’ quality of life and ability to adapt to climate change.

Fourth, in this study, planning is the factor with the lowest impact on smart urban construction in Pleiku, but it is indispensable. A smart city cannot develop with patchy and ineffective planning. However, this is a significant issue that Vietnamese urban areas are currently grappling with [65]. Reality shows that most localities are focusing heavily on smart urban services without paying attention to the development of urban technical infrastructure [66]. If the Vietnamese government solely concentrates on the development of smart urban services, it will only address the surface of the urban development issue, failing to address the underlying issues. Vietnam must thoroughly resolve basic urban problems like traffic, environment, energy, waste, and flooding to lay the groundwork for implementing smart urban construction.

6. Conclusions and Policy Implications

The goal of building a smart urban area in Pleiku City is to foster comprehensive development, collaborate with the flower industry, and modernize in a sustainable manner. This study aims to understand some factors affecting the construction of smart urban areas in Pleiku City. The authors classify these influencing factors into four groups: policy mechanisms, human resources, planning, and implementation organizations. The results of this research can help make smart urban construction projects in Vietnam more feasible. The study utilizes data from a survey conducted among households in Pleiku City using a random sampling method. Next, SPSS software processes the data and determines the regression equation for the influencing factors. The analysis of influencing factors using the linear regression function confirms the acceptance of all research hypotheses in the theoretical model, resulting in a regression equation of the following form:

Based on the standardized regression coefficient, the factor groups are arranged in order of priority from high to low as follows. The group of organizational and implementation factors has the greatest influence on smart urban construction in Pleiku City with a rate of 30.23%. The human resource factor group has a rate of 27.77%, and the policy mechanism group has a rate of 23.36%. Finally, there is the group of planning factors with a contribution rate of 18.65%.

Based on the research results and discussion, the authors propose a few suggestions to improve the quality of smart urban construction in Pleiku City. Firstly, in terms of human resources, local authorities need to train and foster management staff to meet the requirements of implementing smart urban construction plans. This training should be based on the concentration of resources in the city’s education sector. Furthermore, local authorities must strengthen the inspection and supervision of content implementation within the responsibilities of officials and civil servants related to smart urban construction. The second solution involves financial issues. It is necessary to ensure that capital from the city budget is available for investment in technology development. For technology infrastructure projects, the city should also deploy information technology services. Third is the policy mechanism solution. It is necessary to propose policy mechanisms for implementing smart education plans. Regulations on building connection standards are needed to guarantee the sharing and integration of data with the city’s overall data. The fourth solution is about propaganda. It is also necessary to promote propaganda and mobilization efforts, creating consensus in society so that all people support and share responsibility for investing in urban infrastructure. Recognizing the urgency and importance of building and developing Pleiku City into a smart urban area is also one of the goals of this solution. The final and crucial solution involves effective planning. The government must expedite the process of finalizing the adjustments and preparations for the general planning of Pleiku City, aiming to complete it by 2030, while maintaining a long-term vision for 2050. In addition, the government should complete the technical infrastructure system of the old urban center in an open manner, with the goal of developing new urban areas within the existing urban areas, as this could contribute to a large social impact in the Central Highlands.