The purpose of this study was to evaluate the wearing comfort of wireless earphones. In order to test the effects of ear dimensions with the product characteristics on wearing comfort, first, a total of six ear dimensions were selected. In addition, each ear dimension was classified into two groups using K-means clustering analysis. Second, four major factors related to wearing comfort (pain, pressure, comfort, and fixation) with four sample products were investigated, considering the characteristics of the product (kernel-type/open-type), operation method (touch-input/button-press), and shape. Finally, a total of 38 participants (23 men and 15 women) participated in the experiments. The results show that pain in the ear canal was low to the participants who had a long length of the ear canal–incisura intertragica, and the fixation of the ear canal was high in the participants with a long incisura intertragica–antitragus cluster. Compared to open-type products, kernel-type products put more pressure on the ear canal area, so the participants felt less comfortable. In terms of operation methods, operating products in touch-input types caused less pain than operating products in button-press types did. Overall, the factors affecting user satisfaction and wearing comfort determined in this study will serve as a basis for the future design of wireless earphone products.

When developing a user-oriented product, it is crucial to consider users’ affective needs. Various semantic differential (SD) methods have been used to identify affect regarding materials, and this is the most important property in products. This study aims to determine which of the three conventional SD methods (absolute evaluation 1 [AE 1], absolute evaluation 2 [AE 2], or relative evaluation [RE]) is most effective for affective evaluation. Affective evaluation was performed for vehicle instrument panels by each of these three SD methods. Two quantitative analysis methods (correlation analysis and repeated-measures ANOVA) were used to examine the performance (sample distinguishability) of each evaluation method, and it was found that both AE 2 and RE produced better results than AE 1. The correlation coefficients and p-values in correlation analysis were slightly better for RE than for AE 2. In conclusion, an affective evaluation produced better results when pairwise samples (especially one sample pair) were presented, indicating that maintaining distinct samples is very important. The clearer the difference in comparison targets is, the more accurate the evaluation results.