Attention-Weighted Kansei Engineering: Ming-Style Armchair

Abstract

Ming-style armchairs represent a distinctive form of Chinese classical furniture, yet systematic methods for linking their morphological features to users’ affective responses remain limited. Conventional Kansei Engineering (KE) models treat design components as equally weighted, neglecting differences in visual attention. This study proposes an attention-weighted KE framework that integrates eye-tracking data with partial least squares (PLS) regression modelling. Five representative Ming-style armchairs were evaluated by 200 Generation Y participants across five Kansei dimensions using semantic differential scales. A subsequent eye-tracking experiment with 30 participants derived visual attention weights for nine morphological components via the coefficient of variation of total fixation duration. These weights were embedded into binary morphological coding and analysed using partial least squares (PLS) regression. Results indicated that the stretcher, apron, and spindle showed stronger associations with affective differentiation than the dominant backrest, which served as a stable perceptual anchor. Plain aprons and slightly curved spindles enhanced perceptions of simplicity and naturalness, while step-by-step stretchers and carved aprons were associated with complexity. The regression coefficients were translated into preference-avoidance design rules, suggesting that redesign may benefit from a two-fold strategy of preserving simple, recognisable treatment of the dominant category-defining component while concentrating tasteful elaboration on one selected secondary feature.