An Integrated Analysis of Electric Battery Charging Station Selection—Thailand Inspired

Abstract

The growing adoption of electric vehicles (EVs) necessitates a well-distributed network of charging stations. However, selecting optimal locations for these stations is a complex issue influenced by geographic, demographic, technical, and economic factors. This study aims to fill the gaps in previous research by providing a comprehensive analysis of factors influencing the selection of EV battery charging stations. This research focuses on integrating geographic, demographic, technical, and infrastructure considerations to inform strategic placement decisions. A quantitative approach was employed, using questionnaires distributed to 300 entrepreneurs in Thailand’s EV charging station sector. The data were analyzed using descriptive statistics and structural equation modeling (SEM) to evaluate the relationships among the influencing factors. The results reveal that technical and infrastructure factors significantly impact economic and financial considerations, which in turn influence the selection of charging stations. Additionally, geographic and demographic factors play a crucial role in shaping economic outcomes and the strategic placement of these stations. A holistic approach that integrates these diverse factors is essential for the strategic deployment of EV charging infrastructure, which supports increased EV adoption and contributes to environmental sustainability.

1. Introduction

The proliferation of EVs is essential for reducing greenhouse gas emissions and achieving sustainable transportation goals globally. Central to the proliferation of EVs is the availability and efficiency of electric battery charging stations. The selection of suitable sites for these charging stations is a multifaceted problem, influenced by various factors that can be categorized into technical, infrastructure, economic, financial, geographic, and demographic considerations.

In Thailand, the expansion of EV infrastructure, particularly the establishment of charging stations, is gaining significant attention due to its potential to support the country’s environmental and economic objectives. The strategic placement and development of EV charging stations are critical for encouraging EV adoption, enhancing user convenience, and ensuring efficient energy use. The strategic selection of EV charging stations is of paramount importance for entrepreneurs in the rapidly evolving automotive and energy sectors. As the demand for electric vehicles continues to rise globally, the ability to provide convenient and accessible charging solutions becomes a crucial competitive advantage. For entrepreneurs, the decision-making process regarding the location and infrastructure of charging stations involves a complex interplay of technical, economic, and market-driven factors. Effective site selection strategies can significantly enhance the profitability and sustainability of EV charging businesses. Research indicates that the optimal placement of charging stations not only improves operational efficiency but also maximizes user satisfaction and increases the adoption rates of electric vehicles [1]. Entrepreneurs must consider various criteria, such as proximity to high-traffic areas, availability of existing electrical infrastructure, and potential for future expansion, to ensure long-term viability and growth.

Research has demonstrated that the availability of a robust network of charging stations can significantly impact the adoption rate of electric vehicles [1]. In Thailand, the government’s commitment to promoting EVs through policy support and financial incentives has created a favorable environment for expanding charging infrastructure [2]. The integration of these stations into the national grid not only facilitates the transition to cleaner energy sources but also promotes economic benefits by reducing dependence on imported fuels and fostering technological innovation within the automotive industry. Additionally, the geographic and demographic characteristics of Thailand present unique challenges and opportunities for the deployment of EV charging stations. Urban areas with high population density, such as Bangkok, show a higher potential for EV usage due to shorter travel distances and greater environmental awareness among residents [3]. Conversely, rural areas require strategic planning to ensure accessibility and address infrastructure limitations.

Moreover, the financial aspect of deploying EV charging stations is critical. Initial investment costs, operational expenses, and potential revenue streams must be meticulously analyzed. Entrepreneurs can leverage financial incentives, subsidies, and partnerships to offset costs and enhance profitability [2]. Understanding the economic landscape and identifying strategic locations that offer the highest return on investment is essential for entrepreneurial success in this field. Additionally, market analysis and consumer behavior studies play a vital role in shaping the strategy for EV charging station selection. Demographic trends, including urbanization rates and the propensity of local populations to adopt green technologies, inform the decision-making process [3]. Entrepreneurs who align their strategies with these insights are better positioned to meet consumer demand and capitalize on emerging market opportunities. The strategic selection of electric vehicle charging stations is a critical factor for entrepreneurs aiming to succeed in the EV infrastructure industry. By integrating technical, economic, and market considerations into their site selection strategies, entrepreneurs can enhance operational efficiency, improve user satisfaction, and achieve sustainable growth.

Previous research on the selection of EV battery charging stations has explored various factors influencing optimal site placement. This body of work examines multiple dimensions, including technical specifications, infrastructure requirements, economic and financial considerations, and geographic and demographic influences. Scholars have investigated the availability of electrical infrastructure, cost implications, and potential returns on investment, as well as the spatial distribution of users and demographic characteristics that affect charging station demand. This extensive research provides a foundational understanding that informs the development of comprehensive frameworks for strategic and efficient deployment of EV charging infrastructure.

This study addresses the gaps identified in prior research by providing a detailed analysis of the factors influencing the selection of electric battery charging stations. The conceptual framework of this research comprehensively integrates technical and infrastructure considerations, economic and financial aspects, and geographic and demographic factors. By examining these dimensions, this research aims to enhance the understanding of how these variables interact and influence the decision-making process, thereby filling a significant gap in the existing literature.

In the context of the literature review focused on the selection of electric vehicle (EV) charging stations, it is noted that much of the existing research has been concentrated in Thailand, highlighting the country’s unique geographic and demographic considerations as well as its technical and infrastructure challenges. However, there is an opportunity to expand the scope of this research to encompass various other locations. This expansion could provide a more comprehensive understanding of how different regions with varying geographic, demographic, and infrastructure profiles influence the decision-making process for EV charging station placement. By broadening the geographic focus, the research could identify universal trends as well as location-specific factors, ultimately leading to a more robust and adaptable framework for the strategic deployment of EV charging infrastructure across diverse settings.

This adjustment in research focus would align with the growing global interest in electric vehicles and the need for a well-distributed network of charging stations to support this transition. Therefore, it would be valuable to extend the analysis beyond Thailand to include countries with different levels of EV adoption, varied infrastructure capabilities, and distinct geographic and demographic characteristics.

2. Literature Review

A literature review is a structured analysis of existing research, concentrating on the interconnections between related variables.

This study employed site selection theory as a framework for developing its research concept. The theory of site selection asserts that the ideal placement of a facility is influenced by various factors such as cost-effectiveness, accessibility, available resources, and market reach. This theoretical framework combines geographical, economic, and strategic elements to determine locations that enhance operational efficiency and profitability.

Site selection theory

Site selection theory employs a comprehensive strategy to determine the best location for diverse applications, including data centers, well construction, real estate development, and retail store placement. It considers factors such as weather conditions, geopolitical risks, power supply access, community involvement, and the utilization of advanced technologies like remote sensing and GIS techniques. Methods can involve examining customer demographics, geographic coordinates, shortest path calculations, and site selection formulas to improve accuracy and efficiency in decision-making. The theory highlights the necessity for comprehending both business and community needs, ensuring sustainability and success by integrating multiple criteria and stakeholder perspectives in the site selection process [4].

2.1. Research Hypotheses

2.1.1. Technical and Infrastructure and Economic and Financial Factors

The choice of electric vehicle (EV) charging locations is heavily impacted by technical factors and the availability of infrastructure, which are essential in assessing the feasibility and sustainability of these sites [5]. The technical and infrastructure aspects encompass the physical and technological requirements necessary for establishing and maintaining charging stations. These factors include the availability of electrical infrastructure, the proximity to power grids, and technical compatibility with various EV models. Ensuring robust infrastructure is crucial for the reliability and efficiency of charging stations, thereby affecting user convenience and overall satisfaction. Economic and financial considerations play a pivotal role in the deployment of EV charging stations. These factors include the cost of land, installation, and maintenance of charging stations, as well as the potential return on investment. Financial incentives, subsidies, and partnerships with private sector entities can significantly impact the economic feasibility of charging stations. Understanding these economic dynamics is essential for creating a sustainable and financially viable charging infrastructure. Elements such as the incorporation of battery energy storage systems (BESS) and photovoltaic (PV) systems can increase the economic feasibility of EV charging stations by lowering grid connection expenses and boosting sustainability [6]. Integrating technical solutions with financial evaluations is essential for creating an efficient and cost-effective EV charging infrastructure. This approach promotes the adoption of electric mobility and reduces environmental impact [7].

Hypothesis 1.

Technical and infrastructure factors have a positive and significant impact on economic and financial factors.

2.1.2. Geographic and Demographic as Well as Economic and Financial Factors

The deployment of EV charging stations is a critical component in the transition towards sustainable transportation. The decision-making process for the placement and selection of these stations is significantly influenced by geographic and demographic factors. Understanding these influences is essential for optimizing economic and financial outcomes. Choosing locations for EV charging stations requires important geographical and economic considerations. Key factors include power availability, solar energy potential, installation costs, and proximity to roads and population centers, all of which are critical in identifying the best sites for EV charging stations [8]. Geographic and demographic factors are critical in determining the optimal placement of charging stations to maximize accessibility and usage. Geographic considerations involve the spatial distribution of potential users, proximity to highways and urban centers, and environmental impact assessments. Demographic factors include population density, income levels, and the propensity of residents to adopt EV technology. Analyzing these variables helps in identifying locations where charging stations will be most needed and effectively utilized. Moreover, the economic dimension plays a crucial role, with economic considerations identified as the primary factor, followed by technical factors, in determining the optimal location for charging stations [5]. By integrating geographic factors, such as proximity to roads, and economic factors like construction and operational expenses, decision-makers can strategically plan the deployment of EV charging stations to improve accessibility and sustainability [9]. Geographic and demographic variables play a crucial role in the economic and financial decisions regarding the placement of EV charging stations. Factors such as urban density, income levels, and demographic compositions significantly influence the demand and sustainability of charging infrastructure. For optimal deployment, policymakers and stakeholders must meticulously evaluate these elements to ensure both economic viability and the broad adoption of EVs.

Hypothesis 2.

Geographic and demographic factors have a positive and significant impact on economic and financial factors.

2.1.3. Technical and Infrastructure and Geographic and Demographic Factors

Technical and infrastructural advancements have markedly impacted geographic and demographic trends. Research indicates that developments in infrastructure, especially within transportation, energy, and telecommunications, are pivotal in determining population distribution and density [10]. Advancements in charging technology and infrastructure have substantially influenced the placement and distribution of EV stations. The evolution of electric vehicles has heightened the demand for an effective charging infrastructure [11]. Public fast chargers have become crucial for minimizing wait times and accommodating the transient population, necessitating the design of electrical distribution systems that can handle varying load demands based on the battery state of charge (SoC). Research has concentrated on determining the optimal placement of EV charging stations along highways by analyzing traffic flows and demand forecasts to ascertain the appropriate number and size of charging stations required [12]. Furthermore, integrating renewable energy sources and implementing energy management strategies at charging stations have been suggested to reduce grid stress and peak power demands, thereby improving the overall efficiency and sustainability of EV charging networks [13]. Additionally, the increased load from EV charging stations significantly impacts power distribution system parameters, such as voltage and current, highlighting the need for well-planned infrastructure to ensure efficient operation and mitigate adverse effects on the distribution network [14]. Moreover, demographic factors such as environmental awareness and the educational background of the population can significantly impact the acceptance and success of technical solutions. Regions with high levels of environmental consciousness are more likely to endorse and utilize sustainable charging solutions, thereby ensuring that technical strategies align with demographic preferences [15]. The deployment of electric vehicle (EV) charging stations is heavily influenced by the interaction between technical and infrastructure factors and geographic and demographic variables. Urban areas with high income levels, supportive demographics, and robust infrastructure are more capable of integrating advanced charging technologies, whereas rural regions necessitate innovative solutions to address their distinct challenges. Recognizing this relationship is crucial for stakeholders to devise effective strategies for the widespread adoption of EVs.

Hypothesis 3.

Technical and infrastructure factors have a positive and significant impact on geographic and demographic factors.

2.1.4. Technical, Infrastructure, and Electric Battery Charging Station Selection Factors

The selection of EV charging stations is significantly influenced by technical and infrastructure factors. These aspects are vital for ensuring that charging stations are efficient, reliable, and able to meet the increasing demand for EVs. Technical factors include the type and capacity of charging technology, such as Level 1, Level 2, or DC fast chargers. The selection of technology is determined by the intended use case, whether for residential areas, commercial districts, or highway locations. For example, DC fast chargers are ideal for highway rest stops because they can rapidly charge vehicles, which is crucial for long-distance travel [16]. The relationship between technical and infrastructure factors is mutually dependent. High-capacity chargers necessitate substantial electrical infrastructure support, making regions with advanced grid capabilities and supportive infrastructure more likely to adopt high-tech charging solutions. Conversely, areas with limited grid capacity may need to depend on lower-capacity chargers or innovative solutions such as solar-powered charging stations [17]. The choice of electric battery charging stations is shaped by a range of technical and infrastructure factors. Technically, the need for fast chargers in public areas is essential to accommodate transient populations and reduce consumer wait times [18]. Infrastructure considerations include strategically placing charging stations to minimize adverse effects on the distribution network, and considering parameters such as voltage stability and power loss [11]. Additionally, determining the optimal location for charging stations is critical, with factors such as state of charge per second and road altitude in mountainous regions being key to providing efficient service along steep gradients [5]. Economic and technical feasibility, alongside the use of optimization technologies for effective energy management, are also pivotal in the sustainable development of charging infrastructure. The selection of EV charging stations is deeply impacted by technical and infrastructure considerations. Urban areas with high demand require high-capacity grids and advanced charging technologies, whereas regions with less robust infrastructure may need innovative solutions. Grasping the interaction between these factors is essential for creating effective and efficient EV charging networks.

Hypothesis 4.

Technical and infrastructure factors have a positive and significant impact on electric battery charging station selection.

2.1.5. Geographic, Demographic, and Electric Battery Charging Station Selection Factors

The selection of areas for establishing EV charging stations is heavily influenced by geographic and demographic factors, as confirmed by numerous studies [8]. This has been tested using a multi-criteria decision-making approach, integrated with geographic information systems (GIS) and spatial analysis techniques to assist in selecting suitable locations for electric vehicle charging stations. Additionally, a method has been proposed that leverages graph convolutional networks to select battery charging stations based on traffic density and investment costs, emphasizing the importance of demographic data in the decision-making process [19]. Geographic and demographic factors are vital in determining the placement of electric battery charging stations. The research underscores the importance of proximity to densely populated areas, the current density of charging stations, and socioeconomic disparities in access to charging infrastructure [20]. For example, studies highlight the strategic location of charging stations near high-demand areas such as shopping malls and universities to alleviate “range anxiety” among electric vehicle users [21]. Furthermore, machine learning frameworks have been developed to predict future charging station density based on socioeconomic factors, ensuring equitable access and identifying underserved communities for targeted infrastructure investments. These insights emphasize the need to consider both geographic and demographic factors to optimize the placement of electric battery charging stations and encourage the widespread adoption of electric vehicles [22]. A study utilizing a GIS-based AHP method was conducted to select suitable locations for battery charging stations in Shenzhen, China, considering social, technological, and environmental factors. The study found that areas with high population density and existing charging stations are suitable for establishing new charging stations [23].

Hypothesis 5.

Geographic and demographic factors have a positive and significant impact on electric battery charging station selection.

2.1.6. Economic, Financial, and Electric Battery Charging Station Selection Factors

The decision to select the location for an electric vehicle battery charging station is influenced by numerous economic and financial factors. These factors include cost efficiency, financial conditions, operational considerations, and the behavior of electric vehicle users. Careful consideration of battery prices, financial conditions, and transportation costs is essential when selecting a battery charging station provider [24]. The connection between economic and financial considerations and the selection of electric battery charging stations is examined through several studies. A methodology for selecting sustainable battery suppliers for EV battery swapping stations is to employ fuzzy multi-criteria decision-making. This approach highlights the importance of battery price and financial condition as the essential criteria [25]. Economic and financial considerations are pivotal in determining the sustainability and optimal placement of electric battery charging stations. Research identifies economic factors as the most critical aspect in selecting ideal locations for these stations, with technical factors being a close second [5]. The overall cost of charging stations, encompassing land, installation, and energy demand costs from electric vehicles, presents a significant constraint that must be minimized for optimal placement [11]. Additionally, studies reveal that investors make strategic decisions regarding station capacities, locations, charging unit power outputs, and fees based on anticipated profits. These decisions consider building and operational costs, underscoring the complex balance between profitability and cost-effectiveness within the charging station network [26]. Comprehending the interplay between economic and financial determinants and the selection of electric battery charging stations is pivotal for the successful adoption and efficient implementation of EV infrastructure. This synthesis elucidates principal insights from various studies on how these factors impact the selection of EV charging stations. Qin, Qiu, et al. [27] introduced a method based on graph convolutional networks to optimize the placement of charging stations, considering traffic flow and investment costs. This approach aims to improve the spatial efficiency of charging networks [27]. Studies have been conducted utilizing economic and environmental factors with quantum genetic algorithms to aid in reducing greenhouse gas emissions and operating costs when selecting locations for battery charging stations [28]. Investigating the optimal number of battery charging stations and pricing strategies, which are shaped by customer needs, will impact investment decisions and the selection of charging station locations [29]. These studies collectively demonstrate the intricate interaction of economic, financial, and operational factors in the strategic selection and placement of electric vehicle battery charging stations.

Hypothesis 6.

Economic and financial factors have a positive and significant impact on electric battery charging station selection.

2.1.7. Technical and Infrastructure Factors and Electric Battery Charging Station Selection, with Economic and Financial Factors as Mediators

Numerous studies have examined how technical aspects and infrastructure impact the decision-making process for selecting locations for electric vehicle battery charging stations, with economic and financial factors serving as mediator variables. One study explored technological research on solar-powered charging stations in developing countries, emphasizing the feasibility and profitability of various battery formats and advocating for investment in sustainable charging infrastructure [30]. The Block EV proposal, when applied to specific service stations, includes the monitoring and control of EV service stations and their control systems. This comprehensive control system emphasizes safety and service quality [31]. A report was presented on the rapid feasibility and technology of concentrated gas stations, focusing on cost reduction through battery energy storage and photovoltaic electricity production systems [6]. A review of the situation in India also explored the potential for direct-drive EVs, emphasizing market regulation and the controls governing executive operations [32]. A research study on the use of secondary batteries in EV charging stations identified the optimal balance between cost and performance to ensure system efficiency [33]. The conclusion established that technical and infrastructure factors are related to the selection of electric battery charging stations, with economic and financial factors acting as mediators.

Hypothesis 7.

Technical and infrastructure factors have a positive and significant impact on electric battery charging station selection, with economic and financial factors as mediators.

2.1.8. Technical and Infrastructure Factors and Electric Battery Charging Station Selection, with Geographic and Demographic Factors as Mediators

The density and accessibility of charging stations are critical factors influencing the decision-making process of electric vehicle users. A high concentration of charging stations and their convenient locations significantly enhance the overall convenience for electric vehicle users [34]. The ability of charging stations to provide rapid charging and the incorporation of modern technology significantly impact the preferences of electric vehicle users when selecting a charging station. Fast-charging capabilities are a crucial requirement, particularly for urban residents who prioritize time efficiency [35]. The availability and proper maintenance of charging stations greatly influence the satisfaction of electric vehicle users. Frequent malfunctions and poor maintenance of charging stations deter electric vehicle users from choosing to charge their batteries at those locations [36]. The distribution of battery charging stations varies geographically, with urban areas having a higher concentration of stations compared to rural regions to better serve electric vehicle users. Cities typically have a significantly greater number of charging stations [37]. Government policies and incentives for electric vehicle use, such as subsidies or tax breaks, significantly influence the placement of electric vehicle charging stations in areas benefiting from these policies. Consequently, regions supported by government initiatives will see a substantial increase in the number of electric vehicle charging stations [38]. The choice of electric battery charging stations is shaped by a complex interplay of technical, infrastructural, geographic, and demographic factors. Geographic and demographic variables significantly mediate EV users’ preferences and behaviors. Future research and policy initiatives should concentrate on addressing these mediating factors to improve the effectiveness of charging infrastructure deployment and promote wider EV adoption.

Hypothesis 8.

Technical and infrastructure factors have a positive and significant impact on electric battery charging station selection, with geographic and demographic factors as mediators.

After examining the literature on the selection of locations for electric vehicle battery charging stations, a conceptual framework was developed based on the interrelationships among the identified variables. Correspondingly, hypotheses were formulated in alignment with this framework, as illustrated in Figure 1. The structural equation model identifies the exogenous variable as technical and infrastructure factors, encompassing observable variables such as electric grid capacity, existing charging infrastructure, types of chargers required, and technological innovation. In the endogenous section, there are three latent variables. The first latent variable, geographic and demographic factors, includes observable variables such as population density, traffic patterns, proximity to amenities, and residential and commercial areas. The second latent variable, economic and financial factors, encompasses observable variables like installation and maintenance costs, funding and incentives, revenue potential, market demand, and adoption rates. The third latent variable is electric battery charging station selection, with observed variables including convenience, cost-effectiveness, technology compatibility, and environmental impacts.

The fundamental mathematical structure for the latent variables is detailed in Equations (1) and (2).

Structural Equation for Path Coefficient

$$\eta =\beta \eta +\mathit{\Gamma }\xi +\zeta$$

(1)

$$\left[\begin{array}{c}{\mathsf{\eta }}_{EF}\\ {\mathsf{\eta }}_{GD}\\ {\mathsf{\eta }}_{ES}\end{array}\right]=\left[\begin{array}{ccc}0& {\mathsf{\beta }}_1& 0\\ 0& 0& 0\\ {\mathsf{\beta }}_2& {\mathsf{\beta }}_3& 0\end{array}\right]\left[\begin{array}{c}{\mathsf{\eta }}_{EF}\\ {\mathsf{\eta }}_{GD}\\ {\mathsf{\eta }}_{ES}\end{array}\right]+\left[\begin{array}{c}{\mathsf{\gamma }}_1\\ {\mathsf{\gamma }}_2\\ {\mathsf{\gamma }}_3\end{array}\right]\left[\begin{array}{c}{\mathsf{\xi }}_{TI}\end{array}\right]+\left[\begin{array}{c}{\mathsf{\zeta }}_1\\ {\mathsf{\zeta }}_2\\ {\mathsf{\zeta }}_3\end{array}\right]$$

(2)

• η (eta) is a (m × 1) column vector of m endogenous variables.
• ξ (xi) is a (n × 1) column vector of n exogenous variables.
• β (beta) is a matrix (m × m) of coefficients associated with the direct effects of an endogenous variable on another endogenous variable.
• Γ (gamma) (in a path coefficient figure, we use γ) is a matrix.
• (m × n) of coefficients are associated with the direct effects of an exogenous variable on another endogenous variable.
• ζ (zeta) is a (m × 1) column vector of error terms associated with endogenous variables.

The relationships between latent variables in the structural equation model can be analyzed using different sets of equations. Specifically, Equations (3) and (4) are utilized to assess the relationships among endogenous variables, while Equations (5) and (6) are applied to evaluate the relationships among exogenous variables.

Structural Equation for Endogenous Variables

$$y={\mathit{\Lambda }}_y\eta +\epsilon$$

(3)

$$\left[\begin{array}{c}{\mathrm{y}}_{EF1}\\ {\mathrm{y}}_{EF2}\\ {\mathrm{y}}_{EF3}\\ {\mathrm{y}}_{EF4}\\ {\mathrm{y}}_{GD1}\\ {\mathrm{y}}_{GD2}\\ {\mathrm{y}}_{GD3}\\ {\mathrm{y}}_{GD4}\\ {\mathrm{y}}_{ES1}\\ {\mathrm{y}}_{ES2}\\ {\mathrm{y}}_{ES3}\\ {\mathrm{y}}_{ES4}\end{array}\right]=\left[\begin{array}{ccc}{\lambda }_{EF1}^y& 0& 0\\ {\lambda }_{EF2}^y& 0& 0\\ {\lambda }_{EF3}^y& 0& 0\\ {\lambda }_{EF4}^y& 0& 0\\ 0& {\lambda }_{GD1}^y& 0\\ 0& {\lambda }_{GD2}^y& 0\\ 0& {\lambda }_{GD3}^y& 0\\ 0& {\lambda }_{GD4}^y& 0\\ 0& 0& {\lambda }_{ES1}^y\\ 0& 0& {\lambda }_{ES2}^y\\ 0& 0& {\lambda }_{ES3}^y\\ 0& 0& {\lambda }_{ES4}^y\end{array}\right]\left[\begin{array}{c}{\mathsf{\eta }}_{EF}\\ {\mathsf{\eta }}_{GD}\\ {\mathsf{\eta }}_{ES}\end{array}\right]+\left[\begin{array}{c}{\mathit{\epsilon }}_{EF1}\\ {\mathit{\epsilon }}_{EF2}\\ {\mathit{\epsilon }}_{EF3}\\ {\mathit{\epsilon }}_{EF4}\\ {\mathit{\epsilon }}_{GD1}\\ {\mathit{\epsilon }}_{GD2}\\ {\mathit{\epsilon }}_{GD3}\\ {\mathit{\epsilon }}_{GD4}\\ {\mathit{\epsilon }}_{ES1}\\ {\mathit{\epsilon }}_{ES2}\\ {\mathit{\epsilon }}_{ES3}\\ {\mathit{\epsilon }}_{ES4}\end{array}\right]$$

(4)

y is a (p × 1) column vector of p measured endogenous variables.

Λy (lambda of y) is a (p × m) structural coefficient matrix for the effects of the latent endogenous variables on the observed variables.

η (eta) is a (m × 1) column vector of m endogenous variables.

ε (epsilon) is a (p × 1) column p-vector related to errors of the observed endogenous variables.

Structural Equation for Exogenous Variables

$$x={\mathit{\Lambda }}_x\xi +\delta$$

(5)

$$\left[\begin{array}{c}{\mathrm{x}}_{TI1}\\ {\mathrm{x}}_{TI2}\\ {\mathrm{x}}_{TI3}\\ {\mathrm{x}}_{TI4}\end{array}\right]=\left[\begin{array}{c}{\lambda }_{TI1}^x\\ {\lambda }_{TI2}^x\\ {\lambda }_{TI3}^x\\ {\lambda }_{TI4}^x\end{array}\right]\left[\begin{array}{c}{\mathsf{\xi }}_{TI}\end{array}\right]+\left[\begin{array}{c}{\mathit{\delta }}_{TI1}\\ {\mathit{\delta }}_{TI2}\\ {\mathit{\delta }}_{TI3}\\ {\mathit{\delta }}_{TI4}\end{array}\right]$$

(6)

• x is a (q × 1) column vector of q measured endogenous variables.
• Λ_x (lambda of y) is a (q × n) structural coefficient matrix for the effects of the latent exogenous variables on the observed variables.
• ξ (xi) is a (n × 1) column vector of n exogenous variables.
• δ (delta) is a (q × 1) column q-vector related to errors of the observed exogenous variables.

3. Research Methodology

3.1. Research Design

This exploratory research starts with a review of the literature concerning the selection of locations for electric vehicle battery charging stations by operators and the latent variables influencing this decision. The research aims to identify relationships among various factors influencing the choice of locations for electric vehicle battery charging stations. It examines the interplay between technical and infrastructure, geographic and demographic, and economic and financial variables, and how these factors affect the selection of battery charging station locations. Additionally, this study investigates the impact of these variables on the decision-making process for establishing battery charging stations.

3.2. Data Collection Process

This research employs a quantitative approach, the quantitative approach in this context refers to a research methodology that relies on the systematic collection and analysis of numerical data. This approach often involves the use of structured questionnaires, which are designed to gather measurable data from respondents. By employing this method, researchers can statistically analyze the responses to identify patterns, relationships, and trends within the data, thereby drawing conclusions that are both objective and generalizable to a larger population. This approach is particularly useful in studies aiming to quantify variables and determine the strength of associations among them. The questionnaire is divided into two main sections. The first section addresses the demographics of entrepreneurs operating battery charging station businesses. The second section focuses on identifying relationships between technical and infrastructure variables, geographic and demographic variables, and economic and financial variables, and how these factors influence the selection of battery charging station locations. The questionnaire employed a Likert scale to gauge entrepreneurs’ sentiments, with responses ranging from 1 (strongly disagree) to 5 (strongly agree). The researcher collected the completed questionnaires and conducted statistical analysis to interpret the research findings and survey results.

This study aims to identify the factors that influence operators’ decisions in selecting locations for establishing electric vehicle battery charging stations in Thailand for two main reasons. The first reason is the significant rise in demand for electric cars in Thailand over the past two years. The second reason is the corresponding increase in the need for electric car battery charging stations, driven by the growing number of electric vehicles. The survey sample is a sample of entrepreneurs who operate a business in the field of electric vehicle battery charging stations. The sampling was random to be used as a representative for the study. Due to the lack of clear records on the number of operators providing electric vehicle battery charging stations, the researcher utilized W.G. Cochran’s formula to determine the sample population required for random selection. A total of 300 samples were utilized for statistical analysis, ensuring a 95% confidence level.

3.3. Data Analysis

The data-cleaning process is a critical step in the analysis, aimed at ensuring the accuracy and validity of the results. For this study, the data-cleaning process involved several methodical steps to identify and rectify any discrepancies in the dataset before conducting statistical analysis. Initially, the data were subjected to a thorough inspection for missing values, which were addressed using imputation techniques where appropriate. Outliers were then identified using the Mahalanobis Distance method in SPSS Version 26, which is particularly effective in multivariate settings. The threshold for identifying outliers was set at a p-value of less than 0.001. The outliers were carefully examined to determine whether they represented genuine data points or errors, leading to the exclusion of three out of the original three hundred samples. This meticulous approach ensured that the remaining 297 samples were suitable for subsequent analyses, providing a robust foundation for this study’s conclusions.

Following the removal of outliers, the dataset was tested for construct validity and reliability. Factor loading values greater than 0.4 were considered acceptable, ensuring that the constructs measured were valid. The reliability of the questionnaire was assessed using Cronbach’s Alpha, with values exceeding 0.7, confirming the internal consistency of the constructs. This rigorous data-cleaning process was essential to minimize biases and inaccuracies, thereby enhancing the reliability and validity of the findings derived from the structural equation modeling (SEM) analysis conducted in subsequent sections.

4. Results

4.1. Demographic Data

Table 1. Demographic profile.

Items	Details	Frequency	Percentage
Gender	Male	102	34.3
	Female	195	65.6
Age	30–39 years	54	18.1
	40–49 years	123	41.4
	More than 49 years	120	40.4
Education level	Bachelor’s degree	247	83.1
	Master’s degree	34	11.4
	Higher than Master’s degree	16	5.3
Battery charging station	Doing business	283	95.2
	Expected to expand charging station	14	4.7

presents the characteristics of the business operators of electric vehicle battery charging stations who responded to the questionnaire. Based on the respondents’ characteristics, 34.3% were male and 65.6% were female. The age distribution of the sample revealed that 18.1% were aged 30–39 years, 41.4% were aged 40–49 years, and 40.4% were over 49 years old. The educational level of the sample indicated that 83.1% held a bachelor’s degree, 11.4% had a master’s degree, and 5.3% possessed a degree higher than a master’s. Regarding the battery charging station business, it was found that 95.2% are currently operating a battery charging station business, while 4.7% are considering expanding into this sector.

4.2. Validity and Reliability Results

A convergent validity test evaluates whether the observable variables accurately reflect the underlying component, ensuring the appropriateness of this component in each context. Cronbach’s Alpha is a metric utilized to assess the reliability of a questionnaire employed for data collection from a sample. Cronbach’s Alpha is used because it provides a measure of internal consistency, which is crucial in determining the reliability of a questionnaire or survey instrument. Additionally, the researcher employed confirmatory factor analysis to assess convergent validity, aiming to evaluate behaviors and outcomes from multiple perspectives in alignment with the research objectives and to test consistency with the theoretical framework used. Therefore, the researcher employed Cronbach’s Alpha, composite reliability (CR), and average variance extracted (AVE) as tools to assess the reliability of this study and to evaluate the sample group’s opinions.

“AVE” refers to “Average Variance Extracted”. This is a measure used in structural equation modeling (SEM) to assess the amount of variance that a construct captures from its indicators relative to the amount of variance due to measurement error. In simpler terms, it helps to determine how much of the observed variance in the data is explained by the underlying latent variable as opposed to noise or error.

To clarify, the “AVE” is computed as the average of the squared factor loadings of each construct and is used to establish the convergent validity of a construct. A commonly accepted threshold for AVE is 0.5 or above, indicating that more than half of the variance of the indicators is captured by the construct.

Additionally, Cronbach’s Alpha is used to test the structural reliability of each variable within the equation. The equation’s reliability must exceed 0.7 to demonstrate the relationship between the latent variables. In this study, Cronbach’s Alpha values range from 0.82 to 0.84, surpassing the standard threshold of 0.7. This indicates that the equations are reliable and can be used to test the relationships between the latent variables (

Table 2. Convergent validity.

Construct	Variables	Factor Loading	CR	AVE	Cronbach’s Alpha
Technical and Infrastructure	TI3	0.758	0.83	0.56	0.82
	TI2	0.720
	TI4	0.732
	TI1	0.781
Geographic and Demographic	GD4	0.761	0.82	0.54	0.84
	GD2	0.780
	GD3	0.674
	GD1	0.722
Economic and Financial	EF4	0.714	0.84	0.57	0.84
	EF2	0.761
	EF3	0.784
	EF1	0.763
Electric battery charging station selection	EBS4	0.793	0.84	0.60	0.84
	EBS3	0.784
	EBS1	0.809
	EBS2	0.720

Technical and Infrastructure: TI1 = Electric grid capacity, TI2 = Existing charging infrastructure, TI3 = Types of chargers needed, TI4 = Technology innovations. Geographic and Demographic: GD1 = Population density, GD2 = Traffic patterns, GD3 = Proximity to amenities, GD4 = Residential and commercial areas. Economic and Financial: EF1 = Cost of installation and maintenance, EF2 = Funding and incentives, EF3 = Revenue potential, EF4 = Market demand and adoption rates. Electric battery charging station selection: ES1 = Convenance, ES2 = Cost effectiveness, ES3 = Technological compatibility, ES4 = Environmental impacts.

).

4.3. Testing the Validity of Variables

To ensure the tool’s suitability for measuring equation consistency according to the theory of validity, the CR value should exceed 0.7 [39]. Additionally, the AVE value must be higher than 0.5 for it to be considered appropriate. In this study, the CR values range from 0.82 to 0.84, all exceeding the 0.7 threshold, indicating that the equations are consistent. This study’s AVE values ranged from 0.56 to 0.60, all above the 0.5 threshold. The study results indicated that the CR and AVE values exceeded the specified standards, demonstrating that the variables used in this research met the structural criteria.

The reliability of each latent variable was assessed, revealing that the reliability coefficients for technical and infrastructure, geographic and demographic, economic and financial, and electric battery charging station selection factors were 0.82, 0.84, 0.84, and 0.84, respectively. The reliability study results indicated that the values of the latent variables in the structural equations are interconnected.

4.4. Structural Equation Model Analysis (SEM) Results

SEM was chosen for this study due to its ability to assess both the direct and indirect effects between variables, allowing for a more comprehensive understanding of the causal relationships involved in the decision-making process for EV charging station selection. Unlike traditional regression analysis, which can only handle observed variables, SEM enables the inclusion of latent variables, which are critical for modeling constructs such as “Technical & Infrastructure”, “Geographic & Demographic”, and “Economic & Financial” factors. This makes SEM an ideal method for this study, where the interplay between these constructs significantly impacts the outcome variable, i.e., the selection of EV battery charging stations.

Moreover, SEM’s ability to test hypotheses simultaneously and its incorporation of measurement errors into the model enhance the reliability and validity of the findings. This approach allows researchers to evaluate the structural model fit, ensuring that the proposed relationships between variables are supported by the data. The use of SEM in this context aligns with the best practices in structural analysis, as recommended by experts in the field, including Ref. [40], who emphasized the technique’s robustness in handling complex models in marketing and management research.

Path analysis was employed to analyze the influence of technical and infrastructure (TI), geographic and demographic (GD), and economic and financial (EF) variables on the selection of electric battery charging stations (EBS) and to test the hypotheses.

Structural equation modeling (SEM) is utilized for hypothesis testing, particularly in examining the structure of electric battery charging station selection. SEM is commonly employed for this purpose, despite the challenges associated with indirectly measuring relationships. Quantitative research remains essential when applying SEM equations. Figure 2 depicts the relationships among technical and infrastructure (IT), economic and financial (EF), geographic and demographic (GD), and electric battery charging station selection (EBS) factors. The research findings show R² values of 0.467 for IT, 0.375 for EF, 0.384 for GD, and 0.384 for EBS, with the model’s overall R² value being a substantial 0.617 [41].

The p-values for the hypotheses were significant at p < 0.05 (Figure 2). According to Cohen (1988), the R² values represent a significant effect size. The research model demonstrates a good fit based on the following fit indices: Chi-Square = 103.501, df = 86, Relative Chi-Square = 1.204, p-value = 0.096, RMSEA = 0.026, RMR = 0.012, GFI = 0.960, NFI = 0.955, TLI = 0.989, and CFI = 0.992. Consequently, hypotheses H1, H2, H3, H4, H5, and H6 were all supported, indicating that technical and infrastructure (TI), economic and financial (EF), and geographic and demographic (GD) factors have a positive and significant effect on electric battery charging station selection (EBS).

The results of the path coefficient analysis can be represented by mathematical structural equations, as shown in Equations (7)–(10). Additionally, the structural equation for endogenous variables is depicted in Equation (12), while Equation (14) represents the structural equation for exogenous variables.

Structural Equation for Path Coefficient

$$\eta =\beta \eta +\mathit{\Gamma }\xi +\zeta$$

(7)

$$\left[\begin{array}{c}{\mathsf{\eta }}_{EF}\\ {\mathsf{\eta }}_{GD}\\ {\mathsf{\eta }}_{ES}\end{array}\right]=\left[\begin{array}{ccc}0& 0.417& 0\\ 0& 0& 0\\ 0.160& 0.306& 0\end{array}\right]\left[\begin{array}{c}{\mathsf{\eta }}_{EF}\\ {\mathsf{\eta }}_{GD}\\ {\mathsf{\eta }}_{ES}\end{array}\right]+\left[\begin{array}{c}0.247\\ 0.683\\ 0.249\end{array}\right]\left[\begin{array}{c}{\mathsf{\xi }}_{TI}\end{array}\right]+\left[\begin{array}{c}0.625\\ 0.533\\ 0.616\end{array}\right]$$

(8)

$${\mathsf{\eta }}_{EF}=0.417{\mathsf{\eta }}_{GD}+0.247{\mathsf{\xi }}_{TI}+0.625$$

(9)

$${\mathsf{\eta }}_{GD}=0.683{\mathsf{\xi }}_{TI}+0.533$$

(10)

$${\mathsf{\eta }}_{ES}=0.160{\mathsf{\eta }}_{EF}+0.306{\mathsf{\eta }}_{GD}+0.249{\mathsf{\xi }}_{TI}+0.616$$

(11)

Structural Equation for Endogenous Variables

$$y={\mathit{\Lambda }}_y\eta +\epsilon$$

(12)

$$\left[\begin{array}{c}{\mathrm{y}}_{EF1}\\ {\mathrm{y}}_{EF2}\\ {\mathrm{y}}_{EF3}\\ {\mathrm{y}}_{EF4}\\ {\mathrm{y}}_{GD1}\\ {\mathrm{y}}_{GD2}\\ {\mathrm{y}}_{GD3}\\ {\mathrm{y}}_{GD4}\\ {\mathrm{y}}_{ES1}\\ {\mathrm{y}}_{ES2}\\ {\mathrm{y}}_{ES3}\\ {\mathrm{y}}_{ES4}\end{array}\right]=\left[\begin{array}{ccc}0.714& 0& 0\\ 0.761& 0& 0\\ 0.784& 0& 0\\ 0.763& 0& 0\\ 0& 0.761& 0\\ 0& 0.780& 0\\ 0& 0.674& 0\\ 0& 0.722& 0\\ 0& 0& 0.793\\ 0& 0& 0.784\\ 0& 0& 0.809\\ 0& 0& 0.720\end{array}\right]\left[\begin{array}{c}{\mathsf{\eta }}_{EF}\\ {\mathsf{\eta }}_{GD}\\ {\mathsf{\eta }}_{ES}\end{array}\right]+\left[\begin{array}{c}0.490\\ 0.421\\ 0.385\\ 0.417\\ 0.420\\ 0.391\\ 0.546\\ 0.479\\ 0.372\\ 0.386\\ 0.345\\ 0.482\end{array}\right]$$

(13)

Structural Equation for Exogenous Variables

$$x={\mathit{\Lambda }}_x\xi +\delta$$

(14)

$$\left[\begin{array}{c}{\mathrm{x}}_{TI1}\\ {\mathrm{x}}_{TI2}\\ {\mathrm{x}}_{TI3}\\ {\mathrm{x}}_{TI4}\end{array}\right]=\left[\begin{array}{c}0.758\\ 0.720\\ 0.732\\ 0.781\end{array}\right]\left[\begin{array}{c}{\mathsf{\xi }}_{TI}\end{array}\right]+\left[\begin{array}{c}0.425\\ 0.481\\ 0.464\\ 0.391\end{array}\right]$$

(15)

The path analysis results are presented in

Table 3. Path analysis and hypothesis testing.

Hypothesis	Paths	Path Coefficient	p-Value	Relationship
H1	TI → EF	0.247 **	0.007	Supported
H2	GD → EF	0.417 ***	<0.001	Supported
H3	TI → GD	0.683 ***	<0.001	Supported
H4	TI → EBS	0.249 **	0.006	Supported
H5	GD → EBS	0.306 **	0.002	Supported
H6	EF → EBS	0.160 *	0.036	Supported

Note: * Sig at 0.05 level, ** Sig at 0.01 level, *** Sig at 0.001 level.

. A path coefficient value of less than 1 indicates that the cause variable influences the outcome variable. Consequently, the hypothesis test results revealed that technical and infrastructure (TI) factors have a positive and significant impact on economic and financial (EF) factors (H1) (β = 0.247, p < 0.01). Geographic and demographic (GD) factors have a positive and significant impact on economic and financial (EF) factors (H2) (β = 0.417, p < 0.001). Technical and infrastructure (TI) factors have a positive and significant impact on geographic and demographic (GD) factors (H3) (β = 0.683, p < 0.001). Technical and infrastructure (TI) factors have a positive and significant impact on electric battery charging station selection (EBS) (H4) (β = 0.249, p < 0.01). Geographic and demographic (GD) factors have a positive and significant impact on electric battery charging station selection (EBS) (H5) (β = 0.306, p < 0.01). Economic and financial (EF) factors have a positive and significant impact on electric battery charging station selection (EBS) (H6) (β = 0.106, p < 0.05).

The details of the coefficient values in Figure 2 come from the path analysis and structural equation modeling (SEM) results discussed in this study. A summary of the key coefficient values and their significance is presented in the document. Path coefficient values: these values represent the strength and direction of relationships between the latent variables (e.g., technical and infrastructure, economic and financial, geographic and demographic, and electric battery charging station selection).

Technical and infrastructure (TI) → economic and financial (EF): path coefficient = 0.247, p-value = 0.007 (supported at the 0.01 level).

Geographic and demographic (GD) → Economic and Financial (EF): path coefficient = 0.417, p-value < 0.001 (supported at the 0.001 level).

Technical and infrastructure (TI) → Geographic and Demographic (GD): path coefficient = 0.683, p-value < 0.001 (supported at the 0.001 level).

Technical and infrastructure (TI) → electric battery charging station selection (EBS): path coefficient = 0.249, p-value = 0.006 (supported at the 0.01 level).

Geographic and demographic (GD) → electric battery charging station selection (EBS): path coefficient = 0.306, p-value = 0.002 (supported at the 0.01 level).

Economic and financial (EF) → electric battery charging station selection (EBS): path coefficient = 0.160, p-value = 0.036 (supported at the 0.05 level).

Mediation analysis: this study also performed a mediation analysis to explore the indirect effects through intermediate variables:

Technical and infrastructure (TI) → economic and financial (EF) → electric battery charging station selection (EBS): indirect effect = 0.042, p-value = 0.035 (partial mediation). Technical and infrastructure (TI) → geographic and demographic (GD) → electric battery charging station selection (EBS): indirect effect = 0.070, p-value = 0.041 (partial mediation).

Overall model fit: SEM analysis provided fit indices that suggest a good model fit: Chi-Square = 103.501, df = 86, p-value = 0.096. RMSEA = 0.026, RMR = 0.012, GFI = 0.960, NFI = 0.955, TLI = 0.989, CFI = 0.992.

These values indicate the relationships between the variables studied and their significance in the model. This study confirms that factors such as technical and infrastructure, geographic and demographic, and economic and financial variables significantly impact the selection of electric battery charging stations.

4.5. Mediation Analysis

Table 4. Mediation analysis.

Hypothesis	Paths	Direct Effect	Indirect Effect	p-Value	Mediation	Relationship
H7	TI → EBS	0.249 ***		0.006	Partial	Supported
	TI → EF → EBS		0.042 *	0.035		Supported
H8	TI → EBS	0.306 **		0.002	Partial	Supported
	TI → GD → EBS		0.070 *	0.041		Supported

Note: * Sig at 0.05 level, ** Sig at 0.01 level, *** Sig at 0.001 level.

presents the mediation analysis, a component of structural equation modeling, which examines the indirect effects of two causal factors through an intermediate variable. In this study, economic and financial and geographic and demographic factors act as mediation variables to explore the relationship between technical and infrastructure factors and electric battery charging station selection. The analysis begins with investigating the relationship between technical and infrastructure factors and electric battery charging station selection, using economic and financial factors as a mediation variable (H7). Subsequently, this study examines the relationship between technical and infrastructure factors and electric battery charging station selection, using geographic and demographic factors as a mediator variable (H8). For the mediation variable (H7) concerning the relationship between technical and infrastructure and electric battery charging station selection, the indirect effect was found to have a coefficient of 0.042, which is statistically significant at the 0.05 level. This indicates that economic and financial factors partially mediate the relationship between technical and infrastructure factors and electric battery charging station selection.

When examining the mediator variable of geographic and demographic factors (H8) in the relationship between technical and infrastructure and electric battery charging station selection, it was observed that the direct effect of technical and infrastructure factors on electric battery charging station selection diminished. The indirect effect had a statistically significant coefficient of 0.070 at the 0.05 level. Therefore, geographic and demographic factors partially mediate the relationship between technical and infrastructure factors and electric battery charging station selection.

5. Discussion

The findings of this study align with site selection theory, demonstrating that factors related to technical and infrastructure aspects, geographic and demographic characteristics, as well as economic and financial considerations, all play a significant role in the decision-making process for choosing the location of an electric vehicle charging station. Technical and infrastructure (TI) factors have a positive and significant impact on the economic and financial (EF) factors (H1). The interplay between the technical and infrastructure aspects of electric vehicle (EV) charging stations and their economic and financial implications is significant. Technical and infrastructure elements such as the availability of high-capacity power grids, advanced charging technology, and reliable maintenance systems directly influence the overall cost-efficiency and profitability of EV charging stations [42]. The integration of cutting-edge charging technologies can lead to lower operational costs and higher energy efficiency, thereby enhancing financial returns. Furthermore, robust infrastructure reduces downtime and increases the reliability of charging services, which in turn attracts more users and generates higher revenue streams. Studies indicate that investments in superior technical and infrastructural components are crucial for maximizing the economic viability and financial sustainability of EV charging networks [43].

Geographic and demographic factors, such as population density, urbanization rate, and proximity to major transportation routes, have a profound impact on the economic and financial viability of electric vehicle charging stations. High population density and urbanization rates increase the potential user base for charging stations, leading to higher utilization rates and, consequently, greater revenue generation. Additionally, stations located near major transportation routes or in densely populated urban areas benefit from increased visibility and accessibility, attracting more users and reducing the payback period for the initial investment [44]. These geographic and demographic considerations are crucial for optimizing the economic and financial performance of charging station networks, confirming H2. By integrating these geographic and demographic factors into the site selection process, operators can enhance the economic and financial outcomes of their electric vehicle charging stations, ensuring long-term sustainability and profitability.

Technical and infrastructure (TI) factors significantly influence the geographic and demographic (GD) selection of EV charging station locations. Geographic information systems (GIS) are instrumental in determining the optimal sites for EV charging stations by integrating diverse criteria, including land use, demographic data, and employment centers [6]. These systems employ spatial suitability models to assess various factors, generating hot spot maps that indicate areas of high, moderate, and low suitability for charging station placement, confirming H3. This approach ensures a well-distributed charging infrastructure, which is crucial for alleviating range anxiety among EV users and fostering the adoption of electric vehicles across different regions [45].

Technical and infrastructure aspects play a crucial role in determining the placement of EV charging stations. These factors enhance the efficiency, reliability, and user-friendliness of the charging stations, thereby significantly influencing the decision-making process for both EV users and operators, confirming H4. Technical and infrastructure factors are critical in the selection of EV charging stations. On the technical side, charging speed is paramount, with stations offering higher power outputs (e.g., 50 kW or more), significantly reducing user waiting times and thereby increasing attractiveness. Compatibility is also essential, as stations must support various EV models and charging standards (e.g., CCS, CHAdeMO) to accommodate a broader range of vehicles. Furthermore, high reliability and ease of maintenance are vital, with stations that demonstrate robust uptime and offer prompt maintenance services being highly preferred to minimize downtime [46].

The design and implementation of electric battery charging station networks are significantly influenced by geographic and demographic factors. The placement of these charging stations is determined by several variables, including the percentage of electric vehicles (EVs) in the area, population density, the availability of Electric Vehicle Supply Equipment (EVSE) ports, energy costs, and the local climate, confirming H5. Accessibility to charging infrastructure varies across regions, influencing the equitable use of electric vehicles [47]. Additionally, the effectiveness of charging stations is shaped by geographical data and user characteristics, underscoring the necessity for customized infrastructure deployment strategies. To address these issues, innovative solutions such as multi-voltage charging stations have been suggested to efficiently serve different electric vehicle models [48]. In summary, a thorough comprehension of geographic and demographic dynamics is crucial for optimizing the design and location of electric battery charging station networks. This understanding is key to promoting the widespread adoption of electric vehicles and ensuring environmental sustainability.

The adoption of EV charging stations is heavily influenced by key economic and financial factors. Elements such as the cost of charging, fuel prices, the reliability of EV batteries, power supply stability, spare parts management, and government policies play a significant role in determining the economic feasibility and operation of EV charging stations, which supports H6 [49]. Furthermore, insufficient charging infrastructure is a major obstacle to EV adoption, highlighting the importance of strategically locating and designing charging stations [50]. Additionally, the standardization of connectors, charging types (DC or AC), power levels, and the resolution of operational and economic challenges are crucial for the successful implementation of charging infrastructure to support the growing EV market [51]. Understanding these factors is essential for policymakers and stakeholders to promote the widespread adoption of EVs and the development of effective charging station networks.

The selection of EV battery charging stations is significantly influenced by technical and infrastructure factors, with economic and financial variables serving as mediating elements. Technical aspects such as charging speed and compatibility with various EV models, along with infrastructure considerations like strategic placement and accessibility, are critical for enhancing user satisfaction and station selection, which supports H7. Additionally, economic factors including the cost of charging, energy expenses, government policies, and maintenance costs play a crucial role in determining the financial viability and attractiveness of charging stations, thereby facilitating their widespread deployment in strategic locations.

Geographic and demographic factors significantly influence the relationship between technical and infrastructure requirements and the selection of electric battery charging stations. The research underscores the importance of geographical data, traffic patterns, and investment costs in optimizing the placement of charging stations [27]. The planning and location of these stations are also shaped by spatial parameters, user preferences, and their impact on the existing electrical grid, necessitating an integrated approach that employs geographic information systems (GIS) [19]. Additionally, the technical design specifications for charging stations, including sockets, plugs, charging power, duration, and safety measures, are crucial considerations, guided by standards such as the Swedish Standard 4364000 [52] and European directives (Sweden Institute for Standards) [50]. Understanding these factors is vital for policymakers to strategically place and configure charging stations, thereby supporting the adoption and efficient operation of electric vehicles in both urban and semi-urban regions.

6. Conclusions

6.1. Theoretical Contribution

This study significantly advances the theoretical understanding of electric vehicle (EV) charging station placement by integrating various interdisciplinary factors. Previous research predominantly focused on individual aspects such as technical specifications, economic viability, or geographic considerations. However, this study’s comprehensive framework synthesizes technical and infrastructure requirements, economic and financial implications, and geographic and demographic influences into a unified model. This integrated approach not only provides a holistic view of the factors affecting EV charging station selection but also highlights the interdependencies between these factors. For instance, it elucidates how advancements in charging technology can influence geographic and demographic trends and how economic incentives can mediate the impact of technical infrastructure on site selection. By doing so, this research fills a critical gap in the literature, offering a robust theoretical foundation for future studies aiming to optimize the deployment of EV charging infrastructure.

6.2. Practical Implication

The practical implications of this study are profound for policymakers, urban planners, and entrepreneurs involved in the EV infrastructure sector. The findings provide actionable insights into the strategic placement of EV charging stations, ensuring that they meet both user needs and economic feasibility. For policymakers, this study emphasizes the importance of integrating GIS-based tools to assess and select optimal locations, considering traffic patterns, population density, and proximity to amenities. Entrepreneurs and investors can leverage the economic and financial analyses presented to identify high-return investment opportunities and to design sustainable business models that incorporate financial incentives and subsidies. Moreover, the research underscores the necessity for technical compatibility and robust infrastructure to ensure reliability and user satisfaction. By adopting this study’s comprehensive framework, stakeholders can enhance the efficiency and accessibility of EV charging networks, ultimately promoting the widespread adoption of electric vehicles and contributing to environmental sustainability.

6.3. Research Limitations and Future Research Areas

6.3.1. Limitations of the Research

The research on the selection of electric battery charging stations presents several limitations that must be addressed. Firstly, this study primarily relies on geographic and demographic factors, which may not fully capture the complexities involved in the optimal placement of charging stations. The variability in urban and rural infrastructure and the evolving nature of EV technology can introduce significant challenges that were not entirely accounted for in this research. Additionally, the economic and financial data utilized were constrained to specific regions and timeframes, limiting the generalizability of the findings to other contexts or future conditions.

Furthermore, the technical specifications and standards referenced, such as the Swedish Standard 4364000 and European directives, (Sweden Institute for Standards) may not be universally applicable, potentially affecting the relevance of the conclusions in different regulatory environments. The reliance on GIS and spatial analysis, while beneficial, also presents limitations in terms of data accuracy and the ability to integrate real-time traffic and usage patterns effectively.

6.3.2. Suggestions for Further Research

To address these limitations and build upon the current findings, future research should explore several key areas. Dynamic data integration: Incorporate real-time data on traffic patterns, energy consumption, and EV usage to enhance the accuracy and responsiveness of site selection models. This could involve leveraging advanced IoT technologies and machine learning algorithms. Broader geographic scope: conduct comparative studies across different countries and regions to understand how local policies, cultural factors, and infrastructure differences impact the optimal placement of EV charging stations. Technological advancements: Investigate the implications of emerging EV technologies, such as ultra-fast charging and wireless charging, on infrastructure requirements and site selection criteria. This includes studying the impact of integrating renewable energy sources and energy storage solutions at charging sites. User behavior and preferences: Perform in-depth analyses of EV user behavior and preferences, considering factors such as charging habits, range anxiety, and willingness to pay for charging services. Surveys and field experiments could provide valuable insights into user-centric site selection. Regulatory and policy frameworks: Examine the influence of different regulatory and policy frameworks on the deployment and operation of EV charging stations. This includes evaluating the effectiveness of incentives, subsidies, and public–private partnerships in promoting EV infrastructure. Economic and financial models: Develop more sophisticated economic and financial models that account for long-term investment returns, maintenance costs, and the economic benefits of reduced greenhouse gas emissions. These models should be adaptable to changing market conditions and technological advancements.

By addressing these areas, future research can provide a more comprehensive and nuanced understanding of the factors influencing the selection of electric battery charging stations, ultimately supporting the broader adoption and efficient operation of electric vehicles.