Unraveling the nature of design thinking disposition: Contributions of trait cognitive flexibility and trait empathy on design thinking potential
Bibliographic record
Abstract
Design thinking, a human-centered and creative problem-solving approach, has garnered significant attention across various disciplines. However, its ambiguous conceptual nature and lack of a robust theoretical framework have been points of criticism. This study seeks to address the research question: What are the key psychological traits that contribute to an individual’s design thinking disposition? To explore this, a cross-sectional survey was conducted with 904 young adults (aged 18–35) from diverse ethnic backgrounds. The survey measured trait cognitive flexibility, trait cognitive empathy (perspective-taking), trait affective empathy (empathic concern), and design thinking disposition, alongside personality traits (e.g., openness to experience), demographics, and academic performance. Results indicate that trait cognitive flexibility is strongly associated with design thinking disposition, and this relationship is mediated by cognitive empathy (perspective-taking), but not by affective empathy (empathic concern). These effects persist even when controlling for personal attributes such as age, education level, and openness to experience. The findings highlight the pivotal role of cognitive flexibility and underscore the importance of cognitive empathy over affective empathy in fostering design thinking. This study contributes to a deeper understanding of the psychological foundations of design thinking and offers insights for developing evidence-based strategies to cultivate this important disposition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".