Iterative Quantification of Categorical Criteria for Enhanced Job Seeking

In personal and unstructured multi-criteria decision making (MCDM) contexts such as job seeking, qualitative factors like work culture and team dynamics can be as important as quantitative factors like salary and commute distance. However, most MCDM visualization tools, such as LineUp and ValueCharts, focus on quantitative data, often overlooking qualitative criteria. This gap appears to stem from a lack of studies on real-world decision making tasks. To address this, we conducted in-depth interviews with job seekers, emphasizing the integration and prioritization of qualitative data. After investigating the role of qualitative data in decision making, we introduced and evaluated a tool that extends LineUp’s features to support qualitative criteria. Our insights underscore the vital role of qualitative data in job seeking, illustrating how visualization design can better accommodate the nuanced preferences inherent in these decision making processes.