This research revolutionizes automotive styling education by integrating exploratory factor analysis, student-drawn car sketches, and Kansei engineering vocabulary. By fostering design creativity and analyzing emotions elicited by designs, it empowers students with practical data skills. The research methodology is meticulously designed to encompass three key stages: First, 58 students are instructed to create car sketches from a unified perspective, resulting in a diverse collection of 546 unique works. Following this, a panel of 74 participants is convened to select the 100 most popular sketches and conduct a Kansei engineering vocabulary survey, exploring the emotions elicited by each design. Lastly, an online questionnaire utilizing seven distinct Kansei engineering vocabulary – "excessive," "advanced," "innovative," "modern," "classic," "retro," and "outdated" – is administered to evaluate each work on a Likert seven-point scale. This rigorous process yields 113 valid responses, providing a solid foundation of data for uncovering the pivotal factors that influence automotive styling evaluation.
An in-depth examination of the Kansei engineering vocabulary has identified two core elements: "Super Creative" and "Traditional," with a KMO value of 0.764, suggesting an adequate level of sampling adequacy. These primary components exhibit strong explanatory power, accounting for 78.184% of the total variance 4.135 (59.07%) and 1.338 (19.114%), respectively, providing a concise and effective framework for evaluating automotive styling. This breakthrough simplifies the evaluation process while enhancing its precision, leading to more efficient and accurate assessments.

Authors:
Chu Cheng Ma, National Cheng Kung University, Taiwan
Meng-Dar Shieh, National Cheng Kung University, Taiwan

About the Presenter(s)
CHU CHENG MA is currently a doctoral candidate at National Cheng Kung University in Taiwan.

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