# Exploring Multiple Pathways of Product Design Elements Using the fsQCA Method
## 2 Literature Review
### Multivariate Path Analysis  
Linear regression analysis is commonly used to predict user imagery based on the assumption of a linear relationship between product styling and user imagery, with styling features as the independent variable and user imagery as the dependent variable. The model built on this assumption can predict user imagery to a certain extent and is one of the common methods for constructing quantitative relationship models [17]. However, the relationship between product design and user perceptual imagery is often nonlinear and complex. While the linear assumption has value, it may be limited in prediction accuracy. Complex associations exist between styling elements and user perceptual imagery. For example, a certain shape may evoke positive perceptual imagery in a specific combination of colors and materials, while the effect may differ in another combination. This multi-factor interaction suggests that product styling should adopt a more integrated and dynamic perspective to consider the effects of different factor combinations.  
In multivariate pathways research, fuzzy set qualitative comparative analysis (fsQCA) effectively addresses the aforementioned problems. It deals with complex grouping relationships among variables and allows for simultaneous consideration of the combinatorial effects of multiple conditions. fsQCA has been widely used in management to explore the complex causality in organizational behaviors and strategic decision-making. [PERSON] et al. [18] investigated the adoption intention of wearable technology in building safety through structural equation modeling (SEM) and fsQCA. They found that various technology combination factors are key rather than a single predictor, demonstrating the applicability of fsQCA in complex causal problems. [PERSON] et al. [19] used fsQCA to analyze the effects of transportation connectivity, socioeconomic status, tourism resources, and hotel reception capacity on regional tourism economic development in 153 cities in China. They showed that multiple-factor combination paths can promote economic development, verifying the necessity of diversified condition combinations. [PERSON] et al. [20] revealed how factors such as price value, game content quality, and user emotions affect download intention through different combinations in a study of mobile game user experience, demonstrating the potential application of fsQCA in user behavior analysis. [PERSON] et al. [21] applied fsQCA to analyze the impact of digital transformation on future business performance and participant satisfaction in the automotive industry, successfully applying fsQCA to a complex case study. In summary, fsQCA can reveal complex patterns of associations between multiple variables, helping designers identify and adapt key design elements to meet users' perceptual needs.  
Although fsQCA methods have been relatively well established in many fields, their exploration in product design is still in its infancy. However, they share common research values because product design also involves a complex, multi-dimensional decision space in which multiple design elements interact with each other in a nonlinear manner to jointly influence consumer perception. Just as fsQCA can reveal the synergistic effects of factors in business strategies, it also has the potential to analyze, in depth, the complex interactions between design elements that are critical for enhancing the perceptual evaluation of a product by its users.  
Taking the empirical study of teacups as an example, [PERSON] et al. [22] used fsQCA to analyze questionnaire data from tea ceremony experts and designers. They explored the impact of the visual design of teacups on the taste and aroma of tea and demonstrated that this method can provide designers with a more accurate and clearer styling basis.