A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior |
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| Institution: | 1. School of Business Administration, Shandong Technology and Business University, Yantai, 264005, China;2. Business School, Chengdu University, Chengdu, 610106, China;3. School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731, China;1. Mechanical, Production & Industrial Engineering Department, Delhi Technological University, Delhi 110042, India;2. Operations Management, Management Development Institute, Gurgaon 122007, India;1. School of Management, Shandong University, Jinan, 250100, China;2. School of Business, Qingdao University of Technology, Tsingtao, 266071, China;1. AUT Business School, Auckland University of Technology, 120 Mayoral Drive, Auckland Central, 1010, New Zealand;2. Department of Marketing & Tourism Management, National Chiayi University, 580 Shin-Min Road, Chiayi City, 6000, Taiwan;1. Department of Active Aging Industry, CHA University, South Korea;2. School of AI Healthcare, CHA University, South Korea;1. Graduate School of Business, Universiti Sains Malaysia, Penang, Malaysia;2. School of International Education, Yangtze Normal University, China |
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| Abstract: | Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours. |
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| Keywords: | Multiple criteria decision-making (MCDM) Battery electric vehicle Purchase decision Consumer learning Regret theory |
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