Attractiveness of value-based versus technology-based software-defined vehicle concepts to consumers and employees

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Type of the article: Research Article

Abstract
Software-defined vehicles (SDVs) are gaining importance as a key innovation in the automotive industry, yet their value from the perspectives of consumers and employees remains insufficiently understood. This study aims to examine whether value-based SDV concepts increase perceived attractiveness compared with technology-based concepts. Randomized survey experiment was conducted through an online survey in Japan from July 23 to July 25, 2025, with 600 respondents aged 20-69, including car owners who drove at least once a month and full-time employees in the automobile, home appliance, and IT industries, selected as relevant evaluators of SDV value in market and workplace contexts. The value-based concept achieved a higher mean attractiveness score than the technology-based concept (3.375 vs. 3.132, t(598) = 3.11, p = 0.002) and a higher Top-2-Box attractiveness rate (47.7% vs. 36.5%, χ²(1) = 7.73, p = 0.005). Simple slope analyses yielded patterns that were directionally consistent with expectations, but the interaction effects of age and personal innovativeness were not statistically significant. These findings suggest that, in this Japanese online survey sample and under the specific stimuli used in this study, value-based SDV concept emphasizing rapid feedback incorporation and idea realization was rated as more attractive than a technology-based concept.

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    • Figure 1. Mean attractiveness ratings of SDV concepts for all respondents
    • Figure 2. Mean attractiveness ratings of SDV concepts by respondent type
    • Figure B1. Technology-based concept stimulus (presented to both consumer and employee groups)
    • Figure B2. Value-based concept stimulus for consumers
    • Figure B3. Value-based concept stimulus for employees
    • Table 1. Respondent characteristics by experimental group (%)
    • Table 2. Descriptive statistics of attractiveness by experimental group
    • Table 3. Regression results for perceived attractiveness (OLS)
    • Table A1. Survey items and sources
    • Conceptualization
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Data curation
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Formal Analysis
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Funding acquisition
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Investigation
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Methodology
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Project administration
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Resources
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Software
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Supervision
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Validation
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Visualization
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Writing – original draft
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda
    • Writing – review & editing
      Takashi Miyata, Takumi Kato, Kazuhiko Tsuda