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<p>Battery electric vehicles (BEV) represent a significant shift towards cleaner transportation alternatives. Despite this, adoption varies across generations, with concerns about battery technology influencing consumer acceptance. This study integrates the Kano model with a machine learning approach, utilizing the Random Forest algorithm to identify and prioritize key BEV battery features. The objective is to examine perceptual paradoxes influencing BEV adoption across four generations: Baby Boomers, Generation X, Millennials, and Generation Z. Data were collected from 1281 respondents in Greater Jakarta, Indonesia, the most air-polluted region in Southeast Asia. The findings indicate that Baby Boomers prioritize safety, reliability, and financial assurance, with key features including <i>battery safety standard</i> (ImpScore = 0.117), <i>extended warranty coverage</i> (ImpScore = 0.106), and <i>high battery reliability</i> (ImpScore = 0.0105). Generation X focuses on functionality and cost-efficiency, valuing <i>driving range per charge</i> (ImpScore = 0.116), <i>high-speed charging capability</i> (ImpScore = 0.112), and <i>low replacement cost</i> (ImpScore = 0.105). Millennials prioritize convenience, with key features such as <i>high-speed charging capability</i> (ImpScore = 0.111), <i>driving range per charge</i> (ImpScore = 0.106), and <i>low replacement cost</i> (ImpScore = 0.099). Generation Z emphasizes technological features like <i>battery performance monitoring</i> (ImpScore = 0.116), <i>high-speed charging capability</i> (ImpScore = 0.108), and <i>driving range per charge</i> (ImpScore = 0.103). These generational contrasts highlight a spectrum of preferences, from cautious adoption to embracing technological sophistication. BEV manufacturers must tailor strategies to address older generations’ concerns while meeting younger cohorts’ innovation demands, promoting inclusivity in the transition to sustainable transportation.</p>

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Battery feature paradoxes in battery electric vehicle adoption: machine learning-based innovation for cross-generational strategies

  • 期刊论文

  • 发表日期：

    2025年12月16日

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  • Tutur Wicaksono,
  • Marhadi Marhadi,
  • Julia Loisa,
  • Felliks Feiters Tampinongkol,
  • Krisztina Taralik

摘要

Battery electric vehicles (BEV) represent a significant shift towards cleaner transportation alternatives. Despite this, adoption varies across generations, with concerns about battery technology influencing consumer acceptance. This study integrates the Kano model with a machine learning approach, utilizing the Random Forest algorithm to identify and prioritize key BEV battery features. The objective is to examine perceptual paradoxes influencing BEV adoption across four generations: Baby Boomers, Generation X, Millennials, and Generation Z. Data were collected from 1281 respondents in Greater Jakarta, Indonesia, the most air-polluted region in Southeast Asia. The findings indicate that Baby Boomers prioritize safety, reliability, and financial assurance, with key features including battery safety standard (ImpScore = 0.117), extended warranty coverage (ImpScore = 0.106), and high battery reliability (ImpScore = 0.0105). Generation X focuses on functionality and cost-efficiency, valuing driving range per charge (ImpScore = 0.116), high-speed charging capability (ImpScore = 0.112), and low replacement cost (ImpScore = 0.105). Millennials prioritize convenience, with key features such as high-speed charging capability (ImpScore = 0.111), driving range per charge (ImpScore = 0.106), and low replacement cost (ImpScore = 0.099). Generation Z emphasizes technological features like battery performance monitoring (ImpScore = 0.116), high-speed charging capability (ImpScore = 0.108), and driving range per charge (ImpScore = 0.103). These generational contrasts highlight a spectrum of preferences, from cautious adoption to embracing technological sophistication. BEV manufacturers must tailor strategies to address older generations’ concerns while meeting younger cohorts’ innovation demands, promoting inclusivity in the transition to sustainable transportation.