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Identifying Intention-Based Factors Influencing Consumers’ Willingness to Pay for Electric Vehicles: A Sustainable Consumption Paradigm
In an effort to reduce environmental pollution and energy consumption, the Chinese government strongly promotes the usage of electric vehicles. However, studies focusing on assessing consumers’ willingness to pay (WTP) for electric vehicles (EVs) are limited in the country. This research aims to address this research gap by examining influencing factors of consumers’ WTP for EVs in the Chinese perspective. Combined with the existing consumers’ intention factors, the current study further contributed by augmenting the theoretical framework of the Theory of Planned Behavior by including three new dimensions (performance expectancy, information loaded, and perceived risk) to comprehensively analyze the willingness of Chinese consumers. Analysis is performed on survey data from 498 consumers using EVs in Beijing, China. To evaluate formulated hypotheses, structural equation modeling approach is employed. Empirical findings reveal that environmental knowledge and performance expectancy positively and significantly influence behavioral intention. In contrast, overloaded information has a negative impact on behavioral intention. Moreover, subjective norms are significantly and positively related to behavioral intention. The research outcomes further disclose that perceived risk is positively and significantly related to behavioral intention. Finally, behavioral intention has a significant and positive association with WTP for EVs. The study contributes to the literature on sustainable consumption behavior and provides academics and practitioners with essential future directions.
Identifying Intention-Based Factors Influencing Consumers’ Willingness to Pay for Electric Vehicles: A Sustainable Consumption Paradigm
In an effort to reduce environmental pollution and energy consumption, the Chinese government strongly promotes the usage of electric vehicles. However, studies focusing on assessing consumers’ willingness to pay (WTP) for electric vehicles (EVs) are limited in the country. This research aims to address this research gap by examining influencing factors of consumers’ WTP for EVs in the Chinese perspective. Combined with the existing consumers’ intention factors, the current study further contributed by augmenting the theoretical framework of the Theory of Planned Behavior by including three new dimensions (performance expectancy, information loaded, and perceived risk) to comprehensively analyze the willingness of Chinese consumers. Analysis is performed on survey data from 498 consumers using EVs in Beijing, China. To evaluate formulated hypotheses, structural equation modeling approach is employed. Empirical findings reveal that environmental knowledge and performance expectancy positively and significantly influence behavioral intention. In contrast, overloaded information has a negative impact on behavioral intention. Moreover, subjective norms are significantly and positively related to behavioral intention. The research outcomes further disclose that perceived risk is positively and significantly related to behavioral intention. Finally, behavioral intention has a significant and positive association with WTP for EVs. The study contributes to the literature on sustainable consumption behavior and provides academics and practitioners with essential future directions.
Identifying Intention-Based Factors Influencing Consumers’ Willingness to Pay for Electric Vehicles: A Sustainable Consumption Paradigm
ShiYong Zheng (author) / Hua Liu (author) / Weili Guan (author) / Yuping Yang (author) / JiaYing Li (author) / Shah Fahad (author) / Biqing Li (author)
2022
Article (Journal)
Electronic Resource
Unknown
environmental knowledge , sustainable consumption , performance expectancy , information overloaded , behavioral intention , willingness to pay for electric vehicles , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350
Metadata by DOAJ is licensed under CC BY-SA 1.0
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