Understanding Consumer Adoption of Light-Duty Electric Vehicles: An Interdisciplinary Literature Review

Abstract

The present paper focuses on the adoption of light-duty electric vehicles (EVs), encompassing a comprehensive review and summary analysis of the existing research in the field to identify forthcoming research directions. An emerging interest in understanding factors that influence and accelerate the widespread adoption of EVs has produced insightful research from a variety of disciplines including transportation, natural resources, and social sciences. Considering the vast variety of research endeavors, it is important to establish a baseline understanding of consumer EV adoption to provide an agenda for future research needed to advance current knowledge. This review focuses on aggregating insights gained from 437 research articles across disciplines. Specifically, we systematically reviewed journal articles on EV adoption with the goal of identifying theories used to understand EV adoption, methods by which data was collected, and key findings related to perceived advantages of, and barriers to, EV adoption. Our results help draw an overarching picture of the state of the art in research on EV adoption across disciplines, and identify remaining gaps. We conclude by showing an evolution of methodological practices in EV research and suggesting potential avenues for future research that may be of interest to both academics and practitioners interested in accelerating the widespread adoption of electrified transportation by consumers.

Keywords

sustainability and resilience transportation and sustainability alternative transportation fuels and technologies consumer choice/preferences electric and hybrid-electric vehicles

The adoption of electrified transportation, particularly among the light-duty electric vehicle (EV) segment, holds the potential to reduce greenhouse gas emissions, while improving health, equity, and air quality for consumers ( 1 ). Thus, understanding whether, when, and how the consumer adoption of light-duty EVs can be fostered represents a crucial research agenda across disciplines. For instance, advances in technology characterize an important part of increasing the usability and accessibility of EVs ( 2 ). Advances in charging infrastructure development and modeling, including improved battery technology as well as wireless and in-motion charging, further hold the potential to promote a ubiquitous charging experience for consumers and reduce upfront purchase prices ( 3 ). Moreover, many people have an interest in life cycle assessments and analyses of vehicles and battery impact on the environment throughout the entire lifecycle, such as the reduction of greenhouse gas emissions and pollutants ( 4 ). In addition, understanding the role and perceptions of various stakeholders—consumers, agencies, public policymakers, and manufacturers—throughout the adoption process represents a multi-disciplinary research agenda across fields, often related to social sciences and economics ( 5 ).

Methodology

To develop a baseline understanding of the current state of research in EV adoption literature across disciplines, this review draws on an analysis of 437 research articles from the years 1982–2021 that have been identified through a strategic search of the literature. The present interdisciplinary literature review was conducted utilizing academic databases including JSTOR and EBSCO. First, selected keywords were employed to compile a first inventory of interdisciplinary research articles, focused on a combination of the keywords “adoption,”“consumer,”“perception,”“electric,”“electric vehicles,”“electric transportation,” and “light duty electric vehicles.” Second, snowball sampling—a popular technique in which researchers investigate references of a given paper to identify additional, related research resources—was then utilized on the articles identified through keywording to complement the strategic search approach and generate the full inventory of research papers, resulting in n = 437 ( 6 ).

Next, the identified articles were systematically reviewed by three research assistants and coded with the intention of identifying theories used to understand EV adoption and methods by which data were collected, and summarizing key findings related to perceived consumer advantages of, and barriers to, EV adoption. Word search, document search, and keyword search methods were employed to identify relevant aspects related to the aforementioned categories.

Table 1 represents a high-level overview of the research articles compiled from each field of study, also visualized in Figure 1. To define the field of study, we iterated through the names of the 437 journals. Most journal names were assumed to include the field closely related to their orientation in the name itself, which was then coded, and the field names were stored in another column for those journals. Indeed, our analysis confirms that the study of consumer EV adoption represents an interdisciplinary research agenda which spans across various fields. Specifically, it was identified that journal articles from the field of Transportation Research were a dominant discipline, representing 27.23% of the total journals. This was followed by journals from the field of Energy Research comprising 16.28%. Journals in Environmental Science follow the tally with 14.19%. Finally, Social Sciences, along with Marketing and Management journals, were found to research EV adoption, representing 12.81%, and Psychology-related journals represented 11.21% of all research journals. Finally, journals relating to Renewable and Sustainable Energy comprised 7.09%, and Traffic Psychology and Behavior 4.80%. Economics and other Social Sciences represented 5.71% of journals, with 0.68% classified under Other Engineering.

Table 1.

Research on Electric Vehicle Adoption Represents an Interdisciplinary Research Agenda

Field	Number of articles	Percentage (%)
Transportation	119	27.23
Energy	71	16.28
Environmental science	62	14.19
Marketing research	56	12.81
Psychology	49	11.21
Renewable and sustainable energy	31	7.09
Traffic psychology and behavior	21	4.80
Economics	16	3.66
Other social sciences	9	2.05
Other engineering	3	0.68
Total	437	100

Figure 1.

Overview of number of research articles per field in % (as outlined in Table 1).

The following discussion comprising the remainder of this systematic review is structured as follows. First, a review of the theoretical bases for EV adoption research is explored. Second, the most applied data collection methods are described and enumerated to guide the flow of the present paper, including sections detailing prevalent quantitative and qualitative study designs. Third, key findings related to consumers’ perceived advantages of, and barriers to, EV adoption found in our sample of research articles are summarized. This review concludes with identified gaps in the research on consumer perceptions and adoption of EVs to provide an agenda for targeted future research needed to advance current knowledge.

Discussion of Findings

Theoretical Approaches

The 437 research articles were reviewed and coded for the theoretical approaches employed. It was identified that commonly used theories span across different fields of study, with related fields of study tending to employ similar theories to ground their research endeavor.

Theory of Planned Behavior (TPB)

First, we find that research based on the Theory of Planned Behavior (TPB) by Ajzen ( 7 ) spanned across a variety of fields of study which include marketing, renewable and sustainable energy, clean energy, and social science among many others (e.g., Barbarossa et al., and Degirmenci and Breitner) ( 8 , 9 ). Because of its immediate relevance to the study of adoption of EVs, our analysis revealed that 27.17% of the articles included TPB. This theory is used to explain how consumers form conscious thoughts which then result in behavior. Deriving from the theory of reasoned action, TPB is a well-validated model to interpret social behaviors ( 10 – 13 ). According to TPB, the six main determining factors that result in behavior are: consumer attitudes, behavioral intention, subjective norms, social norms, perceived power, and behavioral control ( 7 , 14 ). Albeit the theory originally takes six different components into consideration that consumers consciously refer to and that can have an impact on their behavior, we find that many researchers choose to focus on selected constructs originally suggested or add others ( 14 ). For instance, the theory can be applied to assist in the understanding of people’s EV purchasing behavior. Based on findings from prior research, those serve as an indicator of the consumer’s behavior ( 15 ). For example, the term “subjective norm” is utilized in the context of EV adoption to describe the influential effects of social pressure to perform or not perform the behavior in mind, such as purchasing an EV. Moreover, perceived behavioral control would characterize how easy it is to perform the action from a person’s perception, such as how easy it would be to purchase an EV. Importantly, it was found that relevant experiences and positive anticipation of difficulties may be able to help increase such perceived behavioral control from a consumer’s perspective. In general, it was found that a more positive attitude toward the outcome of an action, greater pressure from important others, and greater behavioral control result in a strong intent to carry out the action ( 10 – 13 ).

Identity Theory

The second most common theory identified in our sample was Identity Theory. This theory was utilized by 18.08% of all articles (e.g., Barbarossa et al., Egbue and Long, and Heffner et al.) ( 8 , 15 – 18 ). According to Identity Theory, a consumer’s self-identity represents the main factor which drives behavior ( 19 ). Adopting a sociological approach to identity, self-identity can thus be defined as a group of meanings allocated to specific individuals and the roles they occupy in society, including unique ways in which they view themselves in these roles ( 19 ). A consumer’s self-identity was therefore identified as a predictor of attitudes and consumption behaviors, because of the need of individuals to preserve consistency with their identity through behavioral actions ( 20 ). In research related to EV adoption, Identity Theory may be used to explain how consumers’ identity, for example their environmental identity or their relation to sustainability values, affects their decision-making process in considering the purchase of an EV.

Theory of Ethics

Third, the Theory of Ethics was mentioned in 14.02% of the research papers (e.g., Barbarossa et al., Degirmenci and Breitner, and Yuen et al.) ( 8 , 21 , 22 ). According to Hunt and Vitell’s ( 23 ) Theory of Ethics, consumers often rely on the moral philosophies of “teleology” (which may be comprehended as a concept of ethics that originates duty or ethical responsibility from what an individual deems a preferred end goal to attain), and “deontology” (often described in the literature as theories in the field of ethics which illustrate the relationship between “duty” and the “morality” of human actions) in shaping their attitudes in circumstances related to ethical issues ( 23 ). With respect to the purchase and adoption of electric cars that may be perceived as eco-friendly and good for the planet, one may assume that a moral objective to treat the planet respectfully may be at play. In recent studies, it was observed that the more consumers perceive themselves as green consumers, the more they will develop positive attitudes toward the adoption of electric cars, which they believe to be eco-friendly, resulting in an increased intention to purchase these products ( 8 ).

Value–Belief–Norm Theory (VBN)

Fourth, the Value–Belief–Norm (VBN) theory was utilized in 3.5% of the research papers (e.g., Encarnação et al. and Hardman et al.) ( 24 , 25 ). From this theory’s perspective, it is assumed that consumer values affect consumer behavior indirectly. Thus, literature suggests that beliefs and personal norms play an additional role within this process. Specifically, the theory proposes that values affect behavior via a “norm activation process” ( 24 ). These norms emerge from feelings of obligation of the consumer to perform an action, such as, for example, behaving in an environmentally friendly way. With regard to EV adoption and environmental values, it can be noted that selected studies may show similarities between approaching adoption from the angle of the Theory of Ethics and the VBN Theory. This might be because prior research has shown that different types of behaviors which affect the environment including but not limited to car use and acceptability of transport policies can also emerge from personal values (see Encarnação et al. for a summary of literature) ( 24 ). If someone acknowledges that not acting pro-environmentally friendly will lead to negative consequences for the environment, then this belief forms the basis of the VBN Theory ( 24 ). It was demonstrated that the value of environmentalism may be a sufficient theoretical framework to predict the acceptability of a transport pricing policy, as well as the intention to reduce car usage ( 23 ). It was also found that VBN Theory may explain policy acceptability and intention to reduce vehicle usage ( 24 ).

Evolutionary Game Theory (EGT)

Finally, Evolutionary Game Theory (EGT) was utilized by no more than 1.1% of the total research articles. EGT describes an approach to investigating the potential interaction between two or more entities in a situation that is analyzed as involving a set of predetermined rules and outcomes ( 26 ). In the specific case of EV adoption, exemplary research suggests that the adoption of electrified transportation may follow a game theory principle, in which interactions between various “players” from different “sectors” are analyzed in a strategic way ( 26 ). Specifically, the theory proposes that co-evolving choices the consumer can affect—and in return are affected—by multiple effects, including, for example, public policies or social incentives, as well as consumers’ peers. Drawing on EGT, researchers have found interesting similarities concerning “successful behaviors,” which may spread faster within populations ( 26 ). As such, governmental subsidies with shared infrastructure costs, technological investment, and environmental activism were identified as important implementation sectors which may hold the potential to explain the overall adoption of EVs ( 26 ).

Other Theoretical Approaches

Other selected theories which were employed in less than 1% of journal articles of our sample have been summarized as “Other Theoretical Approaches.” In addition, our coding analysis revealed that numerous research articles did not ground their approach on an explicit theory, further emphasizing the need for a clear theoretical agenda moving forward. Table 2 represents an overview of the theoretical approaches used across EV research articles. Table 3 summarizes the main assumptions of each theory and illustrates similarities between the theories utilized to understand EV adoption.

Table 2.

Overview of Theoretical Approaches Used across Electric Vehicle Research Articles

Theory	Articles	Percentage (%)
Theory of Planned Behavior	119	27.17
Identity Theory	79	18.08
Theory of Ethics	61	13.96
Value–Belief–Norm Theory	15	3.5
Evolutionary Game Theory	5	1.1
Other Theoretical Approaches	54	12.35
No Explicit Theoretical Approach	104	23.84
Total	437	100

Table 3.

Overview of Theoretical Approaches and Their Main Assumptions

Theory	Main assumption
1. Theory of Planned Behavior	Consumer behavior is driven by their (planned) behavioral intentions.
2. Identity Theory	Consumer self-identity drives behavior.
3. Theory of Ethics	Consumer behavior is guided by moral philosophies/ethics.
4. Value–Belief–Norm Theory	Consumer values affect consumer behavior indirectly through values, beliefs, and norms.
5. Evolutionary Game Theory	Investigates potential interactions between two or more entities within a set of pre-determined rules and outcomes.

Methods of Data Collection

Next, our coding analysis focused on identifying the different methods of data collection that were employed across research endeavors with the goal of describing common trends in data collection approaches and identify gaps that can strategically result in future research agendas. In the following, we present the most commonly employed methods of data collection, distinguishing between qualitative and quantitative methods.

Qualitative Data Collection Methods

Qualitative research is commonly employed to uncover the structure, content, and perspectives of consumers and stakeholders in novel fields of research ( 27 ). Particularly, because the often-pioneering character of research investigating EV adoption in the first publication years of our sample, it is suggested that “quantitative research must be preceded by appropriate qualitative research” ( 28 ). However, it is important to caution that, in qualitative data collection methods, the purpose of generating insights from methods such as interviews (and utilizing them in the actual research) is often unstated. This is in line with a general review paper on research methodologies which states that only 27% of the papers that use interviews as a methodology mentioned an exact rationale for doing those interviews ( 10 ).

Focus Groups and Interviews

With regard to qualitative research approaches, our coding analysis revealed that focus groups represented the most commonly utilized qualitative method (11.91%). Exploratory research in the form of focus groups is indispensable when it comes to uncovering perspectives related to novel research fields. This was mirrored in the reason for choosing a qualitative approach given in selected research articles, as researchers ascertain that a quantitative questionnaire survey requires a certain level of understanding of the topic in hand and, therefore, to build a questionnaire, a solid grasp of the subject matter through qualitative research is assumed to be essential ( 29 ). While the field of EV adoption was a novel research prospect, some fundamental knowledge about how consumers generally perceive it, their awareness toward it, and the behavioral connotations were largely unknown in a vast majority of instances. Thus, to design effective questionnaires, qualitative research is assumed to play a key role in setting the template to carry out further research. The same approach is also reflected in Govindan and Hasanagic where the authors first conducted in depth interviews and then applied a quantitative research methodology to test the hypothesis in a numerical way ( 30 ). Another study suggests similar advantages, stating that “the richness of the comments, which come from real customers, make this technique highly useful” ( 28 ). For example, interviewees can be asked about their reasons for purchasing their current EV to understand the motivational factors for adoption (e.g., Haustein and Jensen) ( 29 ). Further, questions related to understanding consumer adoption—such as the benefits of ownership—can be explored to understand the advantages of owning a high-end EV and to draw conclusions about motivational factors and post-purchase experiences (e.g., He et al.) ( 13 ). Those insights often prove valuable in the confirmation stage and can influence future purchases and cause repeat purchases ( 30 ). Thus, the method allows for in-depth analysis of particular issues that are considered important by an interviewee, and also simultaneously limits bias among researchers in shaping the interview according to their preconceived notions. Nevertheless, a noteworthy concern persists because it does not always guarantee the comprehensive coverage of all relevant subject matter issues that ought to be investigated. The methodology was also found to lack robustness and credibility when it comes to the question of a comparative data analysis ( 31 ). Consequently, qualitative interviews may be criticized for involving the risk of bias because of the risk of poorly constructed questions or interview output ( 10 ). To tackle this problem, semi structured interviews may offer a valid alternative method.

Semi-Structured Interviews

In this review, semi-structured research interviews were found to be employed by 9.49% of the research articles. We identified that a common approach was to introduce a scenario, which was followed by an interview that was conducted after the participant had experienced the scenario (e.g., Jaiswal et al.) ( 32 ). For instance, an EV was given to consumers to test drive and their perceptions and feedback were surveyed in a qualitative manner during the interview ( 13 ). In this particular research, the researchers interviewed 40 end user subjects about their beliefs about EVs and, consequently, 167 test drives were performed with EVs to test the hypothesis. One of the core limitations in this kind of research is the possibility of accommodating a larger sample size. There might also be a self-selection bias involved, as research teams may chose the subjects through a common metric and not through a random selection ( 13 ). Commonly, the implementation of semi-structured interviews allows the researcher to investigate a set of predetermined questions and to create a meaningful conversation with the participants around them. This enables them to gather necessary information while allowing for depth and unforeseen narratives to the research ( 10 ). Generally, semi-structured interviews are appreciated for their ability to engage in deep conversation with a lot of flexibility, and their generative nature which holds the potential to stimulate new ideas ( 33 ). This technique may be particularly valuable in paving the way for developing quantitative research approaches to investigate consumer acceptance and EV adoption on a larger scale. It can further be elevated with quantitative research approaches to avoid bias. For example, the study in Young et al. uses a semi structured approach, it later uses Structural Equation Modeling to reduce bias in the collected data, thus ensuring a smoother transition from qualitative to quantitative methods ( 13 ).

To summarize, qualitative methodology can generate rich, deep, and detailed insight about novel topics but often falls short on preciseness and suffers from controlling bias which originates from human subjectivity. It can be concluded that, when applied properly, this method can be highly beneficial in many aspects.

Quantitative Data Collection Methods

Quantitative research lends itself well to attaining quantifiable, accumulated knowledge and achieving an understanding about the area of interest. Researchers often use quantitative methods to observe situations or events and how they affect people ( 11 ). As such, quantitative research aims to produce objective data that can provide insights through statistics and numbers ( 12 ).

Survey Research

Our coding analysis revealed that the most utilized quantitative method was survey research, which was represented in 61.73% of the journal articles identified. Surveys were commonly distributed online (e.g., Whitehead et al.), and very few studies were distributed by mail (e.g., Stryker and Burke) ( 34 , 35 ).

Stated Preference Survey

Specifically, our analysis revealed that surveys which included a stated preference component were popular across research endeavors. A stated preference component collects data through introducing choice scenarios in which respondents make hypothetical choices from a given set of alternatives (e.g., Malhotra et al. and Haley) ( 36 , 37 ). However, research also suggests that the stated preference method includes some hypothetical bias, often leading to notable incongruencies between the declared and observed behaviors of the participants ( 38 , 39 ). However, participants, when aligned with familiar context with the situation, often hold the potential to decrease the hypothetical bias ( 14 ). Similarly, in choice-based experiments included in surveys, researchers introduce participants to a hypothetical scenario, such as purchasing an EV with the opportunity to buy a PhotoVoltaic with or without a battery energy storage system in a bundle with the car (e.g., Gopal et al.), and then survey various quantitative attitudes, perceptions, and behavioral intentions to identify differences among scenarios ( 39 , 40 ). However, choice experiments are not without limitations. These include the simulation of real life purchase decisions that may differ from actual behavior, and there may be fewer opportunities to incorporate different variables because of the need to avoid making the decision overly complex.

Revealed Preference Survey

Revealed preferences, which refer to a focus on observed behaviors, are more congruent in assigning relevant attributes ( 39 ). For example, latest research that was published after 2021 incorporates a stated preference study, wherein the researchers included measures of socio-demographic factors, such as income, gender, and age of the EV, to explain the differences in people’s preferences ( 41 ). Because using a mixed logit model allows for more heterogeneity within individual preferences, they found no significant insight into the individual preferences for a specific group (sample size n = 456). The study acknowledged that other potentially important factors could influence charging choices but were not included in the survey. Thus, it can be concluded that self-report measures and revealed behaviors often show divergency.

Thus, a need to include additional modeling techniques to ensure considerations of reliability and validity emerges. While in the previous example, advanced quantitative methods were used to determine heterogeneity within the population, various other research combines hybrid choice modeling along with discrete choice to account for latent or unobserved variables to generate further insights using exploratory factor analysis ( 41 ). For example, a web-based survey on 1,546 Chinese consumers employed a similar technique to test several policy implications toward adoption ( 42 ). Similarly, another study using stated preference surveys after 2021 gained more reliability by implying specific evaluation methods for questionnaires such as using Cronbach’s alpha and composite reliability analysis. In the same paper, convergent validity is evaluated using average variance extracted and factor loadings which show that the questionnaire falls under acceptable internal reliability and convergent validity. This helps to better address and avoid the bias in stated preference surveys ( 43 ). Another study also used the Technology Acceptance Model framework with aspects related to “knowledge,”“beliefs,” and “intention.” Through direct and indirect path analysis, they found the interconnectedness among these factors implying a series of relationships that are otherwise complex to venture through ( 44 ).

Furthermore, important aspects of revealed preferences include capturing mass data that stems from regular drivers of conventional cars and is not a sample of conveniently selected EV drivers resulting in a self-selection bias ( 45 ). The research in Wang et al. further uses a post-consumer satisfaction survey of 1 million users to estimate purchasing behavior ( 46 ). This research used a matching algorithm with the intent to increase credibility to nonlinearities and to not rely on assuming the correct model specification. The researchers employed chi square and Mcnemer tests which largely capture the heterogeneity of the population as opposed to homogenous survey results. The researchers also note that, since EV purchases were at an early stage during 2017, the research endeavor cannot capture the constant evolution of the EV market in the future. Another study pursued the goal of studying charging behavior of EV drivers by using graphical modeling and a probabilistic approach to capture the patterns. This model was later applied to existing historical datasets and was found to be accurate with low root mean squared errors ( 47 ). Furthermore, using real-world climate and driver data, it was possible to account for range anxiety to a certain degree of precision wherein the researchers stated that increasing the power of home charging beyond the standard 15 A, 120 V circuit does not significantly enhance utility, whilst setting up more charging infrastructure could help drivers, even those who do not cover high mileage. However, often user-specific information such as gender, age, and other demographic information are missing in large datasets because of confidentiality issues, which results in revealed preference surveys not being able to control for the plethora of potentially confounding variables. Besides that, faulty data entries and data collusion are common risks related to large datasets, highlighting the importance of a filtering process to ensure robust results ( 48 ).

Secondary Data Analysis

In addition to primary data collection from respondents through quantitative or qualitative means, the analysis of existing data sets or secondary data represents a viable avenue for data collection and analysis and is utilized in several research articles related to EV adoption (e.g., Axsen et al.) ( 49 ). In the following section, specific examples of commonly utilized data analyses are summarized and their potential advantages and disadvantages evaluated.

Social Media Data

Various research articles within our sample have analyzed data from the social media network Twitter (now known as X), which allows for compiling large data sets of various consumer groups and analyzing microblog comments based on repeated frames, sentiments, or perceptions. Twitter is considered one of the most frequently used microblog services by consumers, with 328 million daily active users around the globe posting 500 million tweets per day up until 2017 ( 49 – 51 ). These data are useful to identify consumer tweets related to EVs, their adoption by consumers, and the barriers consumers voice toward their potential adoption. However, there are also disadvantages that emerge when utilizing Twitter data. For instance, research suggests that, when user demographics utilized for research are skewed, it is challenging to claim to encompass the population who are living in the desired geographic locations ( 51 ). As such, the demographics of Twitter’s user base are not necessarily representative of the overall population. In addition, participation across users within the platform varies: according to the PEW Research Center, 80% of tweets come from the top 10% most active users according to survey on 2019. In other words, when researchers examine a collection of tweets for their projects, this collection may present itself as opinions of a small subset of users, which is often shared in multiple ways by others. Sentiment analysis also causes problems when analyzing tweets. Tweets, because of their short and informal structure, make it difficult for sentiment analysis compared with longer text ( 52 ).

Further, data sets which can be retrieved from official sources have been utilized in 0.95% of the research articles and were related to EV adoption, including data sets such as census data (e.g., Bonges and Lusk, and Hansla et al.), the U.S. National Household Travel survey, or the California Household Travel survey (e.g., Jarvenpaa and Staples, and Kwon and Wen) ( 53 – 56 ). Table 4 provides an overarching summary of the methods of data collections that were most employed.

Table 4.

Overview of Methods of Data Collection

Type of method	Method of data collection	Subdivisions	Articles	Percentage (%)
Qualitative			93	100
	1. Focus groups		52	55.91
	2. Semi-structured research interviews		41	44.09
Quantitative			344	100
	1. Survey data		329	78.48
		Including choice experiments	31
		Including stated/revealed preference data	28
	2. Secondary data analysis			21.52
		Social media data	6
		Other	9

Findings about Consumer Adoption of EVs

Perceptions of EV Adoption

The analysis of the compiled research articles revealed that many papers focus on understanding consumers’ perceptions of EVs with particular regard to their attitudes toward EV adoption (e.g., Lin and Lu, and Adeya) ( 57 , 58 ). For example, we find that one of the major perceived advantages of using an EV is represented as the vehicle’s benefits for the environment in comparison with traditional vehicles. Adoption of electrified transportation is suggested to increase air quality and save natural resources, which is a positive attribute for consumers who are conscious about the environment ( 16 , 59 ). Another advantage was related to the cost savings in long-term operating costs resulting from paying for electricity versus paying for gas. Research proposes that the gap will reduce as EVs are becoming more and more cost competitive ( 52 ).

Another factor that was often demonstrated as a perceived advantage of EV ownership relates to the perception of an EV representing a status symbol for many ( 57 ). Specifically, prior research shows that owning and driving an EV was perceived as a higher-level status symbol than driving a conventional internal combustion engine vehicle ( 60 ). Further, it was demonstrated that people, particularly millennials, value the performance of an EV and are attracted by the new technology that these vehicles offer ( 61 ). This goes in line with other findings that suggest consumers perceive an EV as offering a superior driving experience, for example in comparison with conventional vehicles, and value the lower maintenance cost and maintenance work of batteries in comparison with servicing traditional engines ( 62 ). Consumers were also shown to feel safer when driving an EV because of the advanced technologies that are often employed in these vehicles ( 63 ).

Research further suggests that social influence—that is, the impact that friends, family, or neighbors have on the individual—plays a role in influencing adoption of EVs ( 60 ). A similar mechanism was demonstrated when investigating the impact of social media discussions and comments on individuals, suggesting that factors related to negative perceptions may discourage people from purchasing EVs, while supporting social circles may, on the other side, enhance an individual’s likelihood to adopt ( 64 ).

To gain a deeper understanding not only of the advantages, but particularly the factors hindering EV adoption, recent efforts among academics and practitioners lie in identifying potential barriers to widespread adoption. Indeed, out of the 437 papers, 80 papers (18.3%) explicitly focused on identifying perceived barriers to consumers’ light-duty EV adoption. Table 5 provides an overview of the major barriers identified. For instance, one of the major barriers found for light-duty EV adoption was related to the upfront purchase price of the vehicle (76.99%), which often lies above the purchase price of other non-EV vehicles (e.g., World Economic Forum and Kakilla) (65, 66). Besides that, the high cost of switching vehicles is also a potential barrier to adoption which was found to influence choices of consumers ( 67 ). Another barrier to adoption of EVs was technology, identified in 42.5% of the research articles. While specific consumer groups, such as millennials, may be attracted to the safety the technology provides as mentioned above, it was suggested that other consumer groups are particularly hesitant to utilize the new technology that EVs provide. This can be attributed to uncertainty and lack of knowledge about the new technology. However, with acceptance increasing rapidly over recent years, it was found that, in 2022, a new record of 10.6 million EV sales was made in comparison with the previous year globally ( 43 ). Since the system is evolving rapidly, the fundamental heuristics guiding an individual’s decision-making may not be subject to change, but the factors that influence their choices are changing fast.

Table 5.

Overview of Barriers to Electric Vehicle Adoption

Barrier	Example references	Articles	Percentage (out of n = 80)
Purchase price	Petrick (2002); Sierzchula et al. (2014); Krause et al. (2013); Wang et al. (2017); Yang et al. (2012)	61	76.25
Unwillingness to adopt new technology	Elliott et al. (2019); Kyriakidis et al. (2015); Bansal et al. (2016); Shareef et al. (2018)	34	42.5
Range anxiety	Bonges III and Lusk (68); Jung et al. (66)	28	35.03
Lack of infrastructure readiness	Krupa et al. (72); Caparello and Kurani (15)	18	22.51
Performance/reliability	Lim et al. (2015); Zhang et al. (2013); Graham-Rowe et al. (2012)	12	15.33
Complexity of technology	Jansson (5)	10	12.5
Social influence	Bearden et al. (1989)	5	6.25

Charging infrastructure was another barrier relevant to EV adoption. Similarly, with advances made in infrastructure, less time is required to charge the EV (whereas gas-powered vehicles do not have the option to continuously improve their charging infrastructure) (e.g., Yuen et al., Nguyen-Phuoc et al., and Christiaens et al.) ( 68 - 70 ). Similarly, the lack of connected charging infrastructure was identified as another barrier in 22.5% of the articles (e.g., Yuen et al., Nguyen-Phuoc et al., and Christiaens et al.) ( 68 – 70 ). Comparably, a barrier related to the limited range of EVs, termed “range anxiety,” was identified in 35% of the papers of our analysis. This includes the challenge for customers who would prefer to travel longer distances and must plan their trips accordingly, while potentially facing restrictions in distance and terrain (e.g., Nguyen-Phuoc et al. and Christiaens et al.) ( 68 , 69 ). Interestingly, when investigating detailed results, it can be stated that respondents indicated that their choice of battery range would depend on how long it took to recharge the battery, suggesting a synergistic effort, with a sentiment that improvements in both battery performance and infrastructure availability would be required for mass market acceptance of EVs ( 64 ). It was concluded that policymakers may consider other socio-technical barriers. For example, the Norwegian EV policies may have been successful because they focus on socio-technical barriers that are not as rhetorical as range anxiety. As such, research suggests they have resolved more primary barriers, as well as rhetorically “sweetened the deal” for those rhetorically opposed to EVs ( 71 ).

In addition to the aforementioned perceptions and barriers to adoption, demographics and individual difference variables were selectively found to be related to the adoption of EVs in a variety of research articles. For example, it was demonstrated that there are significant differences for EV adoption based on gender (e.g., Broadbent et al., Ranney et al., and Srinivasan and Reddy Athuru), indicating that males were more likely to indicate prior experience with EVs than females ( 78 , 72 – 75 ). Additionally, another finding suggests that men are 11.5% more interested in EVs than women ( 78 ). Further evidence from selected research suggests that age affects consumers’ likelihood of EV adoption. It was also found that lower levels of education correlate with a decrease in the intent to purchase an EV ( 76 ).

Furthermore, variables representing the individual characteristics of the consumer were found to affect the decisions which the individual takes into consideration while purchasing an EV (e.g., Gohil et al. and Polkinghorne), such as individual levels of green identity, environmental norms, individual values, personal innovativeness, behavioral control, and consumers’ awareness of consequences (e.g., Degirmenci and Breitner, Axsen et al., Adamowicz et al., Han et al., and National Research Council) ( 8 , 44 , 56 , 76 - 78 ). This directly relates to the most commonly identified theories to understand adoption and suggests that prior literature often relates consumer identity and individual difference variables to EV adoption.

Conclusion

By reviewing the interdisciplinary literature related to EV adoption, we provide an overview of the most-employed theories used to understand EV adoption from 1982 to 2021, methods by which data was collected, and key findings related to perceived advantages of, and barriers to, EV adoption. Based on coding analyses of 437 research articles, it can be concluded that, despite the recent increase in research on understanding consumer adoption of light-duty EVs, limitations in some of the research approaches and construct operationalizations still exist, paving the way for future research to increase our theoretical and managerial understanding of consumer adoption of EVs.

For example, with regard to the interdisciplinary interest in consumer adoption of EVs, it was found that novel theories could be employed to deepen our current understanding of the underlying motives of consumer adoption. While TPB and Identity Theory account toward the most common approaches, it will be important to identify novel theories from various fields to expand into a deeper and more strategic understanding of EV adoption from both psychological and managerial perspectives. Additionally, we found that studies stemming from diverse fields were often found to measure similar constructs in different ways, making a comparison of quantitative results across studies challenging because of the different operationalizations of similar constructs. Thus, academics may attempt to streamline quantitative measures to obtain replicable and generalizable knowledge across fields and time points. In addition, integrating novel moderators and mediators will be a fruitful way to strategically expand prior findings. In this regard, future research is needed that integrates interdisciplinary knowledge and insights from diverse fields, for example combining psychology and marketing findings with transportation research.

Investigating commonly employed methods for data collection further revealed that there is a recent growth of scenario-based research which could continue to benefit from incorporating not only stated preference elements, but also fully developed experimental designs to test for causal relationships. This could, for example, include experimental designs testing for consumers’ comparative perceptions of specific government incentives, the comparative effectiveness of differently framed marketing messages, or the differential perceptions of various charging technologies in relation to adoption. Researching acceptance of novel charging infrastructure represents another interesting avenue for future research. While most studies focus on stationary wired charging infrastructure, only elementary evidence is provided for consumer adoption of wireless or wireless in-motion charging (e.g., Hao et al. and Neckermann) and an investigation into consumer preferences and willingness to pay for various charging technologies would be fruitful ( 76 , 79 ).

In addition, perceptions and behavioral intentions are often utilized as the main outcome variable within research studies and modeling. However, given the novelty of the field of electric transportation and the fast-changing private vehicle models and price offerings, little is known with regard to what extent people’s explained perceptions will actually result in true purchasing behavior. This questions whether a move past stated preference data and a shift toward analyzing actual user data may be necessary after 2021 and beyond. A need for utilizing more market and sales data in combination with consumer stated choice data thus emerges. This will make more quantitative research possible which could help to reveal patterns of consumer behavior in the market. Therefore, as the evolution of EVs continues, it might be recommended to ensure more concentration on collecting data of users that will enable researchers to capture more heterogeneity in the population segment. Quantitative research using revealed preference methods increases steadily, with existing techniques that clearly highlight the barriers to adoption. To discover a better understanding of consumer purchase decisions, advanced techniques such as machine learning algorithms (e.g., K-means clustering), propensity score matching, and sophisticated regression models could bring potentially insightful results. Moreover, it is recommended that future research may be needed to explore research contributions above and beyond our current sample, from 2021 onwards, as they may offer further insights into the industry’s evolution and changing dynamics in each new decade. As the EV market sales are increasing and people at large are becoming increasingly aware of EV use, a shift toward more robust measures of research can be recommended as future research prospects.

Furthermore, to gain a deeper insight into the emerging innovations and how consumers are embracing them, it is imperative to persist with qualitative research methods. While we find that qualitative and quantitative research are both important components in identifying distinct dimensions within the research paradigm, the introduction of a novel technology in the market typically demands an initial qualitative investigation to achieve a profound understanding, subsequently complemented by quantitative research to authenticate and substantiate the results (see Figure 2). These methods enable us to delve into consumers’ perceptions, their apprehensions about new innovations, and how market dynamics respond to these concerns.

Figure 2.

Overview of diffusion of qualitative and quantitative research.

In closing, this review paper accommodates an understanding of the current dynamics of EV adoption research in academia and outlines a roadmap to which future research can refer. We specifically highlight which gaps exists and how different theories and methodologies fall short or do not explain current practices in understanding consumer adoption. However, despite identifying perceived advantages of EVs and perceived barriers to adoption within consumer segments, there still remains a paucity of understanding of the relative importance that owners—compared with non-owners—place on specific aspects related to EVs. For instance, nationally representative data collections including owners and non-owners could provide a first account of identifying (mis)perceptions between segments. In addition, examining the relative importance of each barrier for the individual consumer, as well as recognizing ways in which such barriers can be overcome, will be crucial to foster widespread EV adoption. Some studies show that innovative strategies which showcase the claimed environmental benefits of EVs may be able to help inform the debate about consumer misconceptions ( 71 ). Thus, drawing on previous literature on message framing, future research is needed to test the comparative effectiveness of strategic communication and marketing messages. Importantly, it can be noted that many studies suggest barriers, such as technological adoption, which it may not be possible to fully explain by the theories utilized (compare Table 3). Integrating novel theories thus can be an avenue for future research based on the synthesis of our findings. In addition, exploring the impact of variables with real-world implications, such as consumer test drives and their effect on adoption and purchases of EVs, may be fruitful ( 71 ). Altogether, we believe that the insights provided through our analyses provide a new baseline analysis that provides a targeted agenda for future research needed to advance current knowledge. Specifically, following new approaches may pave the way for strategic future research that includes interdisciplinary components and fosters interest from academics and practitioners across disciplines in accelerating widespread EV adoption. As cost is one of the major barriers to light-duty EV adoption, future studies including a focus on government incentives for EV purchase and tax rebates for owning EVs may be worthwhile and could be approached from the angle of EGT to account for the interplay of various “players” from sectors involved in the adoption-related portfolio, including state and national levels. Thus, understanding the roots of the cause of such barriers and the potential role that related variables such as demographics and perceptions play can help us further understand and advance our knowledge.

Footnotes

Author Contributions

The authors confirm contribution to the paper as follows: study conception and design: A. Graul, data collection: J. Dcosta; analysis and interpretation of results: J. Dcosta, S. Hasnat, A. Graul; draft manuscript preparation: J. Dcosta, S. Hasnat. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the NSF Engineering Research Center for Advancing Sustainability through Powered Infrastructure for Roadway Electrification (ASPIRE), National Science Foundation (NSF) under Grant No. 1941524.

ORCID iD

Antje R. H. Graul

References

Guo

Kontou

Disparities and Equity Issues in Electric Vehicles Rebate Allocation. Energy Policy, Vol. 154, 2021, p. 112291.

Whittle

Whitmarsh

Haggar

Morgan

Parkhurst

User Decision-Making in Transitions to Electrified, Autonomous, Shared or Reduced Mobility. Transportation Research Part D: Transport and Environment, Vol. 71, 2019, pp. 302–319.

Ahmad

Alam

M. S.

Asaad

Developments in xEVs Charging Infrastructure and Energy Management System for Smart Microgrids Including xEVs. Sustainable Cities and Society, Vol. 35, 2017, pp. 552–564.

Pellow

M. A.

Ambrose

Mulvaney

Betita

Shaw

Research Gaps in Environmental Life Cycle Assessments of Lithium Ion Batteries for Grid-Scale Stationary Energy Storage Systems: End-of-Life Options and Other Issues. Sustainable Materials and Technologies, Vol. 23, 2020, p. e00120.

Rezvani

Jansson

Bodin

Advances in Consumer Electric Vehicle Adoption Research: A Review and Research Agenda. Transportation Research Part D: Transport and Environment, Vol. 34, 2015, pp. 122–136.

Browne

Snowball Sampling: Using Social Networks to Research Non-heterosexual Women. International Journal of Social Research Methodology, Vol. 8, No. 1, 2005, pp. 47–60, https://doi.org/10.1080/1364557032000081663.

Icek Ajzen, The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, Vol. 50, No. 2, 1991, pp. 179–211.

Barbarossa

Beckmann

S. C.

De Pelsmacker

Moons

Gwozdz

A Self-Identity Based Model of Electric Car Adoption Intention: A Cross-Cultural Comparative Study. Journal of Environmental Psychology, Vol. 42, 2015, pp. 149–160.

Axsen

TyreeHageman

Lentz

Lifestyle Practices and Pro-Environmental Technology. Ecological Economics, Vol. 82, 2012, pp. 64–74.

10.

Young

J. C.

Rose

D. C.

Mumby

H. S.

Benitez-Capistros

Derrick

C. J.

Finch

Garcia

, et al. A Methodological Guide to Using and Reporting on Interviews in Conservation Science Research. Methods in Ecology and Evolution, Vol. 9, No. 1, 2018, pp. 10–19.

11.

Fuscaldo

Weibo has more MAUs in China than Twitter. Investopedia. 2017. https://www.investopedia.com/news/weibo-has-more-maus-china-twitter/. Accessed 3 March 2024.

12.

Yilmaz

Comparison of Quantitative and Qualitative Research Traditions: Epistemological, Theoretical, and Methodological Differences. European Journal of Education, Vol. 48, No. 2, 2013, pp. 311–325.

13.

Degirmenci

Breitner

M. H.

Consumer Purchase Intentions for Electric Vehicles: Is Green More Important Than Price and Range?

Transportation Research Part D: Transport and Environment, Vol. 51, 2017, pp. 250–260.

14.

Schläpfer

Fischhoff

Task Familiarity and Contextual Cues Predict Hypothetical Bias in a Meta-Analysis of Stated Preference Studies. Ecological Economics, Vol. 81, 2012, pp. 44–47.

15.

Heffner

R. R.

Kurani

K. S.

Turrentine

T. S.

Symbolism in California’s Early Market for Hybrid Electric Vehicles. Transportation Research Part D: Transport and Environment, Vol. 12, No. 6, 2007, pp. 396–413.

16.

Egbue

Long

Barriers to Widespread Adoption of Electric Vehicles: An Analysis of Consumer Attitudes and Perceptions. Energy Policy, Vol. 48, 2012, pp. 717–729.

17.

Sovacool

B. K.

Burke

Baker

Kotikalapudi

C. K.

Wlokas

(2017). New Frontiers and Conceptual Frameworks for Energy Justice. Energy Policy, Vol. 105, 2017, pp. 677–691.

18.

Wang

Cai

Liu

Yen

Wang

Sun

Zhou

Yin

Tang

Dobrucki

I. T.

Dobrucki

L. W.

Chaney

E. J.

Boppart

S. A.

Fan

T. M.

Lezmi

Chen

Yin

Cheng

Selective in Vivo Metabolic Cell-Labeling-Mediated Cancer Targeting. Nature Chemical Biology, Vol. 13, No. 4, 2017, pp. 415–424.

19.

Stryker

Burke

P. J.

(2000). The Past, Present, and Future of an Identity Theory. Social Psychology Quarterly, Vol. 22, No. 9, 2000, pp. 284–297.

20.

Arnocky

Stroink

DeCicco

Self-Construal Predicts Environmental Concern, Cooperation, and Conservation. Journal of Environmental Psychology, Vol. 27, No. 4, 2007, pp. 255–264.

21.

Axsen

TyreeHageman

Lentz

Lifestyle Practices and Pro-Environmental Technology. Ecological Economics, Vol. 82, 2012, pp. 64–74.

22.

Rezvani

Jansson

Bengtsson

Consumer Motivations for Sustainable Consumption: The Interaction of Gain, Normative and Hedonic Motivations on Electric Vehicle Adoption. Business Strategy and the Environment, Vol. 27, No. 8, 2018, pp. 1272–1283.

23.

Yuen

K. F.

Cai

Wang

Factors Influencing Autonomous Vehicle Adoption: An Application of the Technology Acceptance Model and Innovation Diffusion Theory. Technology Analysis & Strategic Management, Vol. 33, No. 5, 2021, pp. 505–519.

24.

Jakovcevic

Steg

Sustainable Transportation in Argentina: Values, Beliefs, Norms and Car use Reduction. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 20, 2013, pp. 70–79.

25.

Hardman

Shiu

Steinberger-Wilckens

Turrentine

Barriers to the Adoption of Fuel Cell Vehicles: A Qualitative Investigation into Early Adopters Attitudes. Transportation Research Part A: Policy and Practice>, Vol. 95, 2017, pp. 166–182.

26.

Encarnação

Santos

F. P.

Santos

F. C.

Blass

Pacheco

J. M.

Portugali

Paths to the Adoption of Electric Vehicles: An Evolutionary Game Theoretical Approach. Transportation Research Part B: Methodological, Vol. 113, 2018, pp. 24–33.

27.

Govindan

Hasanagic

A Systematic Review on Drivers, Barriers, and Practices Towards Circular Economy: A Supply Chain Perspective. International Journal of Production Research, Vol. 56, No. 1–2, 2018, pp. 278–311.

28.

Malhotra

N. K.

Birks

D. F.

Wills

P. A.

Marketing Research: An Applied Approach, 4th ed. Pearson Education, Harlow, UK, 2012.

29.

Charoennan

The Impact of In-Store Marketing Stimuli on Impulse Buying Behavior and Post-Purchase Evaluation: A Study of Self-Selected Mass Color Cosmetics in Bangkok. Semantic Scholar, 2016. https://www.semanticscholar.org/paper/The-impact-of-in-store-marketing-stimuli-on-impulse-Charoennan/53a3468468e0160b53e0f9cbd42066cac20ae4b6

30.

Haley

Low-Carbon Innovation from a Hydroelectric Base: The Case of Electric Vehicles in Québec. Environmental Innovation and Societal Transitions, Vol. 14, 2015, pp. 5–25.

31.

Bryman

Social Research Methods. Oxford University Press, UK, 2004.

32.

Ranney

M. L.

Meisel

Z. F.

Choo

E. K.

Garro

A. C.

Sasson

Morrow Guthrie

Interview-Based Qualitative Research in Emergency Care Part II: Data Collection, Analysis and Results Reporting. Academic Emergency Medicine, Vol. 22, No. 9, 2015, 1103–1112.

33.

Kakilla

Strengths and Weaknesses of Semi-Structured Interviews in Qualitative Research: A Critical Essay. Preprints 2021, 2021060491. https://doi.org/10.20944/preprints202106.0491.v1

34.

Han

Jeong

Wolfram

Log Analysis of Academic Digital Library: User Query Patterns. In Conference 2014 Proceedings, 2014, pp. 1002–1008.

35.

Louviere

J. J.

Flynn

T. N.

Carson

R. T.

Discrete Choice Experiments Are Not Conjoint Analysis. Journal of Choice Modelling, Vol. 3, No. 3, 2010, pp. 57–72.

36.

Adamowicz

Louviere

Williams

Combining Revealed and Stated Preference Methods for Valuing Environmental Amenities. Journal of Environmental Economics and Management, Vol. 26, No. 3, 1994, pp. 271–292.

37.

Hao

Boriboonsomsin

Barth

M. J.

Eco-Approach and Departure (EAD) Application for Actuated Signals in Real-World Traffic. IEEE Transactions on Intelligent Transportation Systems, Vol. 20 No. 1, 2018, pp. 30–40.

38.

Whitehead

J. C.

Weddell

M. S.

Groothuis

P. A.

Mitigating Hypothetical Bias In Stated Preference Data: Evidence From Sports Tourism. Economic Inquiry, Western Economic Association International, Vol. 54, No. 1, 2016, pp. 605–611.

39.

Priessner

Hampl

Can Product Bundling Increase the Joint Adoption of Electric Vehicles, Solar Panels and Battery Storage? Explorative Evidence from a Choice-Based Conjoint Study in Austria. Ecological Economics, 167, 2020, p. 106381.

40.

Polkinghorne

D. E.

Language and Meaning: Data Collection in Qualitative Research. Journal of Counseling Psychology, Vol. 52, No. 2, 2005, p. 137.

41.

Solvi Hoen

Díez-Gutiérrez

Babri

Hess

Tørset

Charging Electric Vehicles on Long Trips and the Willingness to Pay to Reduce Waiting for Charging. Stated Preference Survey in Norway. Transportation Research Part A: Policy and Practice, 175, 2023, 9. 103774. https://doi.org/10.1016/j.tra.2023.103774

42.

Wang

Xie

From Government to Market? A Discrete Choice Analysis of Policy Instruments for Electric Vehicle Adoption. Transportation Research Part A: Policy and Practice, Vol. 160, 2022, pp. 143–159. https://doi.org/10.1016/j.tra.2023.103774

43.

1 in 7 cars Sold Globally now is Electric. 2023. World Economic Forum. https://www.weforum.org/agenda/2023/03/ev-car-sales-energy-environment-gas/

44.

Jaiswal

Kant

Singh

P. K.

Yadav

Investigating the Role of Electric Vehicle Knowledge in Consumer Adoption: Evidence from an Emerging Market. Benchmarking: An International Journal, Vol. 29, No. 3, 2021, pp. 1027–1045. https://doi.org/10.1108/BIJ-11-2020-0579

45.

Weldon

Morrissey

Brady

O’Mahony

An investigation into usage 7 patterns of electric vehicles in Ireland. Transportation Research Part D: Transport and Environment, Vol. 43, No. 8 2016, pp. 207–225. https://doi.org/10.1016/j.trd.2015.12.013

46.

Powell

Cezar

G. V.

Rajagopal

Scalable probabilistic estimates of electric vehicle charging given observed driver behavior. Applied Energy, Vol. 309, 2022, p. 118382. https://doi.org/10.1016/j.apenergy.2021.11838212

47.

Sodenkamp

Wenig

Thiesse

Staake

Who can Drive Electric? Segmentation of Car Drivers Based on Longitudinal GPS Travel Data. Energy Policy, Vol. 130, 2019, pp. 111–129. 14 https://doi.org/10.1016/j.enpol.2019.03.04815

48.

Jabbari

Chernicoff

MacKenzie

Analysis of Electric Vehicle Purchaser Satisfaction and Rejection Reasons. Transportation Research Record, Vol. 2628, No. 1, 2017, pp. 110–119. https://doi.org/10.3141/2628-25122681.

49.

Srinivasan

K. K.

Reddy Athuru

Modeling Interaction between Internet Communication and Travel Activities: Evidence from Bay Area, California, Travel Survey 2000. Transportation Research Record, Vol. 1894,No. 1, 2004, pp. 230–240.

50.

Deutsch

A. L.

Ways Sina Weibo and Twitter For example. 2016. http://www.investopedia.com/articles/investing/022715/4-ways-sina-weibo-and-twitter-forexample.asp.

51.

Conway

M. W.

Salon

King

D. A.

Trends in Taxi Use and the Advent of Ridehailing, 1995–2017: Evidence from the US National Household Travel Survey. Urban Science, Vol. 2, No. 3, 2018, p. 79.

52.

Gohil

Vuik

Darzi

Sentiment Analysis of Health Care Tweets: Review of the Methods Used. JMIR Public Health and Surveillance, Vol. 4, No.2, 2018, p. e5789. https://doi.org/10.2196/publichealth.5789

53.

Durand

C. P.

Tang

Gabriel

K. P.

Sener

I. N.

Oluyomi

A. O.

Knell

Porter

A. K.

Hoelscher

D. M.

Kohl

H. W.

III . The Association of Trip Distance with Walking to Reach Public Transit: Data from the California Household Travel Survey. Journal of Transport & Health, Vol. 3, No. 2, 2016, 154–160.

54.

Probst

J. C.

Laditka

S. B.

Wang

J. Y.

Johnson

A. O.

Effects of Residence and Race on Burden of Travel for Care: Cross Sectional Analysis of the 2001 US National Household Travel Survey. BMC Health Services Research, Vol. 7, No. 1, 2007, pp. 1–13.

55.

Kumar

R. R.

Alok

Adoption of Electric Vehicle: A Literature Review and Prospects for Sustainability. Journal of Cleaner Production, Vol.d253, 2020, p. 119911.

56.

Beak

Kim

Maeng

Cho

Is the Environment-Friendly Factor Attractive to Customers when Purchasing Electric Vehicles? Evidence from South Korea. Business Strategy and the Environment, Vol. 29, No. 3, 2020, pp. 996–1006.

57.

Broadbent

G. H.

Drozdzewski

Metternicht

Electric Vehicle Adoption: An Analysis of Best practice and pitfalls for policy making from experiences of Europe and the US. Geography Compass, Vol. 12, No. 2, 2017. https://doi.org/10.1111/gec3.12358

58.

Axsen

Cairns

Dusyk

Goldberg

What Drives the Pioneers? Applying Lifestyle Theory to Early Electric Vehicle Buyers in Canada. Energy Research & Social Science, Vol. 44, 2018, pp. 17–30.

59.

Khurana

Kumar

V. R.

Sidhpuria

A Study on the Adoption of Electric Vehicles in India: The Mediating Role of Attitude. Vision, Vol. 24, No. 1, 2020, pp. 23–34.

60.

O’Neill

Moore

Kelleher

Brereton

Barriers to Electric Vehicle Uptake in Ireland: Perspectives of Car-Dealers and Policy-Makers. Case Studies on Transport Policy, Vol. 7, No. 1, 2019, pp. 118–127.

61.

Carpenter Tommy. Measuring & Mitigating Electric Vehicle Adoption Barriers. UWSpace, University of Waterloo, 2015. http://hdl.handle.net/10012/9096

62.

Gopal

A. R.

Park

W. Y.

Witt

Phadke

Hybrid-and Battery-Electric Vehicles Offer Low Cost Climate Benefits in China. Transportation Research Part D: Transport and Environment, Vol. 62, 2018, pp. 362–371.

63.

Ghasri

Ardeshiri

Rashidi

Perception Towards Electric Vehicles and the Impact on Consumers’ Preference. Transportation Research Part D: Transport and Environment, Vol. 77, 2019, pp. 271–291.

64.

Cooke

N. J.

Hilton

M. L.

National Research Council: Enhancing the Effectiveness of Team Science, 2015.

65.

Jung

M. F.

Sirkin

Gür

T. M.

Steinert

Displayed Uncertainty Improves Driving Experience and Behavior: The Case of Range Anxiety in an Electric Car. In Proc., 33rd Annual 22 ACM Conference on Human Factors in Computing Systems, 2015, pp. 2201–2210.

66.

Nguyen-Phuoc

D. Q.

Nguyen

N. A. N.

Tran

P. T. K.

Pham

H.-G.

Oviedo-Trespalacios

The Influence Of Environmental Concerns and Psychosocial Factors on Electric Motorbike Switching Intention in the Global South. Journal of Transport Geography, Vol. 113, 2023, p. 103705. https://doi.org/10.1016/j.jtrangeo.2023.103705

67.

Bonges

H. A.

III Lusk

A. C.

Addressing Electric Vehicle (EV) Sales and range anxiety through 24 parking layout, policy and regulation. Transportation Research Part A: Policy and Practice, Vol. 83, 2016, pp. 63–73.

68.

Haddadian

Khodayar

Shahidehpour

Accelerating the Global Adoption of Electric Vehicles: Barriers and Drivers. The Electricity Journal, Vol. 28, No. 10, 2015, pp. 53–68.

69.

Rezvani

Jansson

Bengtsson

70.

Adnan

Nordin

S. M.

Rahman

Amini

M. H.

A Market Modeling Review Study on Predicting Malaysian Consumer Behavior Towards Widespread Adoption of PHEV/EV. Environmental Science and Pollution Research, Vol. 24, No. 22, 2017, pp. 17955–17975.

71.

Christiaens

Ogrzewalla

Pischinger

Functional Safety for Hybrid and Electric Vehicles. SAE Technical Paper Series, 2012. https://doi.org/10.4271/2012-01-0032

72.

Haustein

Jensen

A. F.

Factors of Electric Vehicle Adoption: A Comparison of Conventional and Electric Car Users Based on an Extended Theory of Planned Behavior. International Journal of Sustainable Transportation, Vol. 12, No. 7, 2018, pp. 484–496.

73.

Zhan

Consumer Purchase Intention of Electric Vehicles in China: The Roles of Perception and Personality. Journal of Cleaner Production, 204, 2018, pp. 1060–1069.

74.

Daziano

R. A.

Chiew

Electric Vehicles Rising from the Dead: Data Needs for Forecasting Consumer Response Toward Sustainable Energy Sources in Personal Transportation. Energy Policy, Vol. 51, 2012, 876–894.

75.

Hansla

Gamble

Juliusson

Gärling

The Relationships Between Awareness of Consequences, Environmental Concern, and Value Orientations. Journal of Environmental Psychology, Vol. 28, No. 1, 2008, pp. 371–9.

76.

Jarvenpaa

S. L.

Staples

D. S.

The Use of Collaborative Electronic Media for Information Sharing: An Exploratory Study of Determinants. The Journal of Strategic Information Systems, Vol. 9, No. 2-3, 2000, pp. 129–154.

77.

Kwon

Wen

An Empirical Study of the Factors Affecting Social Network Service Use. Computers in Human Behavior, Vol. 26, No. 2, 2010, pp. 254–263.

78.

Neckermann

. The Mobility Revolution: Zero Emissions, Zero Accidents, Zero Ownership. Troubador Publishing Ltd, 2015.

79.

Adeya

C. N.

Wireless Technologies and Development in Africa. Unpublished Report. 2005.