Correlation research on early-stage Design Data Search Strategies and Design Quality

Abstract

Data searching is an essential process in design, and research within the design field indicates a distinction in how expert designers and novice designers approach design problems. Expert designers, possessing a wealth of specialized knowledge, tend to search for additional information less frequently during the design process. In contrast, novice designers, due to their lack of professional knowledge, often need to search for, redefine, or organize information. (Ho, 2001; Cross et al., 1994). Consequently, novice designers tend to spend a significant amount of time repeatedly searching for information, leading not only to time wastage but also to designs that lack comprehensive consideration. Therefore, this study posits that the data collection process at the onset of design significantly affects the quality of design outcomes, necessitating further exploration of the relationship between various search strategies and design quality.The methodology involves think-aloud protocols, with the selection of coding systems based on search strategies divided into 0 to 3 dimensions as proposed by Gero and McNeill (1998) within the "problem domain" abstraction. Dimension 0 defines the system from the product's usage and user needs; 1 pertains to the product's interaction, styling, and imagery; 2 involves subsystems from product specifications, functions, behaviors, principles, and pain points; 3 considers details, integrating product principles with design elements and local details for effective optimization within the design concept. Additionally, regarding the quality of design outcomes, this study draws from the basic criteria for product design concepts proposed by Li, Feng-Qiang et al. (2016), encompassing ten aspects: functionality (F), usability (U), aesthetics and form (A), innovation (I), sustainability (ST), possibility (P), safety and regulation (SA), and marketability (M). These ten design criteria serve as a standard for assessing the quality of product design. Through the "problem domain" and "ten design criteria," this study aims to explore the differences in data search processes between novice and expert designers and the resulting impact on design quality.The findings reveal that novice designers mainly focus on the shallow, basic search dimensions of 1 and 2, lacking in-depth understanding of product details, which often leads to insufficient information for design execution. Experts iterate across dimensions 0 to 3, gaining a comprehensive understanding of the product, which better supports the subsequent design process. In terms of design criteria, novices focus on aesthetics/form (A) and innovation (I), with functionality also considered to some extent, while other aspects appear to be unconsciously neglected. Moreover, interviews and data organization revealed that novices tend to search from their personal experiences, whereas experts start from user needs, leading to more effective and accurate searches. Hence, in design education, teaching students to conduct comprehensive data collection could significantly enhance their design quality.