TY - JOUR
T1 - Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern
AU - Wang, Xuefeng
AU - Wang, Zhinan
AU - Huang, Ying
AU - Liu, Yuqin
AU - Zhang, Jiao
AU - Heng, Xiaofan
AU - Zhu, Donghua
N1 - Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2017/11/26
Y1 - 2017/11/26
N2 - 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.
AB - 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.
KW - Partner identification
KW - Subject-Action-Object semantic analysis
KW - correlation mapping
KW - dye-sensitized solar cells (DSSCs)
KW - problem & solution pattern
KW - term clumping
UR - http://www.scopus.com/inward/record.url?scp=85010651553&partnerID=8YFLogxK
U2 - 10.1080/09537325.2016.1277202
DO - 10.1080/09537325.2016.1277202
M3 - Article
AN - SCOPUS:85010651553
SN - 0953-7325
VL - 29
SP - 1167
EP - 1180
JO - Technology Analysis and Strategic Management
JF - Technology Analysis and Strategic Management
IS - 10
ER -