Adoption of electric vehicle: A literature review and prospects for sustainability

Journal Pre-proof Adoption of electric vehicle: a literature review and prospects for sustainability

Rajeev Ranjan Kumar, Alok Kumar PII:

S0959-6526(19)34781-X

DOI:

https://doi.org/10.1016/j.jclepro.2019.119911

Reference:

JCLP 119911

To appear in:

Journal of Cleaner Production

Received Date:

14 July 2019

Accepted Date:

27 December 2019

Please cite this article as: Rajeev Ranjan Kumar, Alok Kumar, Adoption of electric vehicle: a literature review and prospects for sustainability, Journal of Cleaner Production (2019), https://doi. org/10.1016/j.jclepro.2019.119911

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Adoption of electric vehicle: a literature review and prospects for sustainability Rajeev Ranjan Kumara and Alok Kumara

a,

XLRI Xavier School of Management, Jamshedpur 831001, Jharkhand, India

(Corresponding Author) Rajeev Ranjan Kumar - Executive Fellow Program in Management (More than 15 years of automotive experience) Email: [email protected] Mobile No.: +919263639891

Alok Kumar – Assistant Professor Email: [email protected] Mobile No.: +919473369513

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Adoption of electric vehicle: a literature review and prospects for sustainability Abstract Scholarly research on the topic of electric vehicles has witnessed a dramatic increase in the current decade; however, reviews that synthesize and integrate these findings comprehensively have been lacking. This study is an attempt at filling in that void through an integrative review methodology. It includes an integrative review of 239 articles published across Scopus Q1 journals and compiled using an integrative review protocol. It encompasses the identification of variables in five different categories: antecedents, mediators, moderators, consequences, and socio-demographics. The analysis procedure revealed many interesting insights related to research methods and regionspecific developments. The review draws attention to relatively neglected topics such as dealership experience, charging infrastructure resilience, and marketing strategies as well as identifies muchstudied topics such as charging infrastructure development, total cost of ownership, and purchasebased incentive policies. It also clarifies the mechanisms of electric vehicle adoption by highlighting important mediators and moderators. The findings would be beneficial to both researchers and policymakers alike, as there has been a dearth of earlier reviews that have analyzed all sustainable consequence variables simultaneously and collectively. The development of a comprehensive nomological network of electric vehicle adoption added a new dimension in this study. The segment-wise key policy recommendations provide many insights for stakeholders to envisage electric mobility.

Keywords: Electric vehicles, Sustainability, Adoption, Integrative Review, Policy

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1. Introduction The transport sector plays a significant role in air pollution resulting in climate change due to greenhouse gases (GHG) emissions mostly in urban regions; this has necessitated road transport electrification, whereby replacing internal combustion vehicles with new energy vehicles like electric vehicles (EV) seems to be a promising step towards envisaging urban sustainability. Technologies related to electric-mobility have been changing exponentially; therefore, literature covering these changes have also increased significantly. Studies covering multiple dimensions of EV adoption across countries, including charging infrastructure (Chen et al.,2017; Dorcec et al., 2019), policies and incentives (Sierzchula et al.,2014; Bjerkan et al.,2016; Melton et al.,2017), business models (Wu P,2018; Nian et al.,2019; Yoon et al.,2019), among others. Review articles focused on specific aspects of EV adoption have started emerging. For instance, Hardman (2019), Biresselioglu et al. (2018), and Rezvani et al. (2015) respectively explored the role of reoccurring and nonfinancial incentives for plug-in hybrid electric vehicle (PHEV) adoption, electric mobility in the European context, and the drivers and barriers for EV adoption based on theoretical perspectives. Although the relatively ample empirical evidence available on the various factors of EV adoption and advantages of EV has been widely recognized, a question remains: why is the EV adoption so difficult? What are other critical factors that are still mostly unexplored? For instance, most of the previous studies have focused on either the antecedents or consequences (Sierzchula et al., 2014; Berkeley et al.,2018) with less focus on mediating or moderating variables. The relationship among these variables is still mostly unmapped. Additionally, previous literature mostly focused on the survey-based studies (Adnan et al.,2018; Sovacool et al.,2019), optimization techniques (Onat et al.,2016), or secondary data analysis and predictions (Onat et al.,2018; Choi et al.,2018) to

understand the EV adoption in some specific country or regions and may have limited policy implications. Thus, there is a need to collate these regional insights and draw conclusions judiciously. Extant literature has mostly unheeded the multifaceted, heterogeneous, and segmented characteristics of the EV market (Brand et al.,2017). Even though the consumer preferences for EV vary based on a mix of symbolic, environmental, economic, and pro-societal benefits, there is a dearth of research capturing the widespread gamut of factors related to EV adoption (Axsen et al.,2015). Furthermore, these factors vary from country to country and also across cultures (Kaptan

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et al.,2013; Spencer et al.,2015; Wang et al.,2016). Hence, there is a need to collate these crosscultural findings to understand EV adoption (Spencer et al.,2015; Wang et al.,2016). In this study, we contribute to the existing EV domain by exploring the comprehensive gamut of antecedents, consequences, mediators, and moderators from relevant literature across countries and cultures to draw meaningful insights. For this purpose, an integrative review would be suitable to comprehend the development of this emerging topic by providing a clear understanding in the form of a conceptual framework or research agenda (Torraco,2016; Alcayaga et al.,2019). There are proven usefulness of such integrative reviews in multiple disciplines of literature like, Psychology (Galvan and Galvan, 2017), information systems (Bandara et al.,2011), human resource development (Rastogi et al. 2018), medical (Morgan et al.,2015), biology (Pautasso, 2013), as well as in the management (Wilding et al.,2012; Torraco,2016; Alcayaga et al.,2019). Recently, an integrative literature review by Alcayaga et al. (2019) on the smart-circular system provides interrelationships among concepts and develop a conceptual framework that integrates key constructs. Hence, it is required to delve into the entire EV adoption literature systematically and comprehend these findings to develop a conceptual framework integrating key constructs. Understanding these research gaps, we try to answer the following research questions:1. How to illustrate the nomological network of EV adoption that integrates the key constructs from antecedents to consequences and draw meaningful insights? 2. What is the impact of EV adoption on the sustainability dimensions (economic, environmental, and social) available across the literature? 3. What are different moderating, mediating, and sociodemographic variables that affect the EV adoption based on diverse literature evidence? 4. What are the key barriers and motivators of EV adoption and expected recommendations for manufacturers, policymakers, governments, and academicians? This study tries to answer the stated research questions by using an integrative literature methodology. The review protocol identifies 239 Scopus Q1 articles based on the EV adoption, and subsequently synthesize their findings. Specifically, this study makes the following contributions. First, the integrative framework exhumes all the antecedents and consequences of the EV adoption, derived from a wide range of high-quality literature while mapping out the nomological network. Second, the review framework captured developments at all three

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sustainability dimensions as consequence variables, which in turn provide many insights to envisage reliability and sustainability. Third, the literature analysis helps us to understand the literature classifications, trends, techniques, and research outcomes. Further, based on the research framework, we categorize both barriers and motivators distinctly along with segment-wise policy recommendations, which would facilitate academicians and policymakers alike to understand the intricacies of EV adoption and future course of action. Additionally, the use of integrative review methodology provides multiple dimensions of EV adoption, which may easily overcome the limitations inherent in the survey or panel studies. Fourth, this study identified factors of EV adoption from an integrative perspective and explored the moderating and mediating mechanisms collectively, thus the inferences drawn from this study are much more comprehensive. Finally, the sample domain covers all major countries and journals publishing on EV adoption; therefore, the conclusion of this study has greater practical relevance. Section 2 presents the theoretical background, research design, and framework. Section 3 includes the review protocol and literature analysis of selected articles. Section 4 entails identified antecedents, consequences, mediators, moderators, and socio-demographic factors related to EV adoption. Significant barriers, and motivators, along with policy and academic implications, are presented in section 5. Finally, future directions and conclusions are covered in sections 6 and 7, respectively.

2. Theoretical background 2.1. Literature review The extant literature covered various methods of EV adoption like survey-based studies (Lieven, 2015; Adnan et al.,2018; Sovacool et al.,2019), optimization techniques (Onat et al.,2016; Xiong et al.,2018), data collected from drivers (Skippon et al.,2016; Berkeley et al.,2018), secondary data analysis (Sierzchula et al.,2014), and so on. Additionally, the literature also reflects countryspecific policies and ecosystems. For example, European countries like France or Norway are well suited for EV adoption due to high use of renewables in their electricity generation, whereas Germany, UK, or the USA should focus on decarbonizing their electricity generation to get GHG reduction benefits (Onat et al.,2015; Casals et al.,2016). The extant literature confirms that EV buying behavior could be influenced by multiple factors like vehicle price, total cost of ownership (Lévay et al.,2017; Palmer et al.,2018), driving

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experience (Skippon et al.,2016; Berkeley et al.,2018), charging infrastructure availability (Sierzchula et al., 2014; Berkeley et al. 2018), social influence (Schuitema et al.,2013; White and Sintov,2017), environmental awareness (Smith et al.,2017) and so on. Recognizing these critical factors could help the policymakers and researchers to develop a model or method to envisage the EV adoption rate because these factors can be used as independent variables in different adoption models. Similarly, there are many mediating or moderating variables (Qian and Yin,2017; Adnan et al.,2018) that have varying influences on electric vehicle adoption/intention. Understanding these variables could help in a better understanding of the EV adoption model, and thus an integrative literature review is sought. Further, comprehending the effect of electric vehicles at the sustainability front is the less-studied topic, and thus a comprehensive understating is required. Multiple literature reviews have integrated sustainability performances into business in different fields (Morioka and de Carvalho,2016; Siegel et al.,2019); however, such reviews are missing in the electric field domain. Only a few literature reviews identified on the electric vehicles covering limited aspects of adoption. For instance, a recent literature review by Hardman (2019), explored the role of reoccurring and non-financial incentives for PHEV adoption, whereby he suggested that policymakers should consider consumer preferences, travel patterns, and regulatory requirements to ascertain regional policy interventions. Biresselioglu et al. (2018) reviewed electric mobility-related motivators and barriers focused only in European countries. Rezvani et al. (2015) summarized the drivers and barriers for consumers adopting EV by providing theoretical perspectives to understand consumers’ intention and adoption behavior towards EV. Hence, understanding this wide-range literature, dissemination of their findings, and identification of key terminologies/categories are an integral part of an integrative literature review (Torraco, 2005, 2016; Liao et al.,2018).

2.2. Key terminologies The identification of key terminologies involves a process in which researchers eradicate their subjective biases after scrutinizing the original studies, data, and information thoroughly, arranging the information contained in the literature objectively; and developing the concepts and categories more accurately and comprehensively (Corbin and Strauss,1990). Accordingly, this study broke down the findings of literature into five broad categories based on the influential factors closely linked with EV adoption.

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The first category of factors is the antecedents that precede EV adoption and act as barriers or motivators. For instance, Sovacool et al. (2019) explored multiple antecedents like cost, performance features, knowledge, charging facilities, experience with EV, and policy support on willingness to adopt EV in China. The second category is the consequences that measure the effect of the adoption. For example, Casals et al. (2016) analyzed the global warming potential associated with electricity generations as a consequence variable in the European countries. We envisage all three sustainability outcomes of EV adoption viz. economic, environmental, and social to illustrate the fuller gamut of adoption. The third category of factors is the mediators that constitute potential mechanisms by which antecedents influence EV adoption. For example, Qian and Yin (2017) evaluate the mediating role of ethical evaluation on EV adoption intention in China. The fourth category is the moderators that affect the strength of the relationship between independent and dependent variables. For instance, Adnan et al.(2018) studied the moderating role of hyperbolic discounting between consumer's environmental concerns based on PHEV adoption intention and actual adoption. Lastly, the socio-demographic factors are used as control variables in many of EV adoption studies and have an influence on the adoption based on the regional profiles and demographics (Wang et al.,2017; Sovacool et al.,2018).

2.3. Research design An integrative literature review can be considered as a different form of research that can stand alone (Callahan,2010; Alcayaga et al.,2019). Integrative review synthesizes the findings of representative literature; thereby, generating new frameworks and perspectives (Torraco,2005). Here, the inclusion of studies encompassing diverse methodologies enables the development of a broader description of a phenomenon. More specifically, for an emerging topic (e.g., electric vehicles), such review is likely to lead a preliminary, yet holistic conceptualization of the topic (Torraco,2005; 2016) and provides a more comprehensive understanding of the subject. Thus, our research design is based on the integrative literature methodology. Further, we follow an established process of research synthesis (Cooper,2015) grounded on the multistage framework to enhance the rigor in the review process (Whittemore and Knafl,2005; Gardner et al.,2011; Alcayaga et al.,2019). The multistage framework includes the review protocol and sample identification, literature analysis, framework development, key variables identification, discussion, and finally, implications to understand the wide gamut of the EV adoption, which we discuss in subsequent sections.

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2.4. Conceptual map The conceptual or thematic structuring of a topic illustrates the literature review with broad clarity and coherence about what is being reviewed and how different concepts or themes of the topic are analyzed to get a unified idea (Torraco,2016). Providing the visual framework of a review strengthens the linkages among the sections and enhances the reader's understanding of a topic. Visual representation like concept maps (Annarelli et al.,2016), chronological timeline (Green et al.,2006), nomological networks (Gardner et al.,2011; Liao et al.,2018), and others (Alcayaga et al.,2019) provide valuable adjuncts to a literature review. We find that the nomological network is better suited in this study to demonstrate the structure of the EV adoption. Here, the nomological network is the relationship map of key antecedents, consequences, mediators, and moderators based on consumers' inclination/anxiety towards EV. In developing such a network, we advance existing literature based on multi-field research studies (Gardner et al.,2011; Alcayaga et al.,2019) and propose, in turn, a research framework (Fig. 1). As a starter, we posit a research framework that encompasses the following: (1) comprehensive overview of antecedent variables, (2) consequence variables and its measures, (3) mediating variables, (4) moderating variables, and the role of socio-demographic variables. As most of the articles have focused on the 'adoption intention' or 'adoption behavior' rather than actual adoption, we made a clear distinction between adoption intention and actual adoption.

3. Analysis of EV literature 3.1. Sample identification As per Torraco (2016), an integrative literature review may be structured in multiple ways; the author needs to follow an accepted methodology like how literature is identified, analyzed, and reported. First, the procedure for selecting articles should be described and how it was obtained, including the keywords and databases used. Second, the mix of recent published and older literature should be systematically searched. Additionally, the inclusion of niche quality publications will further enhance the outcome. Following the guidelines and using a combination of appropriate keywords (i.e., ‘Electric Vehicle’ & ‘adoption’), relevant articles were selected for review from online journal databases; the review protocol used is presented in Table 1. Ebsco

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Host, Science Direct (Elsevier), and Wiley Library were thoroughly explored for gathering relevant publications; subsequently, all duplicate publications were removed. Only Scimago Journal and Country Rank (www.scimagojr.com) Q1 (journal ranking in 2017) journals were selected to ensure good quality articles from these databases. Further, to restrict the domain of study, journals relevant to ‘Business Management,’ ‘Policy,’ and ‘Transportation and Environmental science’ were compiled. The cutoff period was chosen as ‘2010’ as the evolution of EV supports the basis of this period, and also to get the current pulse of research. It may be noted that the golden age of EV starts to post the commencement of the Nissan Leaf and the Chevrolet Volt models launched in 2010, which led to sales about 50,000 units in 2011 to 315,000 units in 2014 [Argonne National Laboratory, 2016]. Two hundred thirty-nine articles were finally selected for the review based on the protocol outlined in Table 1, encompassing all EV technologies like PHEV, hybrid electric vehicles (HEV), and battery electric vehicles (BEV). The basis for restraining our assessment exclusively to peer-reviewed journals only because working papers and dissertations may not be fully developed; hence, taking inputs from them could potentially undermine the review outcome.

3.2. Research publications period The chosen articles (i.e., 239) were used as a sample for further analysis. The year-wise publication trend of these 239 articles is presented in Fig. 2. Only seven articles before 2010 justify the cutoff year, indicating in turn that there is not anything substantial earlier, which possibly may be missed during this study. The trend from 2010 to 2018 suggests that there has indeed been a sharp increase in publications during recent years in top category journals due to the relevance of the topic. Out of 239 articles, 65 were published in 2018 alone, reiterating the spike in publications and signifying thereby, the importance of the research domain for both researchers and academicians alike. There are 35 articles (data retrieved on 7th may 2019) already published in the current year (2019) and expected to grow further as the year progresses. < Insert Fig.2>

The understanding of journal-wise importance for EV adoption is also part of the literature analysis. The sample categorized into journals of publication, as illustrated in Fig.3. The maximum articles are from the "Energy policy" journal, followed by Transportation Research Part D, Part A, Journal of Cleaner Production, and Applied Energy. These five journals encompass 75 % of the

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sample size and pioneering research journals in the field of business management, policy, transportation, and environment domain dealing with EV adoption. The Fig.3 reveals that "Journal of cleaner production" is among the leaders in the field of "EV adoption" and giving substantial importance to the underlying topic. Additionally, Fig. 3 also illustrates a large number of journals covering this research topic; hence, it is worthwhile to extract their findings into one integrative review paper. < Insert Fig.3>

3.3. Regional attributes of research publications The development of EV related studies varies across countries due to differences in regional factors. Multiple studies are covering country-specific requirements and policy decisions to expedite EV adoption. The country-wise research publications, as illustrated in Fig. 4, indicate the distribution of related research across regions. Researchers almost covered all major countries to analyze the contributing factors. Thus, the requirement of an integrative literature review is all the more necessary to collate these findings and suggest major policy decisions. Fig. 4 illustrates that the USA is leading the EV adoption research, followed by China, Germany, and the UK. There are 40 articles in our sample, which do not belong to any specific country, covering mainly the game-theoretic approach, optimization techniques, review articles, etc. Twenty-five studies involve multiple countries together to understand the dynamics of EV diffusion based on regional differences.

3.4. Overview of Research methods For analysis of the sample articles, Torraco (2005) suggests the deconstruction of the topic into its basic elements like causes, interactions, and research methods. This recommends the critical examination of the extant literature from methodological perspectives. Accordingly, we analyze the articles based on the methodologies employed and separate the constructs of EV adoption from stated antecedents to consequences. The detail of methods used across the period has been presented in Table 2. The most prominent research design used in literature is quantitative methods (224), while qualitative methods have been used in 15 articles only. The qualitative research design includes literature review, case studies, interviews, and a few of mixed methods. Articles belonging to quantitative research design encompass surveys, simulations, secondary data analysis, optimization techniques, etc. The most predominant method used for EV adoption is the

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survey-based methodology, followed by simulation, optimization techniques, and secondary data analysis. Beyond the research methods employed, we also analyzed the research based on the countries and geography to understand the adoption heterogeneity. The top five country-wise research statistics summarized in Table 2. The USA-based research studies have increased gradually over the years, while studies based on other countries have been gaining momentum, with China being the frontrunner, especially from 2017 onwards. Most cross country related studies based on survey and secondary data analysis, which in turn indicate that researchers are more interested in understanding heterogeneity, causality, and locational disparity related factors.

4. Elements of the research framework This section enumerates the key factors based on the proposed research framework. We have done an exhaustive review and in-depth analysis of the literature to identify the list of factors in each of the identified categories as below.

4.1. Antecedents The nomological network of the EV adoption starts with antecedents, which are formed based on clusters of related studies derived from the existing literature. From 239 articles studied, 23 antecedents identified after a thorough review; we have not looked to reduce this number (i.e., 23) because further clustering may leave out the original identity of that specific antecedent. The antecedents themselves have been prioritized based on the frequency of articles covering that particular antecedent as a central theme; for instance, the first antecedent (charging infrastructure development) studied most of the time as seen from existing literature, followed by the second, the third and so on. Interestingly enough, there is a wide gap between the top three and bottom three antecedents in terms of frequency, as depicted in Fig. 5. The three most studied antecedents include: charging infrastructure development, the total cost of ownership, and purchase-based incentive policies. These 23 antecedents are arranged in seven major subcategories to provide multifaceted insights for policymakers. These seven subcategories listed in Table 3.

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4.1.1. Economic Perspectives  The total cost of ownership Studies on the total cost of ownership have been used as an important yardstick to measure the economic viability of EV for consumers. The analysis covers multiple dimensions of cost structure, like purchasing price, maintenance cost, operation cost, depreciation, and energy prices. Several total costs of ownership related assessments have been done in different countries to study its impact on EV adoption (Lévay et al.,2017; Palmer et al.,2018). Each country has its cost structures, mainly due to differences in electricity/fuel prices, insurance premiums, taxes, and subsidies leading to variations in the total cost of ownership (Gnann et al.,2015; Breetz et al.,2018). It may be noted here that few probabilistic models also have been studied to capture uncertainties related to the total cost of ownership parameters (Wu et al.,2015). On the other hand, a comparative study done by Falcão et al. (2017) concluded that BEV’s total cost of ownership is 2.5 times higher than internal combustion vehicles in the minibus segment. The total cost of ownership parameters also includes energy prices (electricity and gasoline), significantly impacting EV adoption (Bjerkan et al.,2016; Wee et al.,2018).

 Business model development Multiple business models have been developed in the last few years to make EV adoption viable and economically sustainable. For example, Nian et al.(2019) developed a novel business model without any policy support to stimulate EV adoption. Wu P (2018) discussed a business model, where battery charging technology and insurance contract were bundled together in the EV sharing business. Meisel and Merfeld (2018) classified the e-vehicle service (vehicle sharing, energy trading, and microgrid operation) and provides an outline of EV economic potential with the help of policy measures. Li et al. (2016) findings revealed that collaboration between the private and public sectors has a significant impact on EV adoptions. An interesting case study is the business model followed by the commercial sector of Germany; herein, EVs perform longer trips than private vehicles. This certainly facilitates EV adoption due to higher mileage, faster amortization, and also improves the driving range (Gnann et al.,2015). Here, organizational fleets act as a vital lever for assimilating EV into the existing vehicle fleet, leading towards mass EV adoption.

 Battery cost and technology

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Battery cost and technology is a multi-dimensional antecedent having multiple effects on other antecedents like total cost of ownership, willingness to pay, range anxiety, and performance attributes. Declining battery prices is a potential motivator for EV adoption (Quak et al.,2016). The cost of battery replacement, recycle, or reuse are significant components of EV lifetime cost analysis (Letmathe and Suares,2017). The study explored a better understanding of battery substitution, especially when it is needed and how much it would cost, including a complete life cycle analysis from the cradle to the grave. Many drivers are potentially compromising safety by not choosing car features (like heaters) to conserve and improve battery lifespan (Jensen et al., 2013). Such uncertainty regarding battery needs a major technological breakthrough to reduce cost and improve performance.

 Willingness to pay Willingness to pay is measured from a consumer's perspective and is linked with EV pricing or services. ’Willingness’ in itself tends to vary with/without experience based on expectations versus the reality of a consumer. As per Skippon et al. (2016), willingness to pay for an EV declined after the driving experience, especially when the range is low. Those participants who were willing to consider a low-range EV were high in self-congruity and viewed it as a symbol of personal identity. For service related willingness, it depends on how much money users are willing to pay for charging and other services to build an effective EV eco-system. As per Dimitropoulos et al. (2013), consumers’ marginal willingness to pay decreases with a decreasing rate of driving range. Dorcec et al. (2019) used a gamified study to discover a person's willingness to pay for EV charging. They suggested that state-of-charge is negatively related to willingness to pay; in effect, this means that drivers would pay less if state-of-charge is more, and vice versa.

4.1.2. Charging infrastructure readiness  Charging infrastructure development A charging infrastructure comprises of fast and slow chargers to support electric mobility. Several studies have confirmed that the unavailability of adequate charging infrastructure is a major constraint for EV diffusion (Sierzchula et al.,2014; Chen et al.,2017; Berkeley et al.,2018). The inadequate charging infrastructure reduces flexibility and user convenience, subsequently making EV driving a less attractive proposition (Haddadian et al.,2015). Madina et al. (2016) go on to justify the lack of charging infrastructure, stating that this business model is not economically

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viable today, as there are only a few electric vehicles in the market. Nevertheless, developing public charging infrastructure is necessary to support the charging needs of early adopters. In these lines, Melliger et al. (2018) studied the positioning of charging stations at consumers’ homes and residential locations, and found significant improvement in the user’s attitude, as s/he had a charging facility that was accessible. Lieven’s (2015) study indicated that a well-established EV charging facility is far better than various convenience policy measures such as using bus lanes. Additionally, few other studies have focused on the optimal distribution of chargers within a region (Mak et al.,2013; Chen et al.,2017). Such models helped in understanding the optimal infrastructure deployment strategy based on battery standardization and technology advancements.

 Range anxiety Several studies have pointed out ‘range anxiety’ to be one of the major hurdles of EV adoption (Skippon et al.,2016; Melliger et al.,2018). For instance, Skippon et al. (2016) studied how a driver’s experience influences BEV adoption. Results suggest that given the fact that BEV's performance rating is higher than the conventional car, drivers are not willing to proceed with the BEV option once they experience the same due to its short range. Adding to this, Travesset-Baro et al. (2015) claimed a limited driving range to be one of the vital blockades for mass adoption of EV, especially in hilly regions like Andorra. Policymakers are looking at future generation EV batteries to get rid of such range anxiety issues, ably and adequately supported by charging infrastructure. Melliger et al. (2018) in these lines looked at ‘range anxiety’ from a whole new perspective; on studying anxiety vs. reality in Switzerland and Finland, they concluded that the growth of residential charging options is, in fact, essential to maximizing EV's potential, and minimizing the ‘range anxiety’.

 Electricity load distribution and management Electricity load distribution includes charging station power levels, topology, and infrastructural constraints for electrified vehicles. Paterakis et al. (2015) illustrated the cost-minimizing effect of electrified vehicles charging/discharging by simulation studies, and proposed service for households with shift-able and non-shift-able loads, energy storage, and generation unit. Ravichandran et al. (2018) proposed a rolling horizon methodology to minimize the microgrid operational cost to evaluate real-time trading of electrified vehicle services for microgrid trading scenarios. Eising et al. (2014) illustrated a geographic information system-based simulation

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methodology for assessing the electricity supply and demand at the neighborhood level. The findings revealed that diffusion of EV was compromising the function of the grid (for short term) in a populated area like Amsterdam. They identified risks associated with the functioning and reliability of charging grids. As new charging technologies like the vehicle to grid and smart grid are not fully developed, rapid diffusion of EV and charging behavior of users may create risks for grid functioning.

 Charging behavior Charging behavior is primarily influenced by the location of charging points and the time required for charging. Usually, charging sessions last much longer in the evening and night because vehicles stay connected overnight. Shorter charging sessions take place in the afternoon, while in the morning, charging sessions are pooled with ‘workplace’ charging, leading to marginally extended charging sessions as compared to afternoons. Wen et al. (2015) used stated preference techniques for mid-trip charging and observed that dwell time, state of charge, and the price is significant factors influencing a driver’s choice of EV adoption. Sun et al. (2016) studied fast charging options of BEVs in Japan. They observed that consumers are willing to detour up to 1.75 km on working days and 750m on holidays reflecting different charging behaviors. Wolbertus et al. (2018) studied policy implications of charging behavior for EV users, as well as the buying intentions of potential users. Their findings revealed that cross-pollination exists between EV charging and adoption policies; thus, this factor should be kept in mind while framing and designing policies.

 Charging infrastructure resilience Adderly et al. (2018) study revealed that EV owners are susceptible to risk, or lack of electricity during natural disaster events when access to electricity curtailed for several days; such disasters may lead to overloading of the grid in the case of a mass evacuation. To address such uncertainties, charging infrastructure resilience is required by creating more charging stations in an evacuation region to ensure a continuous supply of electricity to EV owners. Ustun et al. (2015) described charging infrastructure resilience through the potential use of an improvised micro-grid during a natural disaster, disruption, or warfare by thoroughly understanding their liabilities and potential failure locations.

4.1.3. Consumers perspectives  Psychological characteristics

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Many studies pointed out psychological aspects (e.g., moral values, behavior, attitudes) as significant determinants of EV adoption (Schuitema et al.,2013; Nayum et al.,2016; He et al.,2018). Schuitema et al. (2013) found that consumer emotions and feelings influence attitudes and intentions to adopt EV. Further, personal innovativeness is one of the fundamental personality traits that influence EV adoption (He et al.,2018). The actual usage of EV also influences the consumers' decisions (Skippon et al.,2016). This can be explained by the construal level theory that suggests, psychological distance influences the level of abstraction with which a product is created (Liberman et al.,2007); higher psychological distance means being far away from reality. Therefore, research in which participants have no experience of EV may be subject to substantial uncertainties. Nayum et al. (2016) revealed that policymakers should consider a potential user's psychological characteristics while devising incentive policies for EV diffusion.

 Consumers heterogeneity A behaviorally realistic model is based on the fact that a few users may have slight or no awareness about EV; such awareness could represent considerable heterogeneity in a user’s preferences (Axsen et al.,2015; Brand et al.,2017). Huang and Quin (2018) analyzed heterogeneity in user preferences towards EV in lower-tier cities of China by using the stated preference choice method. Results indicate that Chinese users in lower-tier cities are sensitive towards monetary features (like. EV price, subsidies), service features (like. charging stations, home charging facility), and driving range. Moreover, Chinese car brands are usually perceived to be disadvantageous in comparison to European brands. The heterogeneity in vehicle-usage patterns also has a significant impact on the total cost of ownership, while the determining factors include the trip type (urban vs. highway), user segments, and residential density (Wu et al.,2015; Brand et al.,2017).

 Symbolic attributes Symbolic attributes and its relationship with self-identity has been enumerated in many EV adoption studies based on distinctive psychological/sociological theories; as a matter of fact, it has played a significant role in consumer adoption intention (Schuitema et al.,2013; White and Sintov, 2017; Jansson et al.,2017). Such social influence is also explained by the 'Hawthorne effect,' which explicates that participants change their behavior or preferences when being observed by others (McCarney et al.,2007). Consumer studies in China regarding the significance of face influence and word of mouth information sharing reflect that expensive cars were viewed as status symbols

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to gain social identity (Helveston et al.,2015; Qian and Yin,2017). He et al. (2014) developed analytical techniques at the product design stage to integrate discrete choice models with the social network. They developed a simulation model to address the interactions among product, consumer, and social influences simultaneously. The modeling of social attributes within the framework of choice modeling gives a new dimension to designers and policymakers.

 Environmental concern and awareness Quak et al. (2016) confirmed strong environmental performance (e.g., low CO2 emission) is an important motivator for EV acceptance. Schuitema et al. (2013) concluded that ‘pro-environmental identity’ predicted favorable attitudes towards EV. Smith et al. (2017), in similar lines, studied the environmental enthusiast bias and found that traders conscious about environmental concerns had higher adoption intentions for EV. These apart, a few other studies have also confirmed that early adopters of EV are more environmentally conscious (Axsen et al.,2016).

 Perceived risks Berkeley et al. (2018) studied the challenges of EV adoption in the UK and revealed that economic uncertainty is one of the major ones generating consumer anxiety due to the high purchase price, long payback period, uncertainty over maintenance and repair infrastructures. Anxiety developed from expected post-behavioral emotions (e.g., worry, tension) has a significant influence on the acceptance of high technology products (Hirunyawipada and Paswan, 2006). As emotions tend to strongly predict consumer behavior in highly involved conditions (Moons & De Pelsmacker, 2012), perceived risk and anxiety are more likely to influence the acceptance of new environmentally-oriented vehicles (Barbarossa et al.,2015). As per Qian and Lin (2017), perceived risk is negatively related to EV adoption intention and suggested that public policies should be evolved to minimize the perceived risk of sustainable technologies.

4.1.4. Government policies and regulations  Purchase-based incentive policies Purchase-based incentive policies comprise of direct subsidy given for EV purchases, registration /emission/tax fee exemption, etc., which is most widely used by several countries to lower the purchasing cost while facilitating EV adoption thereof. Sierzchula et al. (2014) explored the relationship between financial incentives and EV market share across 30 countries and found that

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financial incentive is a significant predictor of EV adoption. Regions like Norway, Netherlands, and the State of California have the highest PHEV/EV market share, mainly due to supportive and incentive policies (Sierzchula et al.,2014; Melton et al.,2017). Later on, Bjerkan et al. (2016) studied the role of financial and tax incentive policies in Norway and found that exemptions from taxes were significant motivators for more than 80% of the respondents.

 Use-based incentive policies Use-based incentive policies are designed for the convenience of EV users and vary across countries such as free parking policy, toll tax exemption, highway lane excess, etc. Bjerkan et al. (2016) findings support convenience measures like free parking is very effective for an increase in EV sales. They revealed that for a large number of EV users, free access to bus lanes and exemption from toll taxes were, in fact, the primary deciding factors for EV adoption. Further, Quak et al. (2016) stated that using bus lanes and special parking spaces could also improve the efficiency of the operation. On the contrary, Mersky et al. (2016) found that toll exemptions and use of bus lanes are not statistically significant predictors of EV sales in Norway, probably because of neighboring major cities having these incentive measures. Thus, devising appropriate convenience policy measures are necessary based on regional EV scenarios.

 Government regulations Government regulations are the outcomes of various policy level decisions taken by different countries to electrify the transportation sector. Government regulations often support original equipment manufacturers, dealerships, and fuel suppliers to facilitate EV sales (Melton et al.,2017). A few examples of such regulations are GHG standards and fuel economy (Sen et al.,2017), zero-emission vehicle mandate (Sykes and Axsen, 2107), and low carbon fuel standard (Lepitzki and Axsen, 2018). Sen et al. (2017) study revealed that corporate average fuel economy (CAFE) regulation could boost up the market penetration of EV and thereby reduce dependence on fossil fuel in the United States, especially if executed collectively with other government incentives. Zhao et al. (2016) studied vehicle to grid regulation services of electric delivery trucks and noted both economic and environmental benefits. Additionally, it should be noted that more economic benefits could be achievable for fleet owners once the carbon tax is introduced.

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4.1.5. Vehicle design and performance  Vehicle design features Vehicle design features encompass different classes, models, sizes, and technology used, as stated in the existing literature. Electric vehicles are significantly different from conventional ones due to their design and innovation (Cherubini et al.,2015). Mohamed et al. (2018) studied different car body types and highlighted the psychographic alignment along with the socio-economicdemographic variables of potential EV users towards vehicle designs. They went on to analyze seven-vehicle body types; insights drawn from their study could help in re-defining the potential submarkets of EV. A few studies also reiterated that limited model availability is a significant barrier for successful EV adoption (Barisa et al.,2016; Berkeley et al.,2018). Limited EV model options in the market with limited functionalities fail to achieve different consumer expectations (Haddadian et al.,2015). Some other researchers studied vehicle features like vehicle size/class (Hess et al.,2012), the vehicle makes/model's (Beck et al.,2013) influence on EV adoption. 

Performance measures

Performance measures include functionality and utility achieved from using an EV, mainly encompassing tangible characteristics (Schuitema et al.,2013). For instance, a few studies confirmed that driving range, top speed, pick up, and acceleration are some of the key performance indices to foresee a potential uptake of EV (Sovacool et al.,2018). Alternative fuel vehicle performance is a significant factor that includes vehicles’ acceleration (Hess et al.,2012), horsepower (Beck et al.,2013), etc. as compared to conventional vehicles. Wu et al. (2015) analysis showed that driving electronic vehicles is more efficient in-city routes, as drivers prefer driving in-city routes as compared to freeways routes. Travesset-Baro et al. (2015) showed that road grades are significantly related to energy consumption, affecting EV’s performance in different levels of technology. Though EVs are heavier than internal combustion vehicles, they are less impacted by road grades and tend to recover energy during deceleration. Schuitema et al. (2013) concluded that hedonic attributes (pleasure of driving) are positively related to the adoption intention of BEV/PHEV.

4.1.6. Environmental perspectives  Potential environmental benefits

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Multiple studies have focused on predicting the environmental benefits of electric vehicles through simulations, data analysis, and other methods (Zhao and Heywood,2017; Laberteaux and Hamza, 2018). Laberteaux and Hamza (2018) presented a comparative study of equivalent GHG emission of conventional vs. electrified models in real-traffic like driving patterns through stimulation. Findings from this study revealed several policy-related choices that facilitate EV adoption for achieving higher GHG reduction. Earlier, Zhao and Heywood (2017) developed an electricity supply and emissions model to predict GHG emissions of the electricity grid under different scenarios and find that it largely depends on the electricity generation mix.

 Electricity generation mix Nichols et al. (2015) showed that electricity generation from coal is a significant obstacle for electric vehicle growth because there is no significant GHG reduction observed while considering lifecycle analysis. Zhao and Heywood (2017) assessed the potential impact of electrification in China by varying the level of EVs into the fleet while adopting renewable electricity, and found a significant reduction in GHG emission when electricity generation was based on renewables. Abdul-Manan (2015) presented a lifecycle assessment of EV as compared to gasoline vehicles; he showed that the use of EV does not necessarily result in reducing emissions, especially when a country's electricity generation mix is itself based on fossil fuel. Further, the study revealed that decarbonizing the power sector could achieve a higher reduction in emissions rather than focusing only on EV.

4.1.7. Marketing perspectives  Marketing strategies Marketing strategies should be consumer-centric, focusing on how EV facilitates user's convenience as well as adds to environmental benefits (Egbue and Long,2012; Qian and Yin,2017). Many potential buyers do not have sufficient information about EV models, their availability, and are uncertain about its benefits from an environmental perspective (Barisa et al.,2016; Shao et al.,2016). Shao et al. (2016) stated that buyers lack trust in the marketing campaigns and statements made for EV products; in the process, a prospective consumer’s motivation towards EV reduces drastically. Qian and Yin (2017) studied Chinese cultural values related to EV and revealed that social marketing efforts, along with public policies, should pay more heed towards cultural values during the promotion of environmentally sustainable products.

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Further, White and Sintov (2017) showed that EV marketing strategies should emphasize the use of EV as an identity symbol rather than focusing on instrumental attributes.

 Dealership experience Recent research acknowledged the vital role of marketing intermediaries (e.g., dealerships) in influencing the purchase of the green product through product positing and training (Axsen et al.,2015; Matthews et al.,2017). A few studies showed that the unavailability of EV models with dealers was negatively impacting consumer purchase intention as there were no EV models to view or test drive (Cahill et al.,2014; Matthews et al.,2017). Add to this the additional three to fourmonth waiting period to receive an EV after ordering has further aggravated the situation. Cahill et al. (2014) explored another dimension of the dealership and found that the profit margins for EV are much lower than normal vehicles; ironically, electric vehicles require less maintenance than internal combustion vehicles. As a result, those dealers who get a major chunk of their income from services and parts are reluctant to sell EVs. These 23 antecedents are the broad level classification of variables derived from the existing literature. The impact of a few antecedents may be linked with others, but have been considered separately here because they were primarily studied as different factors in the literature.

4.2. Consequence variables (Sustainability measures) As discussed earlier, we are considering three consequence variables here to demonstrate the sustainability impact of EV adoption. Studies related to each of these variables are presented here to understand its effect on the government/environment/society.

4.2.1. Economic impact In economic measures, we broadly consider revenue generation/reduction, profit, net present value analysis, and market share/sales. Zhao et al. (2016) found that providing vehicle-to-grid regulation services for electrified trucks could produce a substantial extra revenue of $20,000-$50,000 as compared to conventional trucks. There are a few regions like New York ISO, where regulation service prices are quite high; thus, lifetime vehicle-to-grid revenue could be achieved up to $60,000, leading to a considerable amount of profit generation. Noel and McCormack (2014) illustrated a cost-benefit analysis of the vehicle-to-grid electric school bus as compared to the conventional diesel school bus in the USA and achieved a monetary saving of around $6070 per

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seat in net present value. They calculated that if the entire district's school bus would transform to the vehicle-to-grid based, then the net present saving would be more than $38 million. Rietmann and Lieven (2019) studied the influences of policy instruments on EV sales and market share; they concluded that policy measures were positively influencing the EV market share. A case of tax revenue reduction also studied by Jenn et al. (2015) illustrated that the high adoption of EV in the USA would lead to a reduction in gasoline consumption and subsequently reducing the revenue generated from the transportation sector.

4.2.2. Environmental impact Environmental impact broadly captures the GHG emissions, carbon, and water footprint along the complete lifecycle of energy generation and use. Zhao et al. (2016) study indicated that apart from economic benefits, providing the vehicle-to-grid regulation services for electric trucks could also produce significant GHG emission saving (around 300 ton CO2) as compared to the conventional diesel-powered truck. However, the total lifecycle GHG emission of an electric truck is almost equivalent to conventional trucks, mainly due to GHG emissions generated during electricity generation and transmission phases. Choi and Song (2018) studied well-to-wheel GHG emission of BEV in oil-importing countries like South Korea, which are mainly importing through maritime transportation. The comparative statistics between BEV and its counterparts indicate that driving a BEV to emit around 90-110gCO2 eq/km less than an equivalent conventional vehicle. It is possible mainly due to energy generation from nuclear sources, which produces almost zero GHG emissions. Onat et al. (2015) studied the carbon and energy footprint analysis for conventional, hybrid, and EV across 50 states in the USA. As per the existing electricity generation mix, EV was found to be the least carbon-intensive option in 24 states, whereas hybrid electric vehicles are found the most energy-efficient option in 45 states. Further, Onat et al. (2018) studied the well-towheel water footprint of EV versus conventional vehicles in the USA. Their findings exhibited that in the worst scenario, BEV may consume up to 70 times more water than a traditional vehicle, whereas BEV with electricity generation from solar charging has the lowest level of water consumption and may reduce water footprint by up to 97%.

4.2.3. Social impact The transition towards electric mobility creates multiple opportunities for vehicle manufacturers by modernizing technologies. Apart from vehicle manufacturers, energy companies, and service

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providers could also be benefited by upgrading the technologies and creating job opportunities in different sectors (Haddadian et al.,2015; European Commission,2016). Onat et al. (2015) studied the lifecycle for optimal distribution of new energy vehicles in the USA, whereby they analyzed socio-economic indicators like maximizing employment under different scenarios, to assess the impact of EV development. Karaaslan et al. (2018) showed the importance of pedestrian safety during the movement of EV versus conventional vehicles. Their comparative findings revealed that EV has 30% more pedestrian traffic safety risk as compared to conventional vehicles under high ambient sound level, whereas at the low ambient sound level, EV has a 10% higher safety risk for pedestrians. The studies covering these three sustainability dimensions as consequence variables of EV adoption are presented in Fig. 6. There are only three studies (Zhao et al.,2016; Choi and Song,2018; Yan S,2018) covering both economic and environmental factors, whereas, only one study (Onat et al.,2016) covers all three sustainability dimensions simultaneously as consequence variables.

4.3. Mediating variables Mediating variables are an integral part of the relationship map and influence many variables, which go on to impact EV adoption. For instance, Adnan et al. (2018) studied Malaysian consumer behavior while adopting PHEV and showed that attitude towards adoption, perceived behavioral control, and personal moral norm mediated between environmental concern and intention to adopt. In contrast, subjective norms did not mediate between environmental concern and intention to adopt. He et al. (2018) stated that perceived monetary benefits and perceived risk are partially mediating between personal innovativeness and EV purchase intention. Similarly, the perceived fee is partially mediating between environmental concern and EV purchase intention. Schuitema et al. (2013) posited the hedonic attributes and symbolic attributes fully mediate the relationship between the instrumental attributes and intention to adopt PHEV as a second car, whereas partially mediating between instrumental attributes and the intention to adopt BEV. The hedonic attribute defined here is the pleasure of driving, while the symbolic attribute acts as an identity symbol derived from owning and using EV. White and Sintov’s (2017) concluded that considering EV as

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environmentalist and social innovator symbols partially meditating the relationship between concern for climate change and EV adoption intention as summarized in Table 4. Table 4 illustrate that environmental concern is the most studied antecedent variable for the mediation effect analysis. Apart from environmental concern, psychological characteristics, vehicle design and performance measures, and cultural values are the other antecedent variables studies for the mediation effect. The only dependent variable used is the purchase intention/adoption intention, and none of the studies analyzed the mediating effect of variables on actual EV adoption or the consequence variables. Three major types of mediating variables used to understand the mediating effect on EV purchase intention. (i) Psychological characteristics (ii) symbolic attributes, and (iii) perceived attributes.

4.4. Moderating variables The role of moderating variables in the relationship map is significant and studied by many scholars. For example, He and Zhan (2018) considered the external cost (i.e., perceived price and perceived complexity) as a moderating variable between personal norm and EV adoption intention. They found that perceived price was negatively moderating between personal norm and EV adoption intention, while perceived complexity had a nonlinear moderation effect between them. Adnan et al. (2017) conducted an experimental examination on predicting the adoption behavior of EV among Malaysian users. The study revealed that environmental concern positively moderated the relationship between consumer purchase intention and the actual adoption of EV. He et al. (2018) examined the moderation effect of gender between personality and purchase intention. Their study revealed that the positive influence of personal innovativeness on purchase intention is higher for males as compared to females. Conclusively, women are more unwilling to purchase an EV due to negative utility. In contrast, on the other hand, men are more inclined to buy an EV due to its positive utility, as summarized in table 5. Table 5 shows that both adoption intention and actual adoption have been studied as dependent variables for moderation effect analysis. It is interesting to note that purchase intention to actual adoption is positively moderated by the hyperbolic discounting and the environmental concern. Further, environmental concerns studied as both an independent variable as well as a moderating

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variable. Most of the antecedent variables studied for moderation analysis are related to psychological characteristics. Gender is the most studied moderating variable and influences the EV purchase intention. Apart from gender, psychological characteristics related factors are the second most studied moderating variables. No studies available in the sample literature analyzed the moderation effect on the consequence variables.

4.5. Socio-demographic variables Extant literature has considered socio-demographic characteristics as a distinguishing factor between adopters and non-adopters. However, in a few of them, the significance level of these variables have changed based on geography, culture, and country-specific conditions. The summary of existing literature highlighted the importance of various socio-demographic factors towards EV adoption listed below. 

A higher level of education (Plötz et al.,2014; Nayum et al.,2016; Liu et al., 2017; Sovacool et al.,2018)

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High-income group (Axsen et al.,2016; Nayum et al.,2016; Liu et al.,2017; Morton et al.,2017)

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Younger age and middle age group (Plötz et al.,2014; Nayum et al.,2016; Sovacool et al.,2018)

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Having multicar households (Nayum et al.,2016; Melliger et al.,2018)

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Gender – predominantly male (Plötz et al.,2014; Sovacool et al.,2018)

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Live in larger households (Nayum et al.,2016)

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Household size (Liu et al.,2017; Morton et al.,2017)

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Travel work pattern (Liu et al.,2017; Morton et al.,2017)

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Car availability (Liu et al.,2017; Morton et al.,2017)

The characteristics of these socio-demographical variables vary in different regions and should be adequately measured to foster EV diffusion based on regional requirements. The comprehensive nomological network of all variables shown in Fig.7.

5. Discussion and Implications Extant literature on EV adoption is multifaceted and requires a holistic ecosystem approach to uncover all key variables at multiple levels, as posited in this paper. Such an integrative review

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and analysis is significantly important for enriching academia while devising an effective strategy and policy to facilitate EV transition. We compile these scholarly contributions, extracting useful insights, and conclude as below.

5.1. Benefits of the nomological network None of the integrative literature reviews on the said topic indicate the difficulties that EV researchers and practitioners face while integrating such a diverse knowledge base to explain how government and policymakers should shape the current transportation sector towards sustainability. Though, there have been a few valuable frameworks developed recently to organize EV adoption literature (Biresselioglu et al.,2018; Hardman,2019), this integrative framework stands apart, as it emphasizes on the continuity from antecedent to consequence along with mediator and moderator variables. Thus, the nomological network provides a platform here to integrate diverse research findings and give valuable insights. For instance, we could capture herein consequence variables collectively across all sustainability dimensions, which was missing in earlier literature reviews (Rezvani et al.,2015; Biresselioglu et al.,2018; Hardman,2019). Many important antecedents like charging infrastructure resilience, dealership experience, etc. have also been missing in earlier reviews (Rezvani et al.,2015; Hardman,2019). The relationship among variables is possible with the help of the nomological network, which further helps us for the classification of all antecedents and consequence variables into two major categories, i.e., motivators and barriers to understanding the impact of each variable. Motivators are those factors, which facilitate the EV adoption, and must be sustained or further improved. However, barriers need to be eliminated or minimized to improve EV adoption. There are only seven motivators identified out of 23 antecedents, as illustrated in Table 6. Most of these 23 antecedents come under the 'barriers' category indicating in turn that there are many hindrances currently, which need to be dealt through adequate policy, awareness, or technological advancements. These findings extend the existing literature with a wide range of antecedents (Biresselioglu et al.,2018; Hardman,2019), and require a focused approach based on geography and existing scenarios (like electricity generation mix, policies, infrastructure, etc.). Table 6 also illustrates the impact of EV adoption on all consequence variables, including economic, environmental, and social impact, which are observed in both motivators as well as

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'barriers' categories. The findings would be helpful to both academicians and policymakers alike due to understanding the linkages among variables with the help of a nomological network.

5.2. Policy Implications The top few antecedents (Fig. 5) captured the pulse of current EV related studies and provided multifaceted policy implications. To recapitulate: the first antecedent was the development of charging infrastructure; the most studied topic as an antecedent of EV adoption and emphasizing, thereby, the role of chargers. Globally, publicly available chargers played an important role in improving charging infrastructure, which mainly comprised of slow chargers, reaching to almost 3,20,000 worldwide as in 2017 (Bunsen et al.,2018). Many recent studies also highlighted the importance of fast chargers for urban and densely populated environments (Neaimeh et al.,2017; Bunsen et al.,2018). Currently, China has the highest number (i.e., 83,395) of publicly accessible fast chargers, followed by Japan (7,327) and the USA (6,267) (Bunsen et al.,2018). This certainly signifies the importance of publicly accessible fast chargers for early-adopting countries and devise policies accordingly to boost up their charger density to foster EV adoption. Apart from publicly accessible chargers, home/workplace/parking chargers have also played an influential role in improving charging infrastructures (Helveston et al.,2015; Wu X.,2018). Thus policy decisions regarding installing charging facilities at home/workplace/parking spaces will create a favorable EV ecosystem. New technologies like vehicle-to-grid, wireless charging have also seen to improve the charging infrastructure based on locations and found cost-effective (Liu and Wang,2017; Xiong et al.,2018). Hence, regulations related to vehicle-to-grid, wireless charging, and the use of technology can further increase the likelihood of EV adoption. The next two antecedents (total cost of ownership, and purchase-based incentive policies) in the top three are related to the economic perspectives. Many countries like Norway, Netherlands, China, and the USA have managed to see a major boost in EV sales recently (Bunsen et al.,2018) due to strong financial policies like subsidies, tax exemptions along with convenience policies, and expansion of charging infrastructure. Such analysis provides a clear signal to government and policymakers that economic perspectives are a significant focus for consumers and need to devise their policies accordingly to make it economically comparable or even better their counterparts. A survey by Lieven (2015) across 20 countries identified three types of clusters based on their preferences. The first cluster included the financial incentives, followed by charging infrastructure, with other incentives forming the third cluster. Our literature review findings extend the Lieven's

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(2015) findings and get the pulse of the worldwide perspective of EV adoption. Our analysis provides seven major subcategories comprised of 23 antecedents and suggests important policy recommendations, as presented in Table 7. The next antecedent is range anxiety, influenced by the charging infrastructure along with battery technology and has been considered a major barrier for EV adoption. The review findings extend the previous studies, which confirmed range anxiety as a major obstacle and devised various strategies to minimize the same (Lim et al.,2014; Melliger et al.,2018). The range anxiety problem is further aggravated by the longer charging time required for refueling an EV. A few recent studies also focused on the use of fast chargers and battery swapping options to reduce the charging time impact on EV adoption (Mak et al.,2013; Neaimeh et al.,2017). Thus policymakers should minimize the range-anxiety issues through options like the use of fast chargers, battery swapping stations, improvement in battery technology, and so on. It has been noted that there is a substantial reduction in tailpipe GHG emission of EV as compared to internal combustion vehicles, but while considering lifecycle analysis or well-to-wheel analysis, these same benefits seem minimal or even worse if energy generation mix is carbon-intensive. For Asian countries like China and India, where coal is a major source of electricity generation, the actual GHG emission in lifecycle analysis is even higher for EV as compared to internal combustion vehicles (Hofmann et al.,2016). Thus carbon-intensive electricity-generating countries need to decarbonize their electricity generation source to get actual benefits of EV. Our findings support the latest research in this field (Onat et al.,2018; Choi et al.,2018) and provide important insights to governments and policymakers to envisage the use of renewable sources in their electricity generation. The review findings also exhume the role of potential buyers, where consumers considered the environmental impact of a vehicle during purchase and found a predictor of EV adoption (Plötz et al.,2014; Sang and Bekhet,2015). The least studied topics like dealership experience, charging infrastructure resilience, and marketing strategies further provide many policy implications. For instance, the dealership experience, unavailability of EV models with a dealer, not only restricts a prospective customer's viewing and testing the experience, but also creates an information gap. Additionally, the sales executive's response, dealership margin, long waiting time, etc. all add up as significant issues, which need to be considered while devising policies related to dealerships. Similarly, the lack of

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trust in marketing campaigns and less focus on environmental and cultural perspectives are additional hurdles of the marketing strategies. Our review findings suggest that different marketing strategies should focus on improving trust in marketing campaigns (Shao et al.,2016), linking cultural values and social marketing efforts (Qian and Yin,2017; McLeay et al.,2018) to increase the EV adoption. As per Qian and Lin (2017), perceived risk is negatively related to EV adoption intention and suggested that public policies should be evolved to minimize the perceived risk of sustainable technologies by providing either educational opportunities or facilitating interactions among consumers. Another least focused topic is the charging infrastructure resilience, which is necessary for EV adoption, especially during natural disasters / mass evacuation scenarios. Each country needs its own natural disasters policy and risk mitigation plans for charging infrastructure resilience to ensure safe and reliable EV options during evacuation scenarios.

5.3. Implications for academia The integrative review findings provide important insights for academia to redirect their research attention to multiple factors like dealership experience, consequence variables, etc. The three least studied antecedents that require more academic attention are dealership experience, charging infrastructure resilience, and marketing strategies. As pointed out, dealership experience has many problems like the unavailability of EV models in dealerships, long waiting periods, and so on. Due to limited studies in this field, a clear research direction for the dealership based on locations and country-specific can be investigated. Similarly, understanding the role of charging infrastructure resilience for reliable EV options during mass evacuation scenarios varies across geographies. More academic investigations are explicitly sought to regions and countries to understand the multiple facets of resilience. As no adoption model available in EV domain comprehend all these antecedents comprehensively, thus future studies should consider these identified antecedents for EV adoption while developing the model. The role of mediating and moderating variables are important for any technology adoption theories. The mediation mechanism illustrated in Table 4 provides many insights. First, the environmental concern is the most studied antecedent for mediation effect analysis followed by psychological characteristics. Second, only adoption intention is captured as a dependent variable for moderation effect analysis. Further investigation is sought for analyzing the moderation effect on actual adoption or the consequence variables. Third, major antecedents used for mediation are

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psychological, symbolic, and perceived related factors; however, other antecedents as listed may be explored for developing a robust EV adoption model. Finally, varying strength and direction of mediation variables give multiple directions for academicians. In similar lines, Table 5 illustrates the moderation effect provides multiple directions for academicians. First, only limited variables (hyperbolic discounting, environmental concern) have been studied for the moderation effect between adoption intention and actual adoption. Whereas both adoption intention and actual adoption used as a dependent variable in the literature. Second, the environmental concern is studied as both antecedents and dependent variables; however, most studies considered it as an antecedent variable. Third, most of the antecedents studied for moderation analysis are related to the psychological characteristics; hence, other antecedents need to be studied for the moderation effect. Fourth, gender is the most studied moderating variable, followed by psychological factors. Finally, none of the studies analyze the moderation effect on the consequence variables, which is a major gap to be filled by the academicians. This literature review also pointed out that experience with EV influences the consumer decisions (Skippon et al.,2016), hence more academic investigations required to capture such experiences to minimize the psychological distance and get the actual consumers' concerns based on the usage.

5.4. A critique of the existing approaches As it is evident that the underlying framework delves deeper into the finer nuances. There are multiple critiques, as stated in different sections. First, we examine the classifications on EV literature based on methodologies employed, geographical concentration, and trend of the relevant research studies (Table 2). We can infer that there is a dearth of meta-analysis, literature reviews, and longitudinal based research studies, which are important for literature conceptualization, model development, and theory building for future studies. Second, countrywide stratification indicates that most of these studies were based on the USA, whereas the highest penetration of EV is in Norway and Sweden in terms of market share (Bunsen et al.,2018). It may provide many insights for policymakers to increase the market share of EV in line with Norway or Sweden. Further, the adoption of EV for oil-importing countries like India, South Korea reduces their dependency on fossil fuel significantly and maybe a game-changer for their economy. Third, we categorize the factors of EV adoption, as demonstrated in the relationship map (Figure 7). It uniquely presents the relationship among various factors based on the literature. For making EV adoption viable, various business models are presented in different countries; however, EV

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mass acceptance is still missing raises several questions about its adaptability in the prevailing scenarios. Specifically, the charging infrastructure and range anxiety-related concerns are mapped differently by authors and are major barriers for EV adoption across the countries. Further, the economic benefits of EV are vary based on country-specific policies and incentives. The upfront cost of an EV is also a major hindrance for the consumers. One most prominent cost determinant of EV is, i.e., battery and its replacement cost require major technological improvement and cost reduction initiatives to make it adaptable in line with conventional vehicles. Additionally, a lack of viable battery recycling models in the literature is also a critique of prevalent studies. Fourth, the review findings reveal that actual GHG benefits of EV are minimal in countries like China, the USA, and India, where electricity generation is carbon-intensive. However, EV' GHG reduction is evident in a few European countries like Norway, Sweden, mainly due to the high contribution of renewables in their electricity generation. Economic growth and urbanization need electric mobility to reduce GHG emissions, and at the same time, natural resources like water, air quality, etc. also to be preserved. Few other studies have pointed out the impact of economic development on natural resources (Luo et al.,2018; 2019), and similar studies in the field of EV may provide its full effect on the ecosystem. Finally, the consideration of social issues like employment opportunities generated due to EV supply chain and loss due to closure of conventional vehicles are missing in the literature. Developing a socially acceptable supply chain model of EV is still in the nascent stage and needs further exploration.

6. Future directions The limited studies based on moderating and mediating variables gives a direction for future research and exploration. There is a scarcity of articles measuring the consequence variables raised a concern about the benefits of EV adoption among consumers. Future studies may be focused on measuring all three dimensions collectively to study the actual benefits of EV adoption. The roles of these variables may vary in different geographies, and future research may be aligned to make it more collaborative. Researchers from multi-disciplinary fields may come forward to develop a theoretically advance model taking inputs from diverse mobility innovations like autonomous driving, shared mobility, and connected mobility.

7. Conclusion

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The integrative literature review framework used in this study mapped the full range of variables across the niche quality EV literature and depicted a nomological network. The illustration of the nomological network integrating key variables added a new dimension in the field of EV adoption. Further, identification of multiple new antecedents, consequences, mediators, and moderators help the researchers to create a more robust EV adoption models. Additionally, measuring the impact of EV adoption at all three sustainability dimensions make it more relevant in the prevailing scenario. Identification of factors using an integrative review methodology on EV adoption is not available, and thus, these findings provide multifaceted insights for academicians and policymakers alike. The integrative review findings guide academia to redirect their research attention to multiple factors like marketing strategies, charging infrastructure resilience, dealership experience, consequence variables, sustainability, etc. The specific policy level recommendations (table 7) based on each of the categories and its usefulness for concerned stakeholders make this review accessible to a larger section of the society. For instance, the literature indicates that countries like France or Norway are better equipped for EV adoption. In contrast, Germany or the UK should first put their effort on decarbonizing a power plant to get GHG reduction benefits. The effective marketing strategy of electric vehicles involves dynamic marketing mix scenarios, communication, and information flow to ensure adequate awareness among consumers. Categorization of antecedents and consequences into motivators and barriers further pinpoint category wise policy implications. The barriers outperform the motivators, thus creating hindrances for EV adoption. Converting the barriers to motivators is a challenge before governments and policymakers in an attempt to improve the EV market share. Consequence variables available in both motivators and barriers categories require further research to get more concrete actual evidence of EV benefits. Possible future actions include enforcement of adequate regional and national policies, improving grid functionality, financial incentives, optimizing charging infrastructures, convenience policies, improving performance, reducing range anxiety, better information sharing, availability of EV models at dealerships, and enhancing environmental awareness.

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References: Abdul-Manan, A.F., 2015. Uncertainty and differences in GHG emissions between electric and conventional gasoline vehicles with implications for transport policy making. Energy Policy, 87, pp.1-7. Adderly, S.A., Manukian, D., Sullivan, T.D. and Son, M., 2018. Electric vehicles and natural disaster policy implications. Energy Policy, 112, pp.437-448. Adnan, N., Nordin, S.M., Rahman, I. and Rasli, A.M., 2017. A new era of sustainable transport: An experimental examination on forecasting adoption behavior of EVs among Malaysian consumer. Transportation Research Part A: Policy and Practice, 103, pp.279-295. Adnan, N., Nordin, S.M., Amini, M.H. and Langove, N., 2018. What make consumer sign up to PHEVs? Predicting Malaysian consumer behavior in adoption of PHEVs. Transportation Research Part A: Policy and Practice, 113, pp.259-278. Alcayaga, A., Wiener, M. and Hansen, E.G., 2019. Towards a framework of smart-circular systems: An integrative literature review. Journal of Cleaner Production, 221, pp.622-634. Annarelli, A., Battistella, C. and Nonino, F., 2016. Product service system: A conceptual framework from a systematic review. Journal of Cleaner Production, 139, pp.1011-1032. Argonne National Laboratory, United States Department of Energy (2016-03-28). "Fact#918: March 28, 2016 - Global Plug-in Light Vehicles Sales Increased By About 80% in 2015". Office of Energy Efficiency & Renewable Energy. Retrieved 2016-03-29. Axsen, J., Bailey, J. and Castro, M.A., 2015. Preference and lifestyle heterogeneity among potential plugin electric vehicle buyers. Energy Economics, 50, pp.190-201. Axsen, J., Goldberg, S. and Bailey, J., 2016. How might potential future plug-in electric vehicle buyers differ from current “Pioneer” owners?. Transportation Research Part D: Transport and Environment, 47, pp.357-370. Bandara, W., Miskon, S. and Fielt, E., 2011. A systematic, tool-supported method for conducting literature reviews in information systems. In Proceedings of the19th European Conference on Information Systems (ECIS 2011). Barbarossa, C., Beckmann, S. C., De Pelsmacker, P., Moons, I., & Gwozdz, W. (2015). A self-identity based model of electric car adoption intention: A cross-cultural comparative study. Journal of Environmental Psychology, 42, 149-160. Barisa, A., Rosa, M. and Kisele, A., 2016. Introducing electric mobility in Latvian municipalities: Results of a survey. Energy Procedia, 95, pp.50-57. Beck, M.J., Rose, J.M. and Hensher, D.A., 2013. Environmental attitudes and emissions charging: An example of policy implications for vehicle choice. Transportation Research Part A: Policy and Practice, 50, pp.171-182.

Journal Pre-proof

Berkeley, N., Jarvis, D. and Jones, A., 2018. Analysing the take up of battery electric vehicles: An investigation of barriers amongst drivers in the UK. Transportation Research Part D: Transport and Environment, 63, pp.466-481. Bjerkan, K.Y., Nørbech, T.E. and Nordtømme, M.E., 2016. Incentives for promoting battery electric vehicle (BEV) adoption in Norway. Transportation Research Part D: Transport and Environment, 43, pp.169-180. Biresselioglu, M.E., Kaplan, M.D. and Yilmaz, B.K., 2018. Electric mobility in Europe: A comprehensive review of motivators and barriers in decision making processes. Transportation Research Part A: Policy and Practice, 109, pp.1-13. Brand, C., Cluzel, C. and Anable, J., 2017. Modeling the uptake of plug-in vehicles in a heterogeneous car market using a consumer segmentation approach. Transportation Research Part A: Policy and Practice, 97, pp.121-136. Breetz, H.L. and Salon, D., 2018. Do electric vehicles need subsidies? Ownership costs for conventional, hybrid, and electric vehicles in 14 US cities. Energy Policy, 120, pp.238-249. Bunsen, T., Cazzola, P., Gorner, M., Paoli, L., Scheffer, S., Schuitmaker, R., Tattini, J. and Teter, J., 2018. Global EV Outlook 2018: Towards cross-modal electrification. Cahill, E.C., Davies-Shawhyde, J. and Turrtentine, T.S., 2014. New car dealers and retail innovation in California's plug-in electric vehicle market (No. UCD-ITS-WP-14-04). Callahan, J.L., 2010. Constructing a manuscript: Distinguishing integrative literature reviews and conceptual and theory articles. Human Resource Development Review, 9(3), pp.300-304. Casals, L.C., Martinez-Laserna, E., García, B.A. and Nieto, N., 2016. Sustainability analysis of the electric vehicle use in Europe for CO2 emissions reduction. Journal of cleaner production, 127, pp.425-437. Cherubini, S., Iasevoli, G. and Michelini, L., 2015. Product-service systems in the electric car industry: critical success factors in marketing. Journal of Cleaner Production, 97, pp.40-49. Chen, Z., Liu, W. and Yin, Y., 2017. Deployment of stationary and dynamic charging infrastructure for electric vehicles along traffic corridors. Transportation Research Part C: Emerging Technologies, 77, pp.185-206. Choi, H., Shin, J. and Woo, J., 2018. Effect of electricity generation mix on battery electric vehicle adoption and its environmental impact. Energy Policy, 121, pp.13-24. Choi, W. and Song, H.H., 2018. Well-to-wheel greenhouse gas emissions of battery electric vehicles in countries dependent on the import of fuels through maritime transportation: A South Korean case study. Applied Energy, 230, pp.135-147. Corbin, J.M. and Strauss, A., 1990. Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative sociology, 13(1), pp.3-21. Cooper, H., 2015. Research synthesis and meta-analysis: A step-by-step approach (Vol. 2). Sage publications.

Journal Pre-proof

Dimitropoulos, A., Rietveld, P. and Van Ommeren, J.N., 2013. Consumer valuation of changes in driving range: A meta-analysis. Transportation Research Part A: Policy and Practice, 55, pp.27-45. Dorcec, L., Pevec, D., Vdovic, H., Babic, J. and Podobnik, V., 2019. How do people value electric vehicle charging service? A gamified survey approach. Journal of Cleaner Production, 210, pp.887-897. Egbue, O. and Long, S., 2012. Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions. Energy policy, 48, pp.717-729. Eising, J.W., van Onna, T. and Alkemade, F., 2014. Towards smart grids: Identifying the risks that arise from the integration of energy and transport supply chains. Applied Energy, 123, pp.448-455. European Commission, 2016. Implementing the Energy Performance of Buildings Directive 2016, Country Reports. European Commission, Brussels. Falcão, E.A.M., Teixeira, A.C.R. and Sodré, J.R., 2017. Analysis of CO2 emissions and techno-economic feasibility of an electric commercial vehicle. Applied energy, 193, pp.297-307. Galvan, J.L. and Galvan, M.C., 2017. Writing literature reviews: A guide for students of the social and behavioral sciences. Routledge. Gardner, W.L., Cogliser, C.C., Davis, K.M. and Dickens, M.P., 2011. Authentic leadership: A review of the literature and research agenda. The Leadership Quarterly, 22(6), pp.1120-1145. Green, B.N., Johnson, C.D. and Adams, A., 2006. Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. Journal of chiropractic medicine, 5(3), pp.101-117. Gnann, T., Plötz, P., Kühn, A. and Wietschel, M., 2015. Modelling market diffusion of electric vehicles with real world driving data–German market and policy options. Transportation Research Part A: Policy and Practice, 77, pp.95-112. Haddadian, G., Khodayar, M. and Shahidehpour, M., 2015. Accelerating the global adoption of electric vehicles: barriers and drivers. The Electricity Journal, 28(10), pp.53-68. Han, L., Wang, S., Zhao, D. and Li, J., 2017. The intention to adopt electric vehicles: Driven by functional and non-functional values. Transportation Research Part A: Policy and Practice, 103, pp.185-197. Hardman, S., 2019. Understanding the impact of reoccurring and non-financial incentives on plug-in electric vehicle adoption–A review. Transportation Research Part A: Policy and Practice, 119, pp.1-14. He, X. and Zhan, W., 2018. How to activate moral norm to adopt electric vehicles in China? An empirical study based on extended norm activation theory. Journal of Cleaner Production, 172, pp.3546-3556. He, X., Zhan, W. and Hu, Y., 2018. Consumer purchase intention of electric vehicles in China: The roles of perception and personality. Journal of Cleaner Production, 204, pp.1060-1069. He Lin, Mingxian Wang, Wei Chen, Guenter Conzelmann; 2014; Incorporating social impact on new product adoption in choice modeling: A case study in green vehicles; Transportation Research Part D; 32 ; 421–434.

Journal Pre-proof

Hess, S., Fowler, M., Adler, T. and Bahreinian, A., 2012. A joint model for vehicle type and fuel type choice: evidence from a cross-nested logit study. Transportation, 39(3), pp.593-625. Helveston, J.P., Liu, Y., Feit, E.M., Fuchs, E., Klampfl, E. and Michalek, J.J., 2015. Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the US and China. Transportation Research Part A: Policy and Practice, 73, pp.96-112. Hirunyawipada, T. and Paswan, A.K., 2006. Consumer innovativeness and perceived risk: implications for high technology product adoption. Journal of consumer marketing, 23(4), pp.182-198. Hofmann, J., Guan, D., Chalvatzis, K. and Huo, H., 2016. Assessment of electrical vehicles as a successful driver for reducing CO2 emissions in China. Applied energy, 184, pp.995-1003. Huang, Y. and Qian, L., 2018. Consumer preferences for electric vehicles in lower tier cities of China: Evidences from south Jiangsu region. Transportation Research Part D: Transport and Environment, 63, pp.482-497. Jansson, J., Nordlund, A. and Westin, K., 2017. Examining drivers of sustainable consumption: The influence of norms and opinion leadership on electric vehicle adoption in Sweden. Journal of Cleaner Production, 154, pp.176-187. Jenn, A., Azevedo, I.L. and Fischbeck, P., 2015. How will we fund our roads? A case of decreasing revenue from electric vehicles. Transportation research part A: policy and practice, 74, pp.136-147. Jensen, A.F., Cherchi, E. and Mabit, S.L., 2013. On the stability of preferences and attitudes before and after experiencing an electric vehicle. Transportation Research Part D: Transport and Environment, 25, pp.24-32. Kaptan, G., Shiloh, S. and Önkal, D., 2013. Values and risk perceptions: A cross‐cultural examination. Risk Analysis: An International Journal, 33(2), pp.318-332. Karaaslan, E., Noori, M., Lee, J., Wang, L., Tatari, O. and Abdel-Aty, M., 2018. Modeling the effect of electric vehicle adoption on pedestrian traffic safety: An agent-based approach. Transportation Research Part C: Emerging Technologies, 93, pp.198-210. Krupa, J.S., Rizzo, D.M., Eppstein, M.J., Lanute, D.B., Gaalema, D.E., Lakkaraju, K. and Warrender, C.E., 2014. Analysis of a consumer survey on plug-in hybrid electric vehicles. Transportation Research Part A: Policy and Practice, 64, pp.14-31. Lieven, T., 2015. Policy measures to promote electric mobility–A global perspective. Transportation Research Part A: Policy and Practice, 82, pp.78-93. Liao, Z., Xu, C.K., Cheng, H. and Dong, J., 2018. What drives environmental innovation? A content analysis of listed companies in China. Journal of cleaner production, 198, pp.1567-1573. Laberteaux, K.P. and Hamza, K., 2018. A study on opportune reduction in greenhouse gas emissions via adoption of electric drive vehicles in light duty vehicle fleets. Transportation Research Part D: Transport and Environment, 63, pp.839-854. Lepitzki, J. and Axsen, J., 2018. The role of a low carbon fuel standard in achieving long-term GHG reduction targets. Energy Policy, 119, pp.423-440.

Journal Pre-proof

Letmathe, P. and Suares, M., 2017. A consumer-oriented total cost of ownership model for different vehicle types in Germany. Transportation Research Part D: Transport and Environment, 57, pp.314-335. Lévay, P.Z., Drossinos, Y. and Thiel, C., 2017. The effect of fiscal incentives on market penetration of electric vehicles: A pairwise comparison of total cost of ownership. Energy Policy, 105, pp.524-533. Liberman, M., Trope, Y., Stephan, E., 2007. Psychological distance. In: Kruglanski, A.W., Higgins, E.T. (Eds.), Social Psychology: Handbook of Basic Principles.Guilford Press, New York, NY. Li, Y., Zhan, C., de Jong, M. and Lukszo, Z., 2016. Business innovation and government regulation for the promotion of electric vehicle use: lessons from Shenzhen, China. Journal of Cleaner Production, 134, pp.371-383. Lim, M.K., Mak, H.Y. and Rong, Y., 2014. Toward mass adoption of electric vehicles: impact of the range and resale anxieties. Manufacturing & Service Operations Management, 17(1), pp.101-119. Liu, X., Roberts, M.C. and Sioshansi, R., 2017. Spatial effects on hybrid electric vehicle adoption. Transportation Research Part D: Transport and Environment, 52, pp.85-97. Liu, H. and Wang, D.Z., 2017. Locating multiple types of charging facilities for battery electric vehicles. Transportation Research Part B: Methodological, 103, pp.30-55. Luo, P., Mu, D., Xue, H., Ngo-Duc, T., Dang-Dinh, K., Takara, K., Nover, D. and Schladow, G., 2018. Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions. Scientific reports, 8(1), p.12623. Luo, P., Kang, S., Apip, M.Z., Lyu, J., Aisyah, S., Binaya, M., Regmi, R.K. and Nover, D., 2019. Water quality trend assessment in Jakarta: A rapidly growing Asian megacity. PloS one, 14(7). Madina, C., Zamora, I. and Zabala, E., 2016. Methodology for assessing electric vehicle charging infrastructure business models. Energy Policy, 89, pp.284-293. Mak, H.Y., Rong, Y. and Shen, Z.J.M., 2013. Infrastructure planning for electric vehicles with battery swapping. Management Science, 59(7), pp.1557-1575. Matthews, L., Lynes, J., Riemer, M., Del Matto, T. and Cloet, N., 2017. Do we have a car for you? Encouraging the uptake of electric vehicles at point of sale. Energy Policy, 100, pp.79-88. McCarney, R., Warner, J., Iliffe, S., Van Haselen, R., Griffin, M. and Fisher, P., 2007. The Hawthorne Effect: a randomised, controlled trial. BMC medical research methodology, 7(1), p.30. McLeay, F., Yoganathan, V., Osburg, V.S. and Pandit, A., 2018. Risks and drivers of hybrid car adoption: A cross-cultural segmentation analysis. Journal of Cleaner Production, 189, pp.519-528. Meisel, S. and Merfeld, T., 2018. Economic incentives for the adoption of electric vehicles: A classification and review of e-vehicle services. Transportation Research Part D: Transport and Environment, 65, pp.264-287.

Journal Pre-proof

Melliger, M.A., Van Vliet, O.P. and Liimatainen, H., 2018. Anxiety vs reality–Sufficiency of battery electric vehicle range in Switzerland and Finland. Transportation Research Part D: Transport and Environment, 65, pp.101-115. Melton, N., Axsen, J. and Goldberg, S., 2017. Evaluating plug-in electric vehicle policies in the context of long-term greenhouse gas reduction goals: Comparing 10 Canadian provinces using the “PEV policy report card”. Energy Policy, 107, pp.381-393. Mohamed, M., Higgins, C.D., Ferguson, M. and Réquia, W.J., 2018. The influence of vehicle body type in shaping behavioural intention to acquire electric vehicles: A multi-group structural equation approach. Transportation Research Part A: Policy and Practice, 116, pp.54-72. Moons, I., & De Pelsmacker, P. (2012). Emotions as determinants of electric car usage intention. Journal of Marketing Management, 28(3-4), 195-237. Morgan, S., Pullon, S. and McKinlay, E., 2015. Observation of interprofessional collaborative practice in primary care teams: an integrative literature review. International journal of nursing studies, 52(7), pp.1217-1230. Morioka, S.N. and de Carvalho, M.M., 2016. A systematic literature review towards a conceptual framework for integrating sustainability performance into business. Journal of Cleaner Production, 136, pp.134-146. Morton, C., Lovelace, R. and Anable, J., 2017. Exploring the effect of local transport policies on the adoption of low emission vehicles: Evidence from the London Congestion Charge and Hybrid Electric Vehicles. Transport Policy, 60, pp.34-46. Nayum, A., Klöckner, C.A. and Mehmetoglu, M., 2016. Comparison of socio-psychological characteristics of conventional and battery electric car buyers. Travel Behaviour and Society, 3, pp.8-20. Neaimeh, M., Salisbury, S.D., Hill, G.A., Blythe, P.T., Scoffield, D.R. and Francfort, J.E., 2017. Analysing the usage and evidencing the importance of fast chargers for the adoption of battery electric vehicles. Energy Policy, 108, pp.474-486. Nian, V., Hari, M.P. and Yuan, J., 2019. A new business model for encouraging the adoption of electric vehicles in the absence of policy support. Applied energy, 235, pp.1106-1117. Nichols, B.G., Kockelman, K.M. and Reiter, M., 2015. Air quality impacts of electric vehicle adoption in Texas. Transportation Research Part D: Transport and Environment, 34, pp.208-218. Noel, L. and McCormack, R., 2014. A cost benefit analysis of a V2G-capable electric school bus compared to a traditional diesel school bus. Applied Energy, 126, pp.246-255. Onat, N.C., Kucukvar, M. and Tatari, O., 2015. Conventional, hybrid, plug-in hybrid or electric vehicles? State-based comparative carbon and energy footprint analysis in the United States. Applied Energy, 150, pp.36-49. Onat, N.C., Kucukvar, M., Tatari, O. and Zheng, Q.P., 2016. Combined application of multi-criteria optimization and life-cycle sustainability assessment for optimal distribution of alternative passenger cars in US. Journal of Cleaner Production, 112, pp.291-307.

Journal Pre-proof

Onat, N.C., Kucukvar, M. and Tatari, O., 2018. Well-to-wheel water footprints of conventional versus electric vehicles in the United States: A state-based comparative analysis. Journal of Cleaner Production, 204, pp.788-802. Palmer, K., Tate, J.E., Wadud, Z. and Nellthorp, J., 2018. Total cost of ownership and market share for hybrid and electric vehicles in the UK, US and Japan. Applied energy, 209, pp.108-119. Pautasso, M., 2013. Ten simple rules for writing a literature review. Paterakis, N.G., Erdinc, O., Bakirtzis, A.G. and Catalão, J.P., 2015. Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies. IEEE Transactions on Industrial Informatics, 11(6), pp.1509-1519. Plötz, P., Schneider, U., Globisch, J. and Dütschke, E., 2014. Who will buy electric vehicles? Identifying early adopters in Germany. Transportation Research Part A: Policy and Practice, 67, pp.96-109. Qian, L. and Yin, J., 2017. Linking Chinese cultural values and the adoption of electric vehicles: The mediating role of ethical evaluation. Transportation Research Part D: Transport and Environment, 56, pp.175-188. Quak, H., Nesterova, N. and van Rooijen, T., 2016. Possibilities and barriers for using electric-powered vehicles in city logistics practice. Transportation Research Procedia, 12, pp.157-169. Rastogi, A., Pati, S.P., Krishnan, T.N. and Krishnan, S., 2018. Causes, contingencies, and consequences of disengagement at work: an integrative literature review. Human Resource Development Review, 17(1), pp.62-94. Ravichandran, A., Sirouspour, S., Malysz, P. and Emadi, A., 2018. A chance-constraints-based control strategy for microgrids with energy storage and integrated electric vehicles. IEEE Transactions on Smart Grid, 9(1), pp.346-359. Rezvani, Z., Jansson, J. and Bodin, J., 2015. Advances in consumer electric vehicle adoption research: A review and research agenda. Transportation research part D: transport and environment, 34, pp.122-136. Rietmann, N. and Lieven, T., 2019. How policy measures succeeded to promote electric mobility– Worldwide review and outlook. Journal of Cleaner Production, 206, pp.66-75. Sang, Y.N. and Bekhet, H.A., 2015. Modelling electric vehicle usage intentions: an empirical study in Malaysia. Journal of Cleaner Production, 92, pp.75-83. Schuitema, G., Anable, J., Skippon, S. and Kinnear, N., 2013. The role of instrumental, hedonic and symbolic attributes in the intention to adopt electric vehicles. Transportation Research Part A: Policy and Practice, 48, pp.39-49. Sen, B., Noori, M. and Tatari, O., 2017. Will Corporate Average Fuel Economy (CAFE) Standard help? Modeling CAFE's impact on market share of electric vehicles. Energy Policy, 109, pp.279-287. Shao, J., Taisch, M. and Ortega-Mier, M., 2016. A grey-DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) analysis on the barriers between environmentally friendly products and consumers: practitioners' viewpoints on the European automobile industry. Journal of Cleaner Production, 112, pp.3185-3194.

Journal Pre-proof

Siegel, R., Antony, J., Garza-Reyes, J.A., Cherrafi, A. and Lameijer, B., 2019. Integrated green lean approach and sustainability for SMEs: From literature review to a conceptual framework. Journal of Cleaner Production, p.118205. Sierzchula, W., Bakker, S., Maat, K. and Van Wee, B., 2014. The influence of financial incentives and other socio-economic factors on electric vehicle adoption. Energy Policy, 68, pp.183-194. Skippon, S.M., Kinnear, N., Lloyd, L. and Stannard, J., 2016. How experience of use influences massmarket drivers’ willingness to consider a battery electric vehicle: a randomised controlled trial. Transportation Research Part A: Policy and Practice, 92, pp.26-42. Smith, B., Olaru, D., Jabeen, F. and Greaves, S., 2017. Electric vehicles adoption: Environmental enthusiast bias in discrete choice models. Transportation Research Part D: Transport and Environment, 51, pp.290303. Sovacool, B.K., Kester, J., Noel, L. and de Rubens, G.Z., 2018. The demographics of decarbonizing transport: the influence of gender, education, occupation, age, and household size on electric mobility preferences in the Nordic region. Global Environmental Change, 52, pp.86-100. Sovacool, B.K., Abrahamse, W., Zhang, L. and Ren, J., 2019. Pleasure or profit? Surveying the purchasing intentions of potential electric vehicle adopters in China. Transportation Research Part A: Policy and Practice, 124, pp.69-81. Spencer, J., Lilley, D. and Porter, S., 2015. The opportunities that different cultural contexts create for sustainable design: a laundry care example. Journal of Cleaner Production, 107, pp.279-290. Sun, X.H., Yamamoto, T. and Morikawa, T., 2016. Fast-charging station choice behavior among battery electric vehicle users. Transportation Research Part D: Transport and Environment, 46, pp.26-39. Sykes, M. and Axsen, J., 2017. No free ride to zero-emissions: Simulating a region's need to implement its own zero-emissions vehicle (ZEV) mandate to achieve 2050 GHG targets. Energy Policy, 110, pp.447-460. Torraco, R.J., 2005. Writing integrative literature reviews: Guidelines and examples. Human resource development review, 4(3), pp.356-367. Torraco, R.J., 2016. Writing integrative literature reviews: Using the past and present to explore the future. Human Resource Development Review, 15(4), pp.404-428. Travesset-Baro, O., Rosas-Casals, M. and Jover, E., 2015. Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra. Transportation Research Part D: Transport and Environment, 34, pp.16-26. Ustun, T.S., Cali, U. and Kisacikoglu, M.C., 2015, October. Energizing microgrids with electric vehicles during emergencies—Natural disasters, sabotage and warfare. In 2015 IEEE International Telecommunications Energy Conference (INTELEC) (pp. 1-6). IEEE. Wang, S., Fan, J., Zhao, D., Yang, S. and Fu, Y., 2016. Predicting consumers’ intention to adopt hybrid electric vehicles: using an extended version of the theory of planned behaviour model. Transportation, 43(1), pp.123-143.

Journal Pre-proof

Wang, Z., Zhao, C., Yin, J. and Zhang, B., 2017. Purchasing intentions of Chinese citizens on new energy vehicles: How should one respond to current preferential policy?. Journal of Cleaner Production, 161, pp.1000-1010. Wee, S., Coffman, M. and La Croix, S., 2018. Do electric vehicle incentives matter? Evidence from the 50 US states. Research Policy, 47(9), pp.1601-1610. Wen, Y., MacKenzie, D. and Keith, D., 2015. Modeling charging choices of BEV owners using stated preference data. In EVS28 International Electric Vehicle Symposium and Exhibition. Whittemore, R. and Knafl, K., 2005. The integrative review: updated methodology. Journal of advanced nursing, 52(5), pp.546-553. White, L.V. and Sintov, N.D., 2017. You are what you drive: Environmentalist and social innovator symbolism drives electric vehicle adoption intentions. Transportation Research Part A: Policy and Practice, 99, pp.94-113. Wilding, R., Wagner, B., Seuring, S. and Gold, S., 2012. Conducting content‐analysis based literature reviews in supply chain management. Supply Chain Management: An International Journal. Wolbertus, R., Kroesen, M., van den Hoed, R. and Chorus, C.G., 2018. Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments. Transportation Research Part D: Transport and Environment, 62, pp.283-297. Wu, G., Inderbitzin, A. and Bening, C., 2015. Total cost of ownership of electric vehicles compared to conventional vehicles: A probabilistic analysis and projection across market segments. Energy Policy, 80, pp.196-214. Wu, P., 2018. Which battery-charging technology and insurance contract is preferred in the electric vehicle sharing business?. Transportation Research Part A: Policy and Practice. Wu, X., 2018. Role of workplace charging opportunities on adoption of plug-in electric vehicles–Analysis based on GPS-based longitudinal travel data. Energy Policy, 114, pp.367-379. Xiong, Y., Wang, B., Chu, C.C. and Gadh, R., 2018. Vehicle grid integration for demand response with mixture user model and decentralized optimization. Applied Energy, 231, pp.481-493. Yan, S., 2018. The economic and environmental impacts of tax incentives for battery electric vehicles in Europe. Energy policy, 123, pp.53-63. Yoon, T., Cherry, C.R., Ryerson, M.S. and Bell, J.E., 2019. Carsharing demand estimation and fleet simulation with EV adoption. Journal of Cleaner Production, 206, pp.1051-1058. Zhao, Y., Noori, M. and Tatari, O., 2016. Vehicle to Grid regulation services of electric delivery trucks: Economic and environmental benefit analysis. Applied Energy, 170, pp.161-175. Zhao, S.J. and Heywood, J.B., 2017. Projected pathways and environmental impact of China's electrified passenger vehicles. Transportation Research Part D: Transport and Environment, 53, pp.334-353.

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Moderating variables

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0 1 Social

Fig. 6. Distribution of articles on sustainability dimensions measuring consequences

1. Charging infrastructure development 2. Total cost of ownership 3. Purchase based incentive policies 4. Business model development 5. Range anxiety 6. Potential environmental benefits 7. Government regulations 8. Consumers heterogeneity 9. Vehicle design and features 10. Psychological characteristics 11. Electricity generation mix 12. Symbolic attributes 13. Battery cost 14. Environmental concern and awareness 15. Performance measures 16. Used based incentive policies 17. Electric load distribution and management 18. Willingness to pay 19. Charging behaviour 20. Perceived risks 21. Marketing strategies 22. Dealership experience 23. Charging infrastructure resilience

Antecedent variables

Environmental impacts:     

Moderating variables:  

Hyperbolic discounting Environmental concern

Consumer adoption intention/ behaviour for EV

Tail pipe GHG reduction WTW GHG reduction LCA analysis, impact GWP reduction Water footprint

Social impacts: Actual adoption of EV

1. Pedestrian safety 2. Impact on social welfare

Economic impacts: 1. 2. 3. 4.

Socio-demographic variables:      

Age, Gender Income, Education Multi-car household Family size, GDP Population, Family size etc.

Sales of EVs/PHEVs Market share of EVs/PHEVs NPV analysis Revenue generation/ reduction, Profit

Consequence variables

Fig. 7. Broader level relationship map of EV adoption

Table 1: Review protocol Filter type

Description

Electric Vehicles

1. First level filter fulfill the search string

AND

meets words in the Title, Abstract, Keywords.

Adoption

2. Articles must be from Scholarly (Peer

EBSCO host

Science

Wiley online

(Business Source

Direct

library

Premier)

(Elsevier)

446

417

14

325

417

14

219

334

7

135

235

2

130

229

2

Reviewed) Journals Initial Screening

Removing duplicates

Journal ranking

Selecting only Scopus Q1 Journal ranking in

analysis and

2017.

consolidation Journal field analysis and consolidation

(1) Select only field of Business Management and policy. (2) Select only field of Transportation and Environment science together

Time line freezing

Publications after 2010.

Sample size

Unique articles for review

239

Table 2: Broad classification of research methods used over the time Year

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019 Grand Total

Research design Qualitative

2

1

1

1

1

1

3

5

15

7

9

12

16

25

19

41

62

30

224

1

1

1

2

1

4

5

3

18

Experimental

2

1

1

3

Game theoretical

1

1

1

3

Quantitative

3

Methodology used Case Study

Interview based

1

1

1

Literature review

1 1

1

Longitudinal study Meta-analysis

1

Observation based

1

Optimization techniques

1

Secondary data analysis Simulation based

1 2

Survey based Mixed (combination of above)

7 2

8

2

6

1

3

2

2 1

2

1

2

2

8

3

2

2

3

7

9

5

32

1

3

2

2

5

11

3

28

3

4

1

5

8

4

10

5

3

45

2

1

4

4

4

5

12

24

7

63

3

1

1

2

2

1

2

5

18

4

4

8

8

6

10

17

9

71

1

Country wise stratification (top 5) USA

1

4

Across countries

1

1

China

1

1

4

3

1

Germany

1

United Kingdom

1

2

1

1

1

1

2

2

9

3

25

8

10

6

25

1 2

5 3

2

13 2

Table 3: Categorization of antecedents Subcatego ry

Anteceden ts

Economic perspective Total cost of ownership Business model development Battery cost and technology Willingness to pay

Charging infrastructure readiness Charging infrastructure development Range anxiety

Electric load distribution and management Charging behaviour Charging infrastructure resilience

Consumers perspectives Psychological characteristics Consumers heterogeneity Symbolic attributes Environmental concern and awareness Perceived risks

Government policies and regulations Purchase-based incentive policies Use-based incentive policies Government regulations

Vehicle design and performance Vehicle design features Performance measures

Environmental perspectives

Marketing Perspectives

Potential environmental benefits Electricity generation mix

Marketing strategies Dealership experience

12

Source Adnan et al. (2018) Adnan et al. (2018) Adnan et al. (2018) Adnan et al. (2018) He et al. (2018) White and Sintov (2017) White and Sintov (2017) Qian and Yin (2017)

Antecedents category

Environmen tal concern

Independent variable Environmental concern Environmental concern Environmental concern Environmental concern Environmental concern Concern about climate change Concern about climate change Human–nature orientation

Qian and Yin (2017)

Human–nature orientation

Schuitema et al. et al (2013)

Instrumental attributes

Schuitema et al. et al (2013) Schuitema et al. et al (2013) Schuitema et al. et al (2013)

Vehicle design and performance measures

Instrumental attributes Instrumental attributes Instrumental attributes

Table 4: Summary of mediation effect analysis Mediating variable Dependent Dependent Predicted variable variable direction category Attitude towards Intention to + adoption adopt Subjective Norm Intention to adopt Perceived behavioral Intention to + control adopt Personal moral norm Intention to + adopt Intention to EV purchase Perceived fee adopt intention Environmentalist EV adoption + intentions Social Innovator EV adoption + intentions Deontological PHEV + Evaluation adoption intention Deontological BEV Evaluation adoption intention Hedonic attributes Intention to + adopt a PHEV as a second car Symbolic attributes Intention to + adopt a PHEV as a second car Hedonic attributes Intention to + Intention to adopt BEVs adopt Symbolic attributes Intention to + adopt BEVs

Findings Supported (Indirect effect) Not Supported (Indirect effect) Supported (Indirect effect) Supported (Indirect effect) Partially mediated Partially mediated Partially mediated Indirect-only mediation Competitive mediation Fully mediated Fully mediated Partially mediated Partially mediated

Qian and Yin (2017)

Long-term orientation

Deontological Evaluation

Face consciousness

Deontological Evaluation

Qian and Yin (2017)

Long-term orientation

Deontological Evaluation

Qian and Yin (2017)

Face consciousness

Deontological Evaluation

Han et al (2017) Han et al (2017)

Social identity value Social responsibility value Epistemic value

Attitude

Emotional value

Attitude

Personal innovativeness Personal innovativeness

Perceived monetary benefits Perceived risk

Qian and Yin (2017)

Han et al (2017) Han et al (2017) He et al. (2018) He et al. (2018)

Cultural values

psychologic al characteristi cs

Attitude Attitude

Intention to adopt

Intention to adopt

PHEV adoption intention PHEV adoption intention BEV adoption intention BEV adoption intention Adoption intention Adoption intention

+

Indirect-only mediation

+

Complementary mediation

+

Complementary mediation

+

Complementary mediation

+

Partially mediated

+

Full mediated

Adoption intention Adoption intention EV purchase intention EV purchase intention

+

Partially mediated

+

Partially mediated

+

Partially mediated

-

Partially mediated

Table 5: Summary of moderation effect analysis Source Adnan et al (2018) Adnan et al. (2017b) He and Zhan (2018) He and Zhan (2018) He et al. (2018) He et al. (2018) He et al. (2018) He et al. (2018)

Antecedents category Purchase intention

Independent variable Intention to adopt Consumer purchase intention

Moderating variable Hyperbolic discounting Environmental concern

Personal norms

Perceived price

Dependent variable category Actual adoption Purchase intention

Personal norms Psychological Personal characteristics innovativeness (PI) Environmental concern Positive utility Negative utility

Perceived complexity Gender Gender Gender Gender

Purchase intention

Dependent variable Actual adoption of PHEVs Actual adoption of EVs

Predicted direction +

Findings

+

Supported

Intention to adopt EV Intention to adopt EV EV purchase intention EV purchase intention EV purchase intention EV purchase intention

-

Supported

nonlinear

Supported

+

Supported

+

Supported

+

Supported

-

Supported

Supported

Table 6: Motivators and barriers of EV adoption (Antecedents, Consequences) Variables

Motivators       

Potential environmental benefits (Laberteaux and Hamza, 2018; Zhao and Heywood, 2017) Purchase based incentive policies (Melton et al., 2017; Bjerkan et al., 2016) Business model development (Wu P, 2018; Nian et al., 2019) Government regulations (Melton et al., 2017; Lepitzki and Axsen, 2018) Symbolic attributes (White and Sintov, 2017; He and Zhan, 2018) Use based incentive policies (Cherchi, 2017; Mersky et al., 2016) Environmental concern and awareness (Schuitema et al., 2013; Smith et al., 2017)

Barriers 

Charging infrastructure development (Barisa et al., 2016; Berkeley et al., 2018)



Total cost of ownership (Lévay et al., 2017; Palmer et al., 2018) Range anxiety (Skippon et al.,2016; Melliger et al.,2018) Electricity generation mix (Zhao and Heywood, 2017; Choi et al., 2018) Vehicles design and features (Barisa et al.,2016; Mohamed et al.,2018) Consumers heterogeneity (Axsen et al., 2015; Huang and Quin, 2018) Psychological characteristics (Nayum et al.,2016; He et al., 2018) Battery cost and technology (Jensen et al., 2013; Letmathe and Suares, 2017) Performance measure (Sovacool et al., 2018; Globisch et al., 2017) Electricity load distribution and management (Yilmaz and Philip, 2013; Ravichandran et al., 2018) Charging behaviour (Sun et al., 2016; Wolbertus et al., 2018) Perceived risks (Barbarossa et al., 2015; Berkeley et al., 2018) Willingness to pay (Skippon et al.,2016; Dorcec et al., 2019)

      

Antecedents

   

    Consequences

 

Environmental impact – GHG reduction without LCA (Zhao et al.,2016; Choi and Song, 2018) Economic impact – Revenue gain, more NPV and profit (Zhao et al., 2016; Noel and McCormack, 2014) Social impact – Job creation (Haddadian et al., 2015)



Marketing strategies (Barisa et al., 2016; Shao et al., 2016) Charging infrastructure resilience (Ustun et al., 2015; Adderly et al., 2018) Dealership experience (Cahill et al., 2014; Matthews et al., 2017) Environmental impact – Life cycle GHG reduction and water footprint (Onat et al., 2018)



Economic impact – (Revenue loss) – (Jenn et al., 2015)



Social impact – Pedestrians safety (Karaaslan et al., 2018)

Subcategory Economic perspective

Stack holders Manufacturers, policymakers

Charging infrastructure

Government, policymakers, service providers

Consumers perspectives

Policymakers, Governments

Government policies and regulations

Policymakers, Governments

Vehicle design and performance

Manufacturers

Environmental perspectives

Governments, energy providers, policymakers

Marketing perspectives

Dealerships and franchises

Table 7: Policy recommendations Policy recommendations                            

Focus on reducing total cost of ownerships from user's perspectives Improve consumer's willingness to pay for EV and charging services Development of business models to make EV economically viable Focus on battery cost technological improvements and cost reduction Focus on fast charging infrastructures Facilitation of home, residential and workplace charging options Improve the charging infrastructure in densely populated area Optimization of charging network locations to minimize range anxiety Focus on charging infrastructure resilience Focus new charging technologies like vehicle to grid and smart grid Focus on potential user's psychological characteristics Consider EV as ‘pro-environmental identity’ and ‘social status’ symbols Programs to improve user's environmental concern and awareness Policy decisions should consider consumer's heterogeneity Elimination of perceived risks related with performance, resale, servicing etc. Use of purchase based incentives like subsidy, discount, tax exemptions Focus on use based incentives like free parking, bus lane access, toll free Government regulations to de incentivize gasoline vehicles Regulations to improve fuel efficiency, zero emission mandate etc. Development of EV variants in line with consumer's expectations Focus on functional attributes like range, top speed, acceleration Designing EVs to improve reliability, riding comfort, convenience of use Providing actual environmental benefits of EVs in different scenarios Switching of electricity generation based on renewables Carbon intensive electricity generating nations should focus on decarbonizing their electricity generation first to get expected EV environmental benefits More availability of EV models at dealerships for view and test drive East accessibility and no waiting period for EV models Improve commission and sales schemes for EV models

  Socio-demographic factors

Governments, policymakers

  

Marketing strategies should be consumer-centric, focus on user's convenience as well as environmental benefits based promotion campaigns EV marketing strategies should emphasize the use of EV as an identity symbol and based on cultural values Targeting higher income and high educated users Focus on younger and middle age group as potential users Focus on multi car households, household size, gender, previous car owners etc. based on demographical characteristics