빅데이터 분석을 활용한 디자이너의 디자인 통찰 도출 과정 탐구 = Exploring Designers’ Process of Design Insight Generation Using Big Data Analysis

다국어 초록 (Multilingual Abstract)

Background: With technological advancement, massive text data has become a crucial resource for deriving design insights. However, there is still a need for practical processes that allow designers to effectively utilize data owned by companies and industries. As an initial step, it is essential to observe designers’ internal cognitive processes when applying text data analysis results to derive design insights. Methods: Accordingly, this study explores how big data analysis results activate designers’ cognitive strategies during design insight generation. A big data–based design insight workshop was conducted to observe how novice designers employ cognitive strategies—pattern recognition, key factor identification, comparison and contrast, and contextual analysis. A total of 6,809 YouTube comments on the topic of food insecurity were collected and analyzed using LDA (Latent Dirichlet Allocation) topic modeling, yielding seven major topics. The same analysis results were then provided to four groups comprising sixteen novice designers, and their processes of deriving design insights were visually analyzed. Results: The results showed that quantitative outputs, including frequency and importance, guided designers’ cognitive criteria and stimulated strategies such as comparison, contrast, and pattern recognition. Three patterns of cognitive strategy combination were identified: (1) multi-justification based on quantitative data, (2) reduction of cognitive bias based on qualitative data, and (3) activation of abductive reasoning through key factor identification. Conclusion: This study visually structured how cognitive strategies operate within the same data context and empirically demonstrated that designers can derive data-driven design insights. These findings provide foundational implications for developing design tools that support user-centered thinking.