Subject–action–object-based morphology analysis for determining the direction of technological change

• Published in: Technological forecasting & social change Vol. 105; pp. 27 - 40
• Main Authors: Guo, Junfang, Wang, Xuefeng, Li, Qianrui, Zhu, Donghua
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.04.2016; Elsevier Science Ltd
• Subjects: Configurations; Construction; Content analysis; Dye-sensitized solar cells (DSSCs); Dyes; Innovations; Keywords; Morphology; Morphology analysis; Performance indices; Photovoltaic cells; Semantic analysis; Semantics; Solar cells; Studies; Subject–action–object (SAO); Technological change; Technological forecasting; Technological planning; Technology; Technology change; Text mining; Vector analysis; Text mining; Morphology analysis; Subject–action–object (SAO); Dye-sensitized solar cells (DSSCs); Technology change
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2016.01.028

Morphology analysis, despite being a strong stimulus for the development of new alternatives, largely relies on domain experts and neglects the relationships between keywords in the construction of morphological structures. In addition, there are few systematic approaches to prioritize the morphological configurations. To address these issues, a hybrid approach is proposed, which enhances the performance of morphology analysis by combining it with subject–action–object (SAO) semantic analysis. Initially, a keyword co-occurrence patent set for subsequent SAO analysis is prepared based on keywords frequency vector analysis. Then, SAO structures are extracted and semantic analysis is performed to identify the relationships between keywords, which help to build morphological structures more objectively. In addition, a well-defined evaluation system that contains eight sub-indexes is proposed to evaluate the morphological configurations. Finally, to demonstrate and validate the proposed approach, the dye-sensitized solar cells technology is employed as the case study. Results indicate that the most promising combination we predict appears frequently in 2012–2014 and the distribution of it is also close to the fact in 2012–2014. Accordingly, the proposed method can be used to effectively determine the direction of technological change and to forecast technology innovation opportunities. [Display omitted] •We propose an improved morphology analysis method for technology forecasting.•Subject–Action–Object semantic analysis is used to construct morphological structure.•A systematic evaluation index system is built to evaluate morphology configurations.•The proposed method can dynamically and effectively forecast technology change.•Case study shows that the morphological distribution we predict is close to the fact