Adoption of electric vehicles: Which factors are really important?

Higueras-Castillo, Elena; Guillén, Alberto; Herrera, José Benavente; Liébana‐Cabanillas, Francisco

doi:10.1080/15568318.2020.1818330

Cited by 73 publications

(44 citation statements)

References 92 publications

(130 reference statements)

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“…Furthermore, three extracted marketing characteristics, including "cheaper electricity tariff," "cheaper maintenance fee," and "a cost-effective price of car payment, " can be categorized as the vehicle subsystem [10] and make an "affordable" impression on consumers; then, the last one is the most crucial among other design characteristics. In addition, both "friendly" and "affordable" impressions can be viewed as incentives that are the reliable predictors of purchase intention of EVs [20]. Then, "Having the function of autopilot" and "having the technique of Internet of vehicles" belong to electronic onboard subsystem [10], "constructing a live circle of pure electric" and "government subsidies" are next and vests in the "infrastructure subsystem [10]; then, "Having the technique of Internet of vehicles" and "government subsides" are more important than the other marketing characteristics and make consumers feel "intelligent" and "affordable individually."…”

Section: Discussionmentioning

confidence: 99%

Exploring the O2O (online to offline) marketing design of electric vehicles based on consumers’ emotions

Lin

Shen

2022

SN Appl. Sci.

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In this study, we present how electric car marketing makes a practical appeal to elicit consumers’ emotions by its unique design characteristics to determine the essential attractive features that are beneficial for promoting the sales of this type of promising vehicle. Thus, we investigate why consumers prefer electric vehicles to fuel cars and what specific characteristics make them willing to own electric vehicles. Furthermore, the preference-based research method is applied to reveal the underlying meaning behind the appeal of electric cars in this study. Hence, this study uses the evaluation grid method to collect professional concepts and establish a semantic hierarchical diagram of preferences for electric vehicles. Then, quantification theory type I is used to analyze the questionnaire survey of consumers’ preferences. The three best original evaluation items of electric cars are determined, including “scenario-based,” “accessible,” and “visionary.” This study provides essential information for managers, designers, and researchers of electric vehicles. The study also contributes to the design, marketing, and consumer psychology.

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Section: Discussionmentioning

confidence: 99%

Exploring the O2O (online to offline) marketing design of electric vehicles based on consumers’ emotions

Lin

Shen

2022

SN Appl. Sci.

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“…The battery life, purchase cost, driving range, and charging times were considered product-level factors [20]. Higueras-Castillo et al (2021) studied product factors like minimum range, charging time, low noise, acceleration, and context factors like price, perceived benefit, incentives, and infrastructure [21]. Higueras-Castillo et al (2021) surveyed 404 prospective EV consumers in Spain about their beliefs, EV attitudes, and purchase intention towards EVs [21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Higueras-Castillo et al (2021) studied product factors like minimum range, charging time, low noise, acceleration, and context factors like price, perceived benefit, incentives, and infrastructure [21]. Higueras-Castillo et al (2021) surveyed 404 prospective EV consumers in Spain about their beliefs, EV attitudes, and purchase intention towards EVs [21]. Their research findings reveal that driving range, government incentives, and reliability are the most important predictors of purchase intention.…”

Section: Literature Reviewmentioning

confidence: 99%

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Factors Influencing Consumers’ Purchase Intention towards Electric Two-Wheelers

Jayasingh¹,

Girija²,

Sivakumar

2021

Sustainability

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The demand for electric vehicles (EVs) has significantly increased in recent years, but it represents only a tiny percentage of the total new vehicles sold globally. In 2021, the total sales of electric two-wheelers were less than 1% only. Therefore, the adoption of an electric two-wheeler (E2W) needs to be studied. This research develops a behavioral electric two-wheelers adoption intention model. The present study aimed to identify the factors that influence consumers’ intentions to adopt electric two-wheelers. The questionnaire method was employed, and 182 valid responses were collected. The partial least squares structural equation modeling (PLS-SEM) was used to test the research hypothesis. The empirical results indicate that environmental concern, perceived economic benefit, charging infrastructure, and social influence significantly impact consumers’ attitudes towards electric two-wheelers. Attitude also significantly affects the consumer’s intention to purchase an electric two-wheeler. Perceived economic benefits were found to be the main factor influencing consumers to purchase electric two-wheelers. The findings of this research also show that women are more inclined towards purchasing electric two-wheelers than men. These results offer useful information for governments and electric-two-wheelers companies to gain a better understanding of consumer behavior towards purchasing electric two-wheelers.

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“…The average price point of $3100, charging time of 35 min, and vehicle driving range of 401 KM were identified as the tipping points to achieve mainstream EV adoption in India [ 22 ]. Higueras-Castillo et al [ 23 ] in their study to find factors that affect electric vehicle purchase intentions in Spain conclude that driving range, financial incentives, and vehicle reliability are the most important predictors of the purchase intention. Bennett and Vijaygopal [ 24 ] found that a weak link exists between attitude and willingness to purchase an electric vehicle.…”

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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Adoption of electric vehicles: Which factors are really important?

Cited by 73 publications

References 92 publications

Exploring the O2O (online to offline) marketing design of electric vehicles based on consumers’ emotions

Exploring the O2O (online to offline) marketing design of electric vehicles based on consumers’ emotions

Factors Influencing Consumers’ Purchase Intention towards Electric Two-Wheelers

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

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