Predicting determinants of consumers' purchase motivation for electric vehicles: An application of Maslow's hierarchy of needs model. (April 2021)

• Record Type: Journal Article
• Authors: Cui, Lixin; Wang, Yonggui; Chen, Weiming; Wen, Wen; Han, Myat Su
• Is Part Of: Energy policy. Volume 151(2021)
• Publication Date: 2021-04
• Subjects: Purchase motivation -- Electric vehicles -- Maslow's hierarchy of needs model -- Multiple regression analysis -- China; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2021.112167
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: Adopting electric vehicles (EVs) is regarded as one of the leading solutions to environmental issues. However, recent research on the promotion of EVs adoption focuses on perceptions and attitudes towards EVs and neglects the importance of different human needs in consumers' purchase decision-making processes. Maslow's Hierarchy of Needs model provides a basic concept stating different human needs which play essential roles in shaping consumers purchase behaviors towards a particular product or service. Based on Maslow's Hierarchy of Needs model, this study predicts the determinants of consumers' purchase motivation for EVs. Data were collected from 550 Chinese residents and analyzed using multiple regression analysis. Result shows that environmental concern is the most significant predictor of EVs purchase motivation, followed by price consciousness, openness to experience, social influence, and self-esteem. From a practical point of view, we make useful recommendations for local policymakers to formulate and implement policies regarding promoting EVs adoption among society. We also provide global EVs marketers and manufacturers with a deeper understanding of Chinese consumers' motivation to purchase EVs. Highlights: This study has extended Maslow's Hierarchy Needs model. We perform multiple regression analysis to test the hypotheses. Environmental concern is found to be the most significant predictor. Suggestions are made for local policymakers and global EVs marketers.