Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern

• Published in: Technology analysis & strategic management Vol. 29; no. 10; pp. 1167 - 1180
• Main Authors: Wang, Xuefeng, Wang, Zhinan, Huang, Ying, Liu, Yuqin, Zhang, Jiao, Heng, Xiaofan, Zhu, Donghua
• Format: Journal Article
• Language: English
• Published: Abingdon Routledge 26.11.2017; Taylor & Francis Ltd
• Subjects: correlation mapping; Data; Dye-sensitized solar cells; dye-sensitized solar cells (DSSCs); Mapping; Partner identification; Photovoltaic cells; problem & solution pattern; R&D; Research & development; Semantics; Subject-Action-Object semantic analysis; Technology; term clumping
• ISSN: 0953-7325; 1465-3990
• DOI: 10.1080/09537325.2016.1277202

Today's companies still rely heavily on expert knowledge rather than quantitative data with a systematic approach to effectively identify and choose Research and Development (R&D) partners. It is advantageous to identify and select potential R&D partners using a Problem & Solution (P&S) pattern. This paper presents a novel process for identifying R&D partners on the basis of solution similarities that assist technology managers in understanding the relationships between research targets. First, we choose a thematic dataset that contains problems and quantitative data with relative topic terms. Then, we extract Subject-Action-Object semantic structures in a P&S pattern from the dataset, and identify various solutions to a technical problem, with each as a subject. In addition, we provide correlation mapping to visualise the text characters and identify R&D partners. Finally, we validate the proposed method through a case study of the dye-sensitized solar cells sector.