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Article

Consumer Segmentation and Market Analysis for Sustainable Marketing Strategy of Electric Vehicles in the Philippines

by
John Robin R. Uy
1,
Ardvin Kester S. Ong
1,2,*,
Danica Mariz B. De Guzman
1,
Irish Tricia Dela Cruz
1 and
Juliana C. Dela Cruz
1
1
School of Industrial Engineering and Engineering Management, Mapúa University, Manila 1002, Philippines
2
E.T. Yuchengco School of Business, Mapua University, Manila 1204, Philippines
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2024, 15(7), 301; https://doi.org/10.3390/wevj15070301
Submission received: 26 May 2024 / Revised: 18 June 2024 / Accepted: 25 June 2024 / Published: 8 July 2024
(This article belongs to the Special Issue Deep Learning Applications for Electric Vehicles)

Abstract

:
Despite the steady rise of electric vehicles (EVs) in other countries, the Philippines has yet to capitalize on its proliferation due to several mixed concerns. Status, socio-demographic characteristics, and availability have been the main concerns with purchasing EVs in the country. Consumer segmentation and analysis for EV acceptance and utility in the Philippines were determined in this study due to the need for understanding consumer preferences and market segmentation towards EVs in the Philippines. A total of 311 valid responses coming from EV owners were collected through purposive and snowball sampling approaches. The data were collected via face-to-face distribution and online distribution of a questionnaire covering demographic characteristics for market segmentation. Demographic data such as gender, age, residence type, car ownership, and income were used to identify consumer segments using the K-means clustering approach. Jupyter Notebook v7.1.3 was used for the overall analysis, and the number of clusters was optimized, ensuring precise segmentation. The results indicated a strong correlation between car ownership and the ability to purchase EVs, where K-means clustering effectively identified consumer groups. The groupings also included “Not Capable at All” to “Highly Capable” individuals based on their likelihood to purchase EVs. Based on the results, the core-value customers of EVs are male, older than 55 years old, live in urban areas, own a vehicle and car insurance, and have a monthly income of more than PHP 130,000. Following those are high-value customers, considered target users expected to use EVs frequently. It could be posited that customers are frequent purchasers of products and services. Based on the results, high-value customers are male, aged 36–45 years old, live in urban areas, own a car, have car insurance, and have a monthly income of PHP 100,001–130,000. Both of these should be highly considered by EV industries, as these characteristics would be the driving market of EVs in the Philippines. The constructed segmentation provided valuable insights for the EV industry, academic institutions, and policymakers, offering a foundation for targeted marketing strategies and promoting EV adoption in the Philippines. Moreover, the sustainable marketing strategies developed could be adopted and extended among other developing countries wanting to adopt EVs for utility. Future works are also suggested based on the study limitations for researchers to consider as study extensions, such as a holistic approach to EV adoption that considers environmental, social, and economic factors, as well as policies and promotion development.

1. Introduction

The adoption of electric cars has been accelerating around the globe, providing a significant shift in consumer preference and the alteration of environmental policies. From bustling urban centers to remote rural areas, there has been a visible increase in electric vehicles (EVs), signifying a significant evolution in transportation. According to Ritchie and Roser [1], 40 million electric cars would be in use globally during 2023, with sales reaching a quarter of all new vehicles sold. Countries with the highest sales for EVs globally are Norway, which is at the top with over 90% of new car sales being electric, followed closely by China, with almost 40% of new car sales being electric. The global sales of non-electric cars peaked in 2018, and since then EVs have steadily increased their market share. Many countries are looking to improve the adoption of EVs, with the European Union proposing a ban on the sale of new gasoline- and diesel-operated cars starting in 2035. Moreso, China dominates the plug-in light commercial vehicle and electric bus deployment [2].
Among the different automobiles in Asia, the EV market has been dominated by BYD, with a total percentage of 84% of all EV sales in all of Asia, as shown in Figure 1 [3]. The rapid expansion of BYD is attributed to its cost-effective and quality batteries, which are distinguishable from other brands [4]. Following BYD is Tesla, a high-end brand with the second-highest registered EV sales in Asia, with 19% in the market. Additionally, the chart depicts other EV manufacturers, including Nissan, Hyundai, General Motors, Honda, and Volkswagen, each having a modest percentage of registered EVs [4]. This suggests a market open to innovations in EVs and the potential for expansion by new companies. The sales of electric cars in Asia are influenced by consumer preferences, infrastructure development, and government incentives, reflecting a diverse range of factors impacting the market.
According to Monzon [5], BYD is the most popular EV brand in the Philippines mainly because of its price, which is much cheaper and has low maintenance costs compared to other EV brands. These aspects, combined with BYD’s dedication to delivering performance and affordability, significantly boost the appeal of BYD EVs. Furthermore, Guison [6] discussed how, despite the significant desire of consumers surrounding Tesla to enter the Filipino market, consumers preferred new emerging brands such as Wuling and Aion because of their more budget-friendly prices.
EVs have been gaining popularity in recent years because of their sustainability in decreasing the amount of rising climate emissions; however, problems and concerns can still be prevented, especially in low- to middle-income countries [7]. There are many barriers to adopting EVs, such as financing, lack of charging infrastructure, issues with driving range and use in different terrains, and availability of EV parts and repair stations. Among these issues, charging infrastructure has the most problems. According to Alanazi [8], establishing a charging infrastructure is immensely difficult because of the chicken and egg problem, which describes that consumers will not purchase EVs if there are not enough charging stations. Subsequently, charging service providers will not construct more charging infrastructures if there is a lack of EV consumers. Another problem, according to Lai et al. [9], is the integration of EVs in smart cities. It has become a problem because smart cities’ purpose is to cut gas emissions and enhance air quality, which EVs could be a big help with. However, the huge problem is the high cost of building charging infrastructures with limited land, which needs a substantial amount of investment. Overcoming this major problem could result in a huge increase in the number of consumers using EVs.
In terms of financial aspects, Gomez Vilchez et al. [10] highlighted that the detrimental reason for purchasing problems in developed countries in Europe is the price range of EVs. Government incentives and support are needed for the mitigation and development of EV utility in countries. Similar to the Philippines, it could be posited that EVs and hybrid cars would need government support and initiatives for the community to be able to afford them [11]. As discussed in the study by Ong et al. [11], the need for progression through government support has an influence on the purchasing of EVs in the Philippines. In support of related studies is Alanazi [8], who explained how the prior factors have affected the purchase of EVs. It was also added that battery swapping for range support, battery technology, and charging time are issues that should also be covered by developers and EV industries.
Another concern regarding the adoption of EVs, specifically in the Philippines, is the high electricity cost. Rosellon [12] stated that electricity in the Philippines is more expensive compared to other countries, making charging much more expensive or the same as traditional gasoline-powered vehicles. Moreover, the high ownership cost partly due to the need for more subsidies and incentives to make electric cars more affordable is another evident issue in the country. Therefore, it could be posited that sustainable solutions are not easily adaptable in the county.
The Philippines has taken steps to address these issues and promote EVs. For example, it is reducing the tariffs on EVs to make them more accessible. However, some problems hinder the growth of the adoption of EVs due to the need for charging infrastructure and market limitations. Cabuenas [13] explained that to combat this, the government created a mandatory order named Executive Order No. 12, which was signed by the president of the Philippines in 2023 and modified the tariff rate of the components for EVs from the previous 5 to 30% to currently 0%. This order was implemented by the EVs Industry Development Act (EVIDA), promoting green transport and helping reduce the country’s carbon emissions. The order implemented by the current president, Ferdinand Marcos Junior, can make EVs more appealing to consumers.
Recent studies have shown that factors such as battery durability, range anxiety, and the potential resale value of EVs are crucial factors in consumer preference for EVs. Etxandi-Santolaya et al. [14] highlighted that battery durability is essential because EVs rely on a battery pack, making a long-lasting, efficient, and reliable battery essential for a positive consumer experience. It is a massive attraction for consumers who want their cars to have sufficient range and charging time, especially those from warmer climate countries since batteries tend to deteriorate in high heat or may cause explosions due to high heat, like in the Philippines. Moreover, issues on battery durability were seen by Roberson et al. [15]. They explained that the larger-sized vehicles are more sustainable compared to older ones, but this is dependent on the model. For the price, it was seen that Tesla, for example, holds their price value for older models but newer models like other brands could have higher depreciation costs. Wan and Wang [16] also explored battery durability, and different types have different issues. For example, lithium-based batteries do not have much of a leaking issue but have a limited lifecycle. On the other hand, nickel-based batteries are more mature and better suited for EVs. Chen et al. [17] also found battery range issues and suggested ways for sorting and identification of the best battery type.
Another problem highlighted by He and Hu [18] is that consumers are concerned about the range of the vehicle, particularly when driving long distances, and the need for charging infrastructure. This can affect consumer preference for EVs, as individuals may hesitate to buy an EV due to these concerns. Lastly, the potential resale value of EVs needs to be solved. According to Xue et al. [19], if a consumer knows the potential resale value of their EV, they are more inclined to purchase it as they can potentially recoup the investment if they decide to sell their vehicle in the future. On the other hand, a consumer knows that the potential resale value of an EV is low and is more likely to decline. In addition, Goetzel and Hasanuzzaman [20] explored forecasting the resale of EVs. It was explained that by approximately 2026 the prices would be on par among midsize vehicles in the market, that is, it is currently in a higher cost range for consumers. Woody et al. [21] explained that resale value is different depending on the state’s differentiation of fuel cost, that is, the more expensive the fuel cost, the cheaper the EVs are. This case is in the United States, where competitive pricing is aligned depending on electricity cost, direct purchasing, and climate of the state as well. Foley et al. [22] stated that the high upfront cost needs to detail the reason for the high cost and the solution. They added that the primary reason for the high upfront cost is the battery and its development, especially for its improved functioning in places with warmer climates. Lastly, Bruckmann et al. [23] explained that cost anxiety is present among consumers where conventional vehicles have a lower price range than hybrid cars and EVs.
In recent studies focusing on Asian countries, Joon-Hyun [24] emphasized the factors significantly affecting consumer preference for EVs in South Korea. These include the high upfront cost of EVs, limited charging infrastructure, and concerns over battery durability. It was stated that the main problem was the high upfront cost, which caused considerable reluctance from consumers to purchase EVs. This is because a car survey conducted by Encar, the largest used car platform in South Korea, mentioned a massive preference shift from EVs to gasoline vehicles in 2024, after South Korea gained huge prominence for EVs in 2022.
This was followed by Zhang et al. [25], who explained that the pursuit of novelty is one of the significant factors affecting consumer preference. Novelty seeking refers to pursuing new and diverse experiences, goods, or technologies. It is a characteristic that can impact consumer preferences because individuals with this trait are more inclined to be attracted to fresh and innovative products and may be more open to paying a premium for EVs due to their novelty. Schiavo et al. [26] mentioned that the factor of novelty seeking overlooks other important determinants of consumer preferences, such as environmental concerns, economic considerations, and vehicle performance attributes.
In China, Wang et al. [27] presented acceptance among Chinese consumers regarding EVs. It was explained that only 18% are willing to shift to EVs because of environmental impact, technical level, perceived risks, and marketing. In the Philippines, Ong et al. [11] found that government incentives could help but are yet to be given to the community for EV adoption. However, several plans on developing a sustainable city are being expressed. Only those who have already bought electric-type or hybrid-type vehicles are subjected to different road regulations. For example, those with hybrid vehicles or EVs are exempted from the coding schemes put in place to solve traffic congestion. It was explained in the study by Oliveira and Dias [28] that the limitation of different factors explored in related studies does not really impact demographic characteristics across different Asian markets. Moreover, demographic characteristics are significant factors affecting consumer preference, yet there has been limited exploration of them [11,28]. Understanding these characteristics can assist manufacturers in properly catering EVs to specific age ranges. Although several factors have been identified, these studies have certain limitations, e.g., focusing on preference analysis, acceptance, and adoption.
Despite adoption and preference analysis performed by Uy et al. [29] from the Philippines, buyer and user demographics of EVs have yet to be established in the country. Moreover, the marketing segmentation and strategies have yet to be established. Following the research on consumer behavior toward EVs in the Philippines, minimal literature was discovered specifically for conjoint analysis using K-means clustering. According to Ong et al. [30], using K-means clustering to determine consumers’ preferences could create proper segmentation, thereby enabling the development of marketing strategies. This was evident in the study by Gumasing et al. [31], where K-means clustering helped in determining the demographic profiles of consumers and segmented them from high-profile customers to least potential users. Hence, it would be helpful to use of K-means clustering to distinguish the potential customer classification and determine demographic characteristics for potential buyers of EVs in the Philippines.
Based on a thorough review of the literature, even more of it covers the technical factors around EV adoption and acceptance. Moreover, the importance of demographic characteristics for preference analysis and marketing strategies is lacking and should be considered. Therefore, this study aimed to determine consumer preference for EVs and market segmentation using K-means analysis to create sustainable business strategies. This study’s findings could benefit the EV industry and academic institutions by providing information regarding consumer preference for EVs. Understanding elements that influence consumer behavior towards EVs can aid in enhancing not only the current and potential development of EVs but also in refining marketing strategies and gaining further insights to promote the adoption of EVs. This segmentation can reveal different consumer groups’ diverse needs and preferences, enabling more targeted marketing and product development strategies. In addition, sustainable marketing strategies were considered and suggested based on the output of this study.

2. Analysis Approach

The research approach is presented in Figure 2. A detailed explanation of each step is presented in the subsections. The input process is the initial step taken into consideration for this study where the needed demographic characteristics and survey form were identified. The process involved two steps: a preliminary step where ethical committee board approval and informed consent among respondents were obtained, following which is the dissemination. The second step involved data processing, where data cleaning and data pre-processing were accounted—looking for missing values and outliers, as well as the K-means processing and parameter optimization. This led to the output phase where the final and optimum clustering was obtained, final market segmentation generation, followed by analysis and interpretation.

2.1. Data Collection for Profiling

The data used demographic characteristics from EV owners in the Philippines, and only those who have and have had EVs in the Philippines were considered as valid respondents. Utilizing both purposive and snowball sampling approaches helped in reaching the desired respondents. It was explained that this method ensures that a more efficient manner is placed to collect valid respondents—those who have and have had EVs, reaching other respondents they know, similar to other studies [32]. It was indicated that this is advantageous since it reduces time and effort in collecting representative data. The survey ran from November 2023 to the end of February 2024.
The data samples were collected via online surveys and face-to-face collection using a QR code and Google Forms, comprising a total of 311 respondents who are EV users living in the Philippines. The data included demographics regarding consumers who own EVs. Demographics included gender, age, type of residence, car ownership, car insurance, and monthly income. The genders used for the data were male (68.7%), female (23.5%), and prefer not to say (23.5%). The choices for the type of residence included urban (85.2%) and rural (14.8%) areas. Moreover, car insurance also had yes (92.9%) or no (7.1%) options. Lastly, the monthly income (in PHP) options were less than 40,000 (11.3%), 40,001–70,000 (21.6%), 70,001–100,000 (33.9%), and the rest were higher (33.2%) from age groups of 26–35 years old (30.1%), 36–45 years old (24.8%), 46–55 years old (27.7%), and older (17.4%).

2.2. Data Pre-Processing and Optimization

The demographics data underwent data pre-processing and organization, where missing values and outliers were removed to prepare them for the subsequent analysis using K-means clustering. An optimal number of clusters in the dataset was determined using the elbow method under K-means clustering. This method calculates the squared difference in various K values to identify the “elbow” point, representing the ideal number of clusters that can effectively segment the consumer data based on demographic characteristics [33]. Optimizing the number of clusters ensured that the K-means clustering analysis produced meaningful and well-defined consumer segments [34], enabling a more accurate understanding of the target market for EVs in the Philippines.

2.3. K-Means Clustering

Demographics data for K-means clustering were analyzed with Python’s Jupyter Notebook v7.1.3. According to Chaudhry et al. [35], this technique works by separating dissimilar items and grouping similar data points together. Sarker et al. [36] stated that the most reliable machine learning algorithm is K-means clustering when handling large datasets. Due to the characteristics of this machine learning algorithm, it is frequently applied in customer segmentation to extensively identify consumer needs based on demographic, geographic, and behavioral data. In this particular case, K-means clustering was utilized to segment Filipino consumers who use EVs based on gender, age, type of residence, car ownership, car insurance, and monthly income.

3. Results

3.1. Elbow Method

In Figure 3, the elbow method determined the optimal number of clusters to minimize the sum-of-squared error (SSE) during cluster optimization. Multiple sets of four, six, seven, and eight clusters were evaluated, and the most favorable outcome was obtained using four clusters compared to the rest. This was because the local optima were obtained with a low SSE value. Despite having a lower SSE among the other clusters considered, the spread of data and data point separation was not very different. A low distance separation was seen within clusters, especially when eight clusters were used. As indicative of the K-means clustering approach, the same characteristic data points should have relatively close distances [37], that is, coders should ensure that meaningful clustering output should be presented with acceptable parameter scoring. With the output, the four-cluster generation was considered as optimum clustering output.

3.2. K-Means Clustering Output with Validation Scores

Figure 4 shows the results from the clustering analysis. The cluster centers show the typical values for each group in the dataset, indicating the clear patterns characterized by specific numeric features. For instance, the first group’s average is around 1.68, 1.73, 1.29, and 2.00. The sum-of-squared error (SSE), about 317.13, shows that data points are spread around these typical values. A lower SSE means the groups are more tightly packed around their averages. The Calinski–Harabasz (CH) score, roughly 38.42, suggests that the groups are well separated, indicating apparent differences. The Davies–Bouldin (DB) score, about 1.82, suggests that the groups are similar but distinct, with clear boundaries. These results showed that the clustering algorithm effectively identified different groups in the dataset, each with its distinct features and boundaries.
It can be seen in the graphical segmentations that the first cluster is the core customer—the intended target audience expected to use EVs regularly. According to Rane et al. [38], these customers generate the most revenue because of frequent purchases of products and services. This indicates that for businesses aiming to attract a vast range of consumers, this group presents an ideal target market segment to focus on. Based on the results, the core value customers of EVs are male, older than 55 years old, live in urban areas, own a vehicle and car insurance, and have a monthly income of more than PHP 130,000.
The second cluster is high-value customers, considered target users expected to use EVs frequently. It could be posited that customers are frequent purchasers of products and services. Based on the results, high-value customers are male, aged 36–45 years old, live in urban areas, own a car, have car insurance, and have a monthly income of PHP 100,001–130,000.
Lastly, the final cluster is low-value customers. They are expected to use EVs less frequently. These customers are usually new or casual users of EVs. As a result, they have limited purchasing power relative to other consumers due to their minimal contributions to various segments. Based on the results, low-value customers are male, aged 26–35 years, live in urban areas, own a car, have car insurance, and have a monthly income of PHP 40,001–70,000.
The consistent involvement of the three cluster groups has a substantial impact on a company’s profitability, as market segmentation provides businesses with insights into their customer base. By understanding customer segmentation, it is easier to target users of EVs in the country, which can assist companies in customizing specific marketing campaigns for valuable customers that are more likely to use EVs and those who are less likely to use one. This enables businesses to target specific customer segments instead of the broader market.
The cluster shows the respondents who are not capable, somewhat capable, capable, and highly capable in their ability to purchase EVs based on different demographics and profiles of the respondents such as gender, age, type of residence, car ownership, and car insurance. The x-axis outputs record the variable representation of the demographic characteristics—its spread among the different characteristics. The y-axis represents the distribution in relation to the capability to purchase EVs among demographics.
Figure 5 shows that the main demographic of highly capable respondents in terms of gender is males, with 218 respondents. This is attributed to the gender wage gap in the Philippines, where women earn significantly less than men [39]. This difference in income could impact women’s purchasing power and access to luxury items such as EVs.
The cluster visualization based on age (Figure 6) shows a massive difference between capable and highly capable respondents. Respondents between 18 and 35 years old are seen to be less capable of purchasing EVs than respondents between 36 and older. This is likely caused by the cumulative effect of career progression and income growth over time. Older respondents, benefiting from more extensive work experience and potentially higher-paying professions, are better positioned financially to afford EVs.
It can be seen in Figure 7 that the demographic for EVs is more distinct in urban areas than in rural areas. This could be due to urban residents having higher incomes due to better employment opportunities and wage levels and easier access to luxury goods than those living in rural areas. In the Philippine setting, differences in income level are present among the different locations. According to Chua et al. [40], there is a 12% difference in nominal wages between urban and rural areas, with higher wages in urban areas. This could imply the buying power of consumers, especially those in rural areas, who may prefer gasoline vehicles over electric vehicles, leading to a decrease in EV adoption in rural areas.
Figure 8 emphasizes the close connection between owning a car and the capacity to buy EVs in the Philippines. The results indicate that owning a vehicle is a crucial factor in assessing financial readiness and interest in investing in EVs, as most consumers who own cars can purchase various types of vehicles. This finding is particularly relevant in the Philippines, where income levels and vehicle ownership patterns significantly influence consumer behavior and market price.
Lastly, Figure 9 shows that most of the respondents can purchase EVs. In relation, out of 310 respondents, only 22 have car insurance. It can be seen that most of those who are not capable of purchasing EVs also fall into the demographic of not owning car insurance due to possible financial constraints or a lack of awareness regarding the importance of insurance. This finding highlights the need for targeted financial education and insurance awareness campaigns to help lower-income consumers overcome barriers to EV adoption and ensure adequate vehicle protection, especially in the Philippines, where public transportation is bad and the need for personal vehicles is crucial.

4. Discussion

4.1. Market Profiling

This study highlights the significance of developing sustainable business strategies that cater to current market needs and align with long-term environmental and economic objectives. Sustainable strategies in the context of EVs encompass a range of initiatives, including enhancing charging infrastructure, promoting renewable energy sources for EV charging, implementing policies to reduce carbon emissions, and fostering a supportive ecosystem for EV adoption. These strategies address critical challenges such as range anxiety, charging infrastructure limitations, and high ownership costs associated with EVs. By emphasizing sustainability in EV adoption strategies, stakeholders can contribute to reducing greenhouse gas emissions, improving air quality, and transitioning towards a more sustainable transportation system. Additionally, a sustainable approach can help drive innovation, create new business opportunities, and enhance the overall competitiveness of the EV market.

4.2. Sustainable Strategies

Sustainability delves into the importance of developing business strategies that cater to current market needs and align with long-term environmental and economic goals. Several actionable items are proposed to promote sustainability in the Philippines, particularly in transportation, smart city development, and infrastructure enhancement. Enhancing the charging infrastructure is crucial, as it involves increasing the number of public and private EV charging stations in urban and rural areas, which helps produce lower carbon emissions. This can be achieved by partnering with private companies to invest in charging networks and offering incentives like tax breaks or subsidies to businesses that install these stations, thus reducing range anxiety and making EVs more practical [41]. Promoting EV adoption through financial incentives such as tax rebates, reduced registration fees, and subsidies can lower the initial cost barrier and make EVs more accessible to a broader population segment. Developing comprehensive EV policies that support long-term adoption, including mandates for EV integration in public transportation fleets, will ensure a steady transition to EVs across various sectors, contributing to lower emissions [42]. Lastly, integrating smart grid technology is essential for smart city development. Developing and deploying smart grids can efficiently manage electricity distribution and consumption, making the entire system more sustainable and reliable [43]. These initiatives collectively address the challenges and leverage opportunities to provide a sustainable and economically stable path in the Philippines.

4.3. Theoretical Implications

The findings indicate that older males within the rich income status from the Philippines would be the core customers of EVs. The roles of the demographic characteristics found in the core customers align with Buhmann and Criado [44], who explained that status-driven demographics would lead to EV adoption compared to conventional fuel-based vehicles. Consequently, it was evident that a higher price range would lead to lower adoption and purchasing. Nonetheless, this claim is in general understandable, as reasonably priced items would be key for sustainable marketing. Similar to the high-value customers of EVs, older men with high monthly income are seen to have higher capability to purchase. It was evident that mostly men are the customers. However, Parent [45] tested that masculinity does not positively correlate with EV adoption and purchase. It was emphasized that men’s attitude, intention, and actual behavior does not necessarily reflect masculinity contingency. Basically, it could be posited that men relate to cars as a product and utility more than women [46].
Lastly, the final cluster is low-value customers. Based on the results, low-value customers are male, aged 26–35 years, live in urban areas, own a car, have car insurance, and have a monthly income of PHP 40,001–70,000. This output could be related to the purchasing power of individuals being a low-targeted market since the monthly income would not align with the purchasing power. Evidenced in the Philippine setting and economy, Cahigas et al. [47] explained that this cluster classification based on monthly income would be about mid-poor and more likely to take public transportation due to price. In accordance, Lashari et al. [48] negated this by stating that the lower socio-demographic profile has higher purchasing intention in South Korea. The reason for this is that they perceive EVs as an investment, especially those burdened by fuel costs, repair of traditional and conventional vehicles, and maintenance of the vehicle. Nonetheless, the male demographic profile presented positive intention for purchase. Despite being influential, Mesimaki and Lehtonen [49] projected that demographic characteristics and profiling are not sole predictors despite being insightful and significant, but experience, knowledge, and familiarity could also be areas to considered.

4.4. Practical Implications

The findings of this study can be used as a valuable resource for future researchers and practitioners in the field of EVs in the Philippine market. This study aimed to determine consumer preferences for EVs and market segmentation using K-means analysis to create sustainable business strategies. The findings can benefit the EV industry and academic institutions by providing insights into consumer preferences for EVs. The results showed a correlation between age and purchasing ability within different demographic groups, with older individuals demonstrating higher purchasing ability. The segmentation revealed different consumer groups’ diverse needs and preferences, enabling more targeted marketing and product development strategies. Understanding these different consumer groups can aid in increased EV adoption. For example, the government could provide incentives for lower-income groups, thereby creating opportunities for sustainable transportation adoption. The government can also offer tax exemptions and subsidies for EV purchases to allow lower-income consumers to offset the higher upfront costs of EVs compared to fueled vehicles.

4.5. Limitations and Future Research Works

This study has several limitations despite its promising output. Firstly, this study primarily focuses on quantitative analysis for customer segmentation. A combination of quantitative and qualitative analyses could provide a more comprehensive understanding of consumer preferences and marketing strategy for EVs. Secondly, this study could benefit from adding more specific details to the characteristics of each customer segment generated through K-means clustering. This could include the average consumer budget and preference for each attribute. Lastly, future research could predict behavior among individuals purchasing EVs through different machine learning algorithms like long short-term memory, as well as other analysis such as behavioral intention and actual purchasing through structural equation modeling with machine learning [50]. This could provide other insights not captured in this study, making more detailed strategies.

5. Conclusions

This study provided valuable insights into the application of K-means clustering in understanding consumer preferences and market segmentation for EVs in the Philippines. By employing K-means clustering, distinct consumer segments based on demographic characteristics such as gender, age, type of residence, car ownership, car insurance, and monthly income were identified. The findings highlighted the strong correlation between car ownership and the ability to purchase EVs, with most “Highly Capable” and “Capable” respondents owning vehicles. This study also emphasized optimizing the number of clusters using the elbow method to ensure meaningful and well-defined consumer segments. The validation scores, including SSE, CH, and DB, collectively assessed the quality and effectiveness of the clustering algorithm in segmenting the consumer data.
The results from the clustering analysis revealed that older respondents and those living in urban areas tend to have higher purchasing power for EVs compared to younger individuals and rural residents. This insight can inform the development of targeted marketing strategies and sustainable business practices in the EV industry. Furthermore, this study highlighted the need for a holistic approach to EV adoption that considers environmental, social, and economic factors. Sustainable strategies such as enhancing charging infrastructure, promoting renewable energy sources for EV charging, and implementing policies to reduce carbon emissions can contribute to a more environmentally friendly transportation system while driving innovation and creating new business opportunities.

Author Contributions

Conceptualization, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; methodology, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; software, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; validation, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; formal analysis, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; investigation, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; data curation, J.R.R.U. and A.K.S.O.; writing—original draft preparation, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; writing—review and editing, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; visualization, J.R.R.U., A.K.S.O., D.M.B.D.G., I.T.D.C., and J.C.D.C.; supervision, A.K.S.O.; project administration, A.K.S.O.; funding acquisition, A.K.S.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Mapua University Directed Research for Innovation and Value Enhancement (DRIVE).

Institutional Review Board Statement

This study was approved by Mapua University Research Ethics Committees (approval code: FM-RC-23-01-82 and approval date: 15 October 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study (FM-RC-23-02-82).

Data Availability Statement

The data presented in this study are available upon request to the corresponding author. The data are not publicly available due to privacy restrictions of the respondents.

Acknowledgments

The authors would like to thank all the respondents who answered our online questionnaire. We would also like to thank our friends for their contributions to the distribution of the questionnaire.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ritchie, H.; Roser, M. Tracking Global Data on Electric Vehicles. Available online: https://ourworldindata.org/electric-car-sales (accessed on 26 May 2024).
  2. Trends in Electric Cars—Global EV Outlook 2024—Analysis. Available online: https://www.iea.org/reports/global-ev-outlook-2024/trends-in-electric-cars (accessed on 26 May 2024).
  3. Terasawa, M.; Tiberghien, Y. Asia Ground Zero in the Revolution of Electric Vehicle Markets. Available online: https://eastasiaforum.org/2024/03/19/asia-ground-zero-in-the-revolution-of-electric-vehicle-markets/ (accessed on 26 May 2024).
  4. Bradsher, K. How China Built BYD, Its Tesla Killer. Available online: https://www.nytimes.com/2024/02/12/business/byd-china-electric-vehicle.html#:~:text=But%20early%20batteries%20made%20from,batteries%20in%20less%20expensive%20models (accessed on 26 May 2024).
  5. Monzon, A.M. Chinese Automaker BYD Mulling Plant In ph. Available online: https://newsinfo.inquirer.net/1818921/chinese-automaker-byd-mulling-plant-in-ph (accessed on 26 May 2024).
  6. Guison, D. You Can Now Buy a Tesla in the Philippines. Available online: https://unbox.ph/news/you-can-now-buy-a-tesla-in-the-philippines/ (accessed on 26 May 2024).
  7. Xia, Z.; Wu, D.; Zhang, L. Economic, functional, and social factors influencing electric vehicles’ adoption: An empirical study based on the diffusion of innovation theory. Sustainability 2022, 14, 6283. [Google Scholar] [CrossRef]
  8. Alanazi, F. Electric vehicles: Benefits, challenges, and potential solutions for widespread adaptation. Appl. Sci. 2023, 13, 6016. [Google Scholar] [CrossRef]
  9. Lai, C.S.; Jia, Y.; Dong, Z.; Wang, D.; Tao, Y.; Lai, Q.H.; Wong, R.T.; Zobaa, A.F.; Wu, R.; Lai, L.L. A review of Technical Standards for Smart Cities. Clean Technol. 2020, 2, 290–310. [Google Scholar] [CrossRef]
  10. Gómez Vilchez, J.J.; Smyth, A.; Kelleher, L.; Lu, H.; Rohr, C.; Harrison, G.; Thiel, C. Electric car purchase price as a factor determining consumers’ choice and their views on incentives in Europe. Sustainability 2019, 11, 6357. [Google Scholar] [CrossRef]
  11. Ong, A.K.; Cordova, L.N.; Longanilla, F.A.; Caprecho, N.L.; Javier, R.A.; Borres, R.D.; German, J.D. Purchasing intentions analysis of hybrid cars using random forest classifier and deep learning. World Electr. Veh. J. 2023, 14, 227. [Google Scholar] [CrossRef]
  12. Rosellon, M.A. Clean Energy Technology in the Philippines: Case of the Electric Vehicle Industry. Available online: https://www.pids.gov.ph/publication/discussion-papers/clean-energy-technology-in-the-philippines-case-of-the-electric-vehicle-industry (accessed on 26 May 2024).
  13. Cabuenas, J.V. Executive Order on Lower Tariffs for e-Vehicles up for Mandatory Review. Available online: https://www.gmanetwork.com/news/money/economy/898068/executive-order-on-lower-tariffs-for-e-vehicles-up-for-mandatory-review/story/#:~:text=The%20order%20only%20exempts%20two,incentives%20for%20e%2Dmotorcycles%20%E2%80%94Salceda (accessed on 26 May 2024).
  14. Etxandi-Santolaya, M.; Canals Casals, L.; Montes, T.; Corchero, C. Are Electric Vehicle Batteries being underused? A review of current practices and sources of circularity. J. Environ. Manag. 2023, 338, 117814. [Google Scholar] [CrossRef]
  15. Roberson, L.; Pantha, S.; Helveston, J.P. Battery-powered bargains? assessing electric vehicle resale value in the United States. Environ. Res. Lett. 2024, 19, 054053. [Google Scholar] [CrossRef]
  16. Wan, T.; Wang, Y. The hazards of electric car batteries and their recycling. IOP Conf. Ser. Earth Environ. Sci. 2022, 1011, 012026. [Google Scholar] [CrossRef]
  17. Chen, W.; Liang, J.; Yang, Z.; Li, G. A review of lithium-ion battery for electric vehicle applications and beyond. Energy Procedia 2019, 158, 4363–4368. [Google Scholar] [CrossRef]
  18. He, X.; Hu, Y. Optimal mileage of electric vehicles considering range anxiety and charging times. World Electr. Veh. J. 2023, 14, 21. [Google Scholar] [CrossRef]
  19. Xue, Y.; Zhang, X.; Zhang, Y.; Luo, E. Understanding the barriers to consumer purchasing of electric vehicles: The Innovation Resistance Theory. Sustainability 2024, 16, 2420. [Google Scholar] [CrossRef]
  20. Goetzel, N.; Hasanuzzaman, M. An empirical analysis of electric vehicle cost trends: A case study in Germany. Res. Transp. Busi. Manag. 2022, 43, 100825. [Google Scholar] [CrossRef]
  21. Woody, M.; Adderly, S.A.; Bohra, R.; Keoleian, G.A. Electric and gasoline vehicle total cost of ownership across US cities. J. Ind. Ecol. 2024, 28, 194–215. [Google Scholar] [CrossRef]
  22. Foley, B.; Degirmenci, K.; Yigitcanlar, T. Factors affecting electric vehicle uptake: Insights from a descriptive analysis in Australia. Urban Sci. 2020, 4, 57. [Google Scholar] [CrossRef]
  23. Brückmann, G.; Wicki, M.; Bernauer, T. Is resale anxiety an obstacle to electric vehicle adoption? Results from a survey experiment in Switzerland. Environ. Res. Lett. 2021, 16, 124027. [Google Scholar] [CrossRef]
  24. Joon-hyun, M. Are Korean Drivers Losing Appetite for Evs? Available online: https://www.koreaherald.com/view.php?ud=20240130000648 (accessed on 26 May 2024).
  25. Zhang, F.; Zhang, X.; Zhang, M.; Edmonds, A.S. Literature Review of Electric Vehicle Technology and its applications. In Proceedings of the 2016 5th International Conference on Computer Science and Network Technology (ICCSNT) 2016, Changchun, China, 10–11 December 2016. [Google Scholar]
  26. Schiavo, F.T.; Calili, R.F.; de Magalhães, C.F.; Fróes, I.C. The meaning of electric cars in the context of sustainable transition in Brazil. Sustainability 2021, 13, 11073. [Google Scholar] [CrossRef]
  27. Wang, N.; Tang, L.; Pan, H. Analysis of public acceptance of electric vehicles: An empirical study in Shanghai. Technol. Forecast. Soc. Chang. 2018, 126, 284–291. [Google Scholar] [CrossRef]
  28. Oliveira, G.D.; Dias, L.C. Influence of demographics on consumer preferences for alternative fuel vehicles: A review of choice modelling studies and a study in Portugal. Energies 2019, 12, 318. [Google Scholar] [CrossRef]
  29. Uy, J.R.; Ong, A.K.; German, J.D. Marketing strategy and preference analysis of electric cars in a developing country: A perspective from the Philippines. World Electr. Veh. J. 2024, 15, 111. [Google Scholar] [CrossRef]
  30. Ong, A.K.; Prasetyo, Y.T.; De Leon, L.A.; Ayuwati, I.D.; Nadlifatin, R.; Persada, S.F. Plantitas/Plantitos preference analysis on succulents attributes and its market segmentation: Integrating conjoint analysis and K-means clustering for gardening marketing strategy. Sustainability 2022, 14, 16718. [Google Scholar] [CrossRef]
  31. Gumasing, M.J.; Prasetyo, Y.T.; Ong, A.K.; Persada, S.F.; Nadlifatin, R. Factors influencing the perceived usability of wearable chair exoskeleton with market segmentation: A structural equation modeling and K-means clustering approach. Int. J. Ind. Ergon. 2023, 93, 103401. [Google Scholar] [CrossRef]
  32. Ong, A.K.; Prasetyo, Y.T.; Lagura, F.C.; Ramos, R.N.; Sigua, K.M.; Villas, J.A.; Young, M.N.; Diaz, J.F.; Persada, S.F.; Redi, A.A. Factors affecting intention to prepare for mitigation of “The big one” earthquake in the Philippines: Integrating protection motivation theory and extended theory of planned behavior. Int. J. Disaster Risk Reduct. 2021, 63, 102467. [Google Scholar] [CrossRef]
  33. Pérez-Domínguez, L.; Garg, H.; Luviano-Cruz, D.; García Alcaraz, J.L. Estimation of linear regression with the dimensional analysis method. Mathematics 2022, 10, 1645. [Google Scholar] [CrossRef]
  34. Ikotun, A.M.; Ezugwu, A.E.; Abualigah, L.; Abuhaija, B.; Heming, J. K-means Clustering Algorithms: A comprehensive review, variants analysis, and advances in the era of Big Data. Inf. Sci. 2023, 622, 178–210. [Google Scholar] [CrossRef]
  35. Chaudhry, M.; Shafi, I.; Mahnoor, M.; Vargas, D.L.; Thompson, E.B.; Ashraf, I. A systematic literature review on identifying patterns using unsupervised clustering algorithms: A data mining perspective. Symmetry 2023, 15, 1679. [Google Scholar] [CrossRef]
  36. Sarker, K.U.; Saqib, M.; Hasan, R.; Mahmood, S.; Hussain, S.; Abbas, A.; Deraman, A. A ranking learning model by K-means clustering technique for web scraped movie data. Computers 2022, 11, 158. [Google Scholar] [CrossRef]
  37. Kuo, R.J.; Zulvia, F.E. Cluster analysis using a gradient evolution-based K-means algorithm. In Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, 24–29 July 2016. [Google Scholar]
  38. Rane, N.L.; Choudhary, S.; Achari, A. Enhancing customer loyalty through quality of service: Effective strategies to improve customer satisfaction, experience, relationship, and engagement. Int. Res. J. Mod. Eng. Technol. Sci. 2023, 5, 427–452. [Google Scholar]
  39. Fausto, R.F. The Gender Pay Gap and How We Can Overcome It. Available online: https://www.pids.gov.ph/details/news/in-the-news/the-gender-pay-gap-and-how-we-can-overcome-it (accessed on 26 May 2024).
  40. Chua, K.K.; Limkin, L.; Nye, J.; Williamson, J.G. Urban-rural income and wage gaps in the Philippines: Measurement error, unequal endowments, or factor market failure? Philipp. Rev. Econ. 2015, 2, 1–21. [Google Scholar]
  41. He, S.Y.; Kuo, Y.-H.; Sun, K.K. The spatial planning of public electric vehicle charging infrastructure in a high-density city using a contextualised location-allocation model. Transp. Res. Part A Policy Pract. 2022, 160, 21–44. [Google Scholar] [CrossRef]
  42. Hsu, C.-W.; Fingerman, K. Public electric vehicle charger access disparities across race and income in California. Transp. Policy 2021, 100, 59–67. [Google Scholar] [CrossRef]
  43. Pandiyan, P.; Saravanan, S.; Usha, K.; Kannadasan, R.; Alsharif, M.H.; Kim, M.-K. Technological advancements toward smart energy management in Smart Cities. Energy Rep. 2023, 10, 648–677. [Google Scholar] [CrossRef]
  44. Buhmann, K.M.; Criado, J.R. Consumers’ preferences for electric vehicles: The role of status and reputation. Transp. Res. Part D Transp. Environ. 2023, 114, 103530. [Google Scholar] [CrossRef]
  45. Parent, M.C. Masculinity contingency and consumer attitudes toward electric vehicles. Psychol. Men Masc. 2023. Advance online publication. [Google Scholar] [CrossRef]
  46. Dundes, L. “Even more than that, Men Love Cars”: “Car guy” memes and hegemonic masculinity. Front. Sociol. 2023, 7, 1034669. [Google Scholar] [CrossRef] [PubMed]
  47. Cahigas, M.M.; Prasetyo, Y.T.; Persada, S.F.; Ong, A.K.; Nadlifatin, R. Understanding the perceived behavior of public utility bus passengers during the era of COVID-19 pandemic in the Philippines: Application of social exchange theory and theory of planned behavior. Res. Transp. Bus. Manag. 2022, 45, 100840. [Google Scholar] [CrossRef] [PubMed]
  48. Lashari, Z.A.; Ko, J.; Jang, J. Consumers’ intention to purchase electric vehicles: Influences of user attitude and perception. Sustainability 2021, 13, 6778. [Google Scholar] [CrossRef]
  49. Mesimäki, J.; Lehtonen, E. Light Electric Vehicles: The views of users and non-users. Eur. Transp. Res. Rev. 2023, 15, 33. [Google Scholar] [CrossRef]
  50. German, J.D.; Ong, A.K.; Redi, A.A.; Prasetyo, Y.T.; Robas, K.P.; Nadlifatin, R.; Chuenyindee, T. Classification modeling of intention to donate for victims of Typhoon odette using Deep Learning Neural Network. Environ. Dev. 2023, 45, 100823. [Google Scholar] [CrossRef]
Figure 1. Total EV sales in Asia for 2023 adapted from Terasawa and Tiberghien [3]. Note: y-axis represents the brand of EV; x-axis represents the percentage in sales.
Figure 1. Total EV sales in Asia for 2023 adapted from Terasawa and Tiberghien [3]. Note: y-axis represents the brand of EV; x-axis represents the percentage in sales.
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Figure 2. Research process diagram.
Figure 2. Research process diagram.
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Figure 3. Elbow method plot.
Figure 3. Elbow method plot.
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Figure 4. Clustering market segmentation.
Figure 4. Clustering market segmentation.
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Figure 5. Cluster visualization based on gender.
Figure 5. Cluster visualization based on gender.
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Figure 6. Cluster visualization based on age.
Figure 6. Cluster visualization based on age.
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Figure 7. Cluster visualization based on the type of residence.
Figure 7. Cluster visualization based on the type of residence.
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Figure 8. Cluster visualization based on car ownership.
Figure 8. Cluster visualization based on car ownership.
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Figure 9. Cluster visualization based on car insurance.
Figure 9. Cluster visualization based on car insurance.
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Uy, J.R.R.; Ong, A.K.S.; De Guzman, D.M.B.; Dela Cruz, I.T.; Dela Cruz, J.C. Consumer Segmentation and Market Analysis for Sustainable Marketing Strategy of Electric Vehicles in the Philippines. World Electr. Veh. J. 2024, 15, 301. https://doi.org/10.3390/wevj15070301

AMA Style

Uy JRR, Ong AKS, De Guzman DMB, Dela Cruz IT, Dela Cruz JC. Consumer Segmentation and Market Analysis for Sustainable Marketing Strategy of Electric Vehicles in the Philippines. World Electric Vehicle Journal. 2024; 15(7):301. https://doi.org/10.3390/wevj15070301

Chicago/Turabian Style

Uy, John Robin R., Ardvin Kester S. Ong, Danica Mariz B. De Guzman, Irish Tricia Dela Cruz, and Juliana C. Dela Cruz. 2024. "Consumer Segmentation and Market Analysis for Sustainable Marketing Strategy of Electric Vehicles in the Philippines" World Electric Vehicle Journal 15, no. 7: 301. https://doi.org/10.3390/wevj15070301

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