A data-driven morphological analysis: A novel approach to identifying new innovative ideas using WordNet/Wikipedia Reinforcement

Morphological analysis has been considered as a prominent tool for generating new and creative ideas. However, it has mostly been relied on experts’ judgment, which has a risk of subjective and biased idea generation. Despite some previous work on integrating data into the morphological matrix, the synergistic effects of using multiple databases have been overlooked due to the reliance on a single data source. In response, this study proposes a morphological analysis using five data sources, each with different characteristics. The new concepts of WordNet Reinforcement and Wikipedia Reinforcement are developed for morphology building. We also suggest a detailed process for data-driven morphological analysis, with a proper customization framework. The proposed data-driven morphological analysis can help managers accelerate creative idea generation in practice.