A computational framework to discover the tellability of car styles

Abstract The tellability of a car’s style—its capacity to communicate emotions, brand identity, country of origin, and car segment—remains underexplored in user-centered automotive design. This study aims to analyze the tellability of car styles by examining relationships between cars and these factors. A computational framework was employed to collect data from users and a selected set of cars and to quantitatively map perceptual relationships using block-seriation analysis. Three robust perceptual patterns—Stable, Friendly, and Powerful—and their associated Core Emotional Themes were identified: Calm, Grounded Dependability (Stable); Warm, Approachable Comfort (Friendly); and High-Energy Performance and Powerful Presence (Powerful). The research further highlights that brand recognition depends not only on distinctiveness but also on the consistency of design cues, with recognition emerging from visually coherent and differentiated brand elements. Moreover, shared design trends and platform similarities can blur brand boundaries, whereas minor similarities generate perceptual uncertainty across brands. At the country-of-origin level, perceptions exist on a continuum from clear to ambiguous, also reflecting globalization’s influence on stylistic identities. Car segments are often perceived as fuzzy rather than discrete, and recognition strength does not always align with segment clarity. By capturing the complexity of observer perception, these findings advance understanding of how users interpret and evaluate automotive designs, providing actionable insights for design, brand management, and strategic development. Graphical Abstract