Who buys New Energy Vehicles in China? Assessing social-psychological predictors of purchasing awareness, intention, and policy

Du, Huibin; Liu, Diyi; Sovacool, Benjamin K.; Wang, Yuru; Ma, Shoufeng; Li, Rita Yi Man

doi:10.1016/j.trf.2018.05.008

Cited by 138 publications

(85 citation statements)

References 74 publications

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“…Perceived behavioral control was defined in this study as assessing their self-perception of eases/difficulties in AV or SAV use intention. The perceived behavioral control has been also criticized as a significant predictor of behavioral intention in TPB model [54,62,65]. In the study, three items used to measure perceived behavior control had high factor loadings and appeared highly internal reliability (Cronbach's alpha = 0.908 for AV model and 0.907 for SAV model).…”

Section: Perceived Behavior Control and Its Implicationsmentioning

confidence: 96%

“…Perceived behavioral control has the same ability as actual behavioral control, which will have a direct impact on behavioral intention. Related research have shown that perceived behavioral control is strong determinants of intentions to purchase new energy vehicles [54,65]. With regards to AVs, several constraints do exist.…”

Section: Perceived Behavioral Control (Pbc)mentioning

confidence: 99%

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Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

et al. 2019

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Autonomous vehicle (AV) is an innovative transport option that has the potential to disrupt all industries tied to transportation systems. The advent of AV technology will bring a novel on-demand mobility pattern such as shared autonomous vehicle (SAV). To promote AV technology, it is important to understand which factors influence travelers’ intention to use AVs and SAVs. This paper collected literature from databases such as Scopus, Web of Science and ScienceDirect, and made a systematic review. The study aims to explore the determinants that influence travelers’ behavioral intentions towards use AVs and SAVs based on an extended version of the theory of planned behavior, which incorporates knowledge and perceived risk. This study was tested empirically using a valid survey sample collected from 906 respondents in China. Structural equation model was conducted to investigate the predictors of intentions to use AVs and SAVs. Results showed that knowledge about AV technology and perceived risk are the two main potential obstacles for travelers to use AVs and SAVs. Attitude significantly affects AVs and SAV choice intentions. Subjective norm is the most critical factor affecting the travelers' intention to use AVs. Perceived behavioral control potentially stymie the travelers' intention to use SAVs. The findings will enhance the understanding of travelers’ choice motivation from psychological and service perspectives, and provide data support for governments and companies in improving travel management strategies and product services.

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Section: Perceived Behavior Control and Its Implicationsmentioning

confidence: 96%

Section: Perceived Behavioral Control (Pbc)mentioning

confidence: 99%

Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

et al. 2019

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“…Regarding psychological factors, Du et al [38] have suggested that attitudes towards behavior, subjective norms, perceived behavioral control and personal norms are strong determinants of intention to purchase an EV. Qian [2] has deemed the use of sustainable products as compatible with a consumer's values and beliefs to be an important factor.…”

Section: Influential Factors To the Adoption Of Electric Vehicles (Evs)mentioning

confidence: 99%

Investigating the Influence of Consumer Behavior and Governmental Policy on the Diffusion of Electric Vehicles in Beijing, China

Yang

Tan

2019

Sustainability

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The adoption behavior of electric vehicles (EVs) has received considerable attention. However, the whole process of purchase behavior has not been well understood. In response, this paper investigates the uptake of EVs in Beijing, China, with a focus on the whole process of purchasing an EV, as well as the relevant policies. Specifically, the classical Howard–Sheth theory is used to the analyze the whole process of EV purchasing, which is divided into four stages here, namely, “Ignored and Neglected”, “Proactive Attention”, “Comparison and Selection of Vehicles” and “Usage Evaluation”. Furthermore, several “what-if” scenarios are set up to quantify the influence of some typical EV-related polices, including the license plate lottery policy and no traffic constriction on battery electric vehicles (BEVs). The results suggest: (1) 63.4% of BEV owners have rigid travel demands with cars; (2) BEVs with a driving range of 500 km, 30-minutes charging time and price of RMB 15,000 are the first choices for the majority of people; (3) 64% of BEV owners are influenced by positive comments from the users around them in the adoption of EVs; (4) the no traffic constriction on BEVs tends to be more influential than the license plate lottery policy in terms of the numbers of applications for the vehicle purchase permits and BEV purchasers, as well as energy saving and vehicular emission reduction. These findings should be helpful for different EV-related stakeholders, such as the government, to shape their policies and promote the development of EVs.

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“…Climate change poses many challenges for sustainable development and energy policy [1,2], and greenhouse gas emissions from human beings will exacerbate global climate change. Therefore, sustained reductions in carbon emissions are required from all sectors of the economy, including households and individuals.…”

Section: Introductionmentioning

confidence: 99%

Residents’ Willingness and Influencing Factors on Action Personal Carbon Trading: A Case Study of Metropolitan Areas in Tianjin, China

Liu

2019

Sustainability

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Personal carbon trading offers a powerful and innovative instrument with which to achieve reductions in carbon emissions. Meanwhile, residents’ personal carbon trading willingness and the factors influencing such willingness have critical effects on the acceptance of personal carbon trading. Therefore, the present research uses a questionnaire survey in metropolitan areas of Tianjin China and the results indicated that most of the interviewees (74.92%) agreed or strongly agreed that they would participant in personal carbon trading. Moreover, according to the results of multiple regression models, governmental policies and residents’ environmental awareness and motivations were positively related to their personal carbon trading willingness. However, personal barriers to personal carbon trading were negatively related to personal carbon trading willingness. Control variables, such as gender and incomes, were not significantly related to personal carbon trading willingness. Thus, monitoring residents’ emission and trading patterns, emphasizing effective, transparent, and fair policies, as well as mitigating uncertainty could all be effective ways to increase the acceptance of personal carbon trading.

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Who buys New Energy Vehicles in China? Assessing social-psychological predictors of purchasing awareness, intention, and policy

Abstract: Who buys new energy vehicles in china? Assessing social-psychological predictors of purchasing awareness, intention, and policy. Transportation Research Part F Traffic Psychology and Behaviour, 58. pp. 56-69.

Cited by 138 publications

References 74 publications

Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China

Investigating the Influence of Consumer Behavior and Governmental Policy on the Diffusion of Electric Vehicles in Beijing, China

Residents’ Willingness and Influencing Factors on Action Personal Carbon Trading: A Case Study of Metropolitan Areas in Tianjin, China

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