Abstract
As the application of big data in design practice becomes increasingly prevalent, large-scale text-based user analysis is emerging as a critical tool for problem definition. However, there is a lack of empirical discussion on how this approach affects designers’ cognitive and emotional understanding. While previous studies have focused on outcome-oriented uses, this study analyzed the impact of providing big data-driven user information during the early stages of the design process on designers’ way of understanding users. In a workshop on “Design for Gen Z” with professional designers, participants were divided into control and experimental groups; the former received qualitative user data, and the latter additionally received results of large-scale user text analysis based on frequency analysis and Latent Dirichlet Allocation (LDA) topic modeling to define a design problem. Empathy levels were measured using selected items from the EMPA-D (Designers’ Empathy) scale, and ease of problem definition was self-assessed on a 7-point Likert scale. The experimental group had a significantly lower empathy score (p = 0.028 < 0.05) on the Personal Experience (PE) dimension; however, in terms of ease of problem definition, their responses had lower variance, indicating stronger alignment among the designers. This suggests that big data is effective for designers’ cognitive clarification but may impose constraints on emotional resonance with users. This study contributes empirical evidence to the discourse on data-driven design and advocates for a balanced approach that integrates both large-scale analytical insights and narrative-driven user perspectives to support holistic understanding in the design process.
Keywords
Problem definition; Empathy; Big data; User understanding
DOI
https://doi.org/10.21606/iasdr.2025.924
Citation
Min, J.,and Lee, H.(2025) Exploring the Role of Big Data in Designers' Empathy and Problem Definition, in Chang, C.-Y., and Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taiwan. https://doi.org/10.21606/iasdr.2025.924
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Track 5 - Design Thinking
Exploring the Role of Big Data in Designers' Empathy and Problem Definition
As the application of big data in design practice becomes increasingly prevalent, large-scale text-based user analysis is emerging as a critical tool for problem definition. However, there is a lack of empirical discussion on how this approach affects designers’ cognitive and emotional understanding. While previous studies have focused on outcome-oriented uses, this study analyzed the impact of providing big data-driven user information during the early stages of the design process on designers’ way of understanding users. In a workshop on “Design for Gen Z” with professional designers, participants were divided into control and experimental groups; the former received qualitative user data, and the latter additionally received results of large-scale user text analysis based on frequency analysis and Latent Dirichlet Allocation (LDA) topic modeling to define a design problem. Empathy levels were measured using selected items from the EMPA-D (Designers’ Empathy) scale, and ease of problem definition was self-assessed on a 7-point Likert scale. The experimental group had a significantly lower empathy score (p = 0.028 < 0.05) on the Personal Experience (PE) dimension; however, in terms of ease of problem definition, their responses had lower variance, indicating stronger alignment among the designers. This suggests that big data is effective for designers’ cognitive clarification but may impose constraints on emotional resonance with users. This study contributes empirical evidence to the discourse on data-driven design and advocates for a balanced approach that integrates both large-scale analytical insights and narrative-driven user perspectives to support holistic understanding in the design process.