The Effect of Design Thinking on Creative & Innovation Processes: An Empirical Study Across Different Design Experience Levels

TL;DR

This empirical study investigates how various design thinking skills influence creativity and innovation, highlighting significant pathways and differences across experience levels to inform educational and professional practices.

Contribution

It provides a comprehensive empirical analysis of the impact of design thinking skills on creativity and innovation, including structural stability across experience levels.

Findings

All four design thinking skills positively influence design thinking.

Design thinking significantly predicts creativity and innovation.

Differences exist between students and professionals in the model.

Abstract

This study employs linear regression and structural equation modeling to explore how Thinking Skills, Design Thinking, Creative Self-Efficacy (CSE), and Collective Creative Efficacy (CCE) drive Design Creativity & Innovation, and analyzes the structural stability of the model across different levels of experience. Path analysis results indicate that the four Design Thinking Skills, Problem-driven Design (beta = 0.198, p < 0.01), Information-driven Design (beta = 0.241, p < 0.001), Solution-driven Design (beta = 0.227, p < 0.001), and Knowledge-driven Design (beta = 0.263, p < 0.001) all significantly and positively influence Design Thinking. Furthermore, Design Thinking has a significant positive predictive effect on Design Creativity & Innovation (beta = 0.286, p < 0.001). Mediation analysis confirms three significant mediation paths: the CSE mediation path (beta = 0.128, p < 0.001), the CCE mediation path (beta = 0.073, p < 0.01), and the "CSE to CCE" chain mediation path (beta = 0.025, p < 0.01). Multi-group comparison results reveal significant differences between the student and professional groups under the full equivalence model. After relaxing specific constraints, there were no significant differences between the nested models of the baseline model, partial measurement invariance, structural weight invariance, and structural covariance invariance. These findings elucidate the multi-dimensional pathways of Design Creativity & Innovation, providing a robust empirical basis for optimizing differentiated pedagogical models and professional practice guidelines.