Consumer intentions to purchase battery electric vehicles in Korea

Kim, Jae Hun; Lee, Gunwoo; Park, Ji Young; Hong, Jungyeol; Park, Juneyoung

doi:10.1016/j.enpol.2019.06.028

Cited by 44 publications

(22 citation statements)

References 15 publications

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“…Having considered the fact that the price of BEV in the Indonesian market is still not affordable to the majority of existing car owners, the target customers coming from senior level professionals in Indonesia were selected to participate as interviewees. This selection is in accordance with the previous studies on the characteristics of early adopters of EV, namely highly educated [8,74,75] and with a high income [74][75][76][77].…”

Section: Conclusion and Further Researchsupporting

confidence: 88%

“…In an effort to have an early prediction of consumers' purchase intentions towards BEV, some target customers consisting of senior-level executives and higher from various industries in Indonesia were invited as interviewees. These senior-level executives are chosen based on previous studies on early adopters of EV, namely highly educated [8,74,75] and with a high income [74][75][76][77] so that they have purchasing power to purchase a BEV evaluated in this study. Beside the ability to buy a BEV, the invited interviewees should have the job to be done (JTBD) that can be fulfilled by a BEV.…”

Section: Data Collectionmentioning

confidence: 99%

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Predicting Purchase Intention towards Battery Electric Vehicles: A Case of Indonesian Market

Febransyah¹

2021

WEVJ

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The emergence of electric vehicles (EV) is inevitable. In Indonesia, EVs in various forms have been introduced to the market. However, the adoption of EV in the Indonesian market is still negligible. The purpose of this paper is to make an early prediction of consumers’ purchase intentions towards EV, particularly battery electric vehicles (BEV), in Indonesia. A multi-criteria decision model based on the analytic network process (ANP) approach has been proposed. There are several main criteria used to explain the purchase/don’t purchase decision towards BEV, namely functionality, emotion, cost of ownership, and car identity. Through a series of pairwise comparisons involving a number of target customers of senior level professionals, their purchase intentions towards BEV have been predicted. The results of this study show that these early wealthy, highly educated consumers have a moderate preference towards purchasing BEV. Their intention to purchase is influenced by criteria as follows: emotion (42.64%), functionality (25.94%), car identity (21.87%), and cost of ownership (9.55%). Even though the invited target customers do not represent the mass market, the findings of this study could help BEV makers in Indonesia choose who the early adopters are and find the BEV product-market fit in order to accelerate the adoption of electric vehicles.

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Section: Conclusion and Further Researchsupporting

confidence: 88%

Section: Data Collectionmentioning

confidence: 99%

Predicting Purchase Intention towards Battery Electric Vehicles: A Case of Indonesian Market

Febransyah¹

2021

WEVJ

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“…Liu et al [55] found that the customer experience is the main decisive factor in buying BEVs. Kim et al [56] concluded that customers with good driving experiences and knowledge are more likely to buy BEVs in Korea. Li et al [57] found in their study that family factors, such as scale, income, and location, can influence consumers' choice of BEVs.…”

Section: E Factors Influencing Consumers' Choice Of Bevsmentioning

confidence: 99%

A Study on Selection Strategies for Battery Electric Vehicles Based on Sentiments, Analysis, and the MCDM Model

Ren

Sun

Yuan

2021

Mathematical Problems in Engineering

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Under the goal of carbon peak and carbon neutrality, developing battery electric vehicles (BEVs) is an important way to reduce carbon emissions in the transportation sector. To popularize BEVs as soon as possible, it is necessary to study selection strategies for BEVs from the perspective of consumers. Therefore, the Latent Dirichlet Allocation (LDA) model based on fine-grained sentiment analysis is combined with the multi-criteria decision-making (MCDM) model to assess ten types of BEV alternatives. Fine-grained sentiment analysis is applied to find the vehicle attributes that consumers care about the most based on the word-of-mouth data. The LDA model is suggested to divide topics and construct the indicator system. The MCDM model is used to rank vehicles and put forward the corresponding optimization path to increase consumer purchases of BEVs in China. The results show that (a) via the LDA model based on fine-grained sentiment analysis, attributes that consumers care most about are divided into five topics: dynamics, technology, safety, comfort, and cost; (b) based on the DEMATEL technique, the dimensions in the order of importance are as follows: safety, technology, dynamics, comfort, and cost; (c) the price is the most important criteria that affect customers’ satisfaction by the DANP model; and (d) based on the VIKOR model, the selection strategies present that Aion S is highlighted as the best choice, and the optimization path is discussed to promote the performance of BEVs to increase customers’ satisfaction. The findings can provide a reference for improving the sustainable development of the automobile industry in China. The proposed framework serves as the basis for further discussion of BEVs.

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“…Kim et al [ 20 ] in their study in Korea examining consumer intentions for purchasing an electric vehicle found that prior experience in driving electric vehicles, along with factors like number of vehicles in the household, educational achievement, availability of parking, and perception of government incentives significantly affect consumers’ intentions for purchasing electric vehicles. Sierzchula et al [ 21 ] in their study in 30 countries found that the electric vehicle market share in different countries was positively correlated with financial incentives, availability of charging infrastructure, and local production.…”

Section: Introductionmentioning

confidence: 99%

Predicting Electric Vehicle (EV) Buyers in India: A Machine Learning Approach

Dixit¹,

Singh²

2022

Rev Socionetwork Strat

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Electric mobility has been around for a long time. In recent years, with advancements in technology, electric vehicles (EVs) have shown a new potential to meet many of the challenges being faced by humanity. These challenges include increasing dependence on fossil fuels, environmental concerns, challenges posed by rapid urbanization, urban mobility, and employment. However, the adoption of electric vehicles has remained challenging despite consumers having a positive attitude toward EVs and big policy pushes by governments in many countries. Marketers from the electric vehicle (EV) industry are finding it difficult to identify genuine buyers for their products. In this context, the present study attempts to develop a machine learning model to predict whether a person would “Buy” or “Won’t Buy” an electric vehicle in India. To develop the model, an exploration of EV context was done first by conducting a text analysis of online content relating to electric vehicles. The objective was to find frequently occurring words to gain a meaningful understanding of the consumer’s interests and concerns relating to electric vehicles. The machine learning model indicates that age, gender, income, level of environmental concerns, vehicle cost, running cost, vehicle performance, driving range, and mass behavior are significant predictors of electrical vehicle purchase in India. The level of education, employment, and government subsidy are not significant predictors of EV uptake.

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Consumer intentions to purchase battery electric vehicles in Korea

Cited by 44 publications

References 15 publications

Predicting Purchase Intention towards Battery Electric Vehicles: A Case of Indonesian Market

Predicting Purchase Intention towards Battery Electric Vehicles: A Case of Indonesian Market

A Study on Selection Strategies for Battery Electric Vehicles Based on Sentiments, Analysis, and the MCDM Model

Predicting Electric Vehicle (EV) Buyers in India: A Machine Learning Approach

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