# Exploring Multiple Pathways of Product Design Elements Using the fsQCA Method
## 2 Literature Review
### Research Motivation  
It is clear from Table 1 that although many studies have explored the influence of shape, color, material, and other design elements on users' perceptual imagery, there are still notable gaps in understanding the interaction between these elements:  
Insufficient systematic analysis of the combined effect of multiple elements. Many studies focus solely on the influence of individual design elements and lack a systematic analysis of how multiple elements interact [5; 23]. This single-dimensional approach cannot fully capture the complex interactions between design elements, nor can it capture the comprehensive impact of these combined elements on user perception. This makes it challenging for designers to consider the synergistic effects of various elements during the design process, limiting both the innovation of design solutions and overall user satisfaction. Lack of nonlinear analysis of the combination effects of modeling elements. Traditional linear analysis methods fall short in capturing the nonlinear interactions between design elements, leading to inaccurate predictions of users' perceptual responses. These \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Article** & **Research Focus** & **Method Used** & **Key Contribution** \\ \hline [PERSON] et al. (2021) [11] & Fusion of rough set theory and customer satisfaction surveys & Rough set theory, customer surveys & Nonlinear relationship analysis between design and sentiment \\ [PERSON] et al. (2015) [12] & Morphological analysis of shaving razors’ impact on psychology & Morphological analysis & Styling features’ psychological impact \\ [PERSON] et al. (2020) [13] & Perceptual engineering & Perceptual engineering, mathematical modeling & Quantitative mapping between product shape and perception \\ [PERSON] (2020) [14] & Combining computational tools with perceptual engineering & Computational modeling, perceptual engineering & Support for generating designs based on user perception \\ [PERSON] (2023) [15] & Application of gray correlation and fuzzy QFD & Gray correlation analysis, fuzzy QFD & Quantification of user feedback applied to design parameters \\ [PERSON] et al. (2020) & & Impact of design on sensory experience using neural networks & Perceptual engineering, neural networks & Machine learning insights into sensory response to design \\ [PERSON] et al. (2023) [18] & Wearable technology adoption using SEM and fsQCA & SEM, fsQCA & Key technology combination factors for safety \\ [PERSON] et al. (2022) [19] & fsQCA analysis of tourism development factors & fsQCA & Combination paths for tourism economic development \\ [PERSON] (2019) [20] & fsQCA on mobile game user experience factors & fsQCA & Combinations of factors affecting mobile game \\  & fsQCA on digital transformation in automotive industry & fsQCA & Digital transformation impact on business performance \\ [PERSON] et al. (2019) [22] & fsQCA on teacup design and tea sensory experience & fsQCA & Styling impact on tea taste and aroma \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related studies in product styling and multivariate path analysis. methods often assume simple, independent relationships between design elements, which is insufficient for addressing complex design problems. Consequently, there is often a significant gap between the predicted outcomes and the actual user experiences when these methods are applied to product design. In this context, fuzzy set qualitative comparative analysis (fsQCA) offers a valuable approach to bridge these research gaps. fsQCA can handle complex causal relationships and group effects among variables, providing new insights into how different combinations of design elements affect users' perceptual imagery [24]. This study applies fsQCA to product design, specifically analyzing how different shape configurations influence consumers' perceptual evaluations.