TY - JOUR
T1 - A systematic idea generation approach for developing a new technology
T2 - Application of a socio-technical transition system
AU - Lee, Keeeun
AU - Kim, Sunhye
AU - Yoon, Byungun
N1 - Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2022/3
Y1 - 2022/3
N2 - Socio-technical systems have been highlighted as a new driver of novel idea generation, providing greater insights for new product development (NPD) in different fields. This research implements a broad set of indicators that measure both the technological and social features for a panel of 50 coevolution cases, and uses a structural vector autoregressive (SVAR) model in order to investigate the long-run relationships and coevolution patterns between technology and society. Technology evolution patterns were analysed using text data from patents, and social evolution patterns were investigated using text data from different news outlets. These influential relationships between technical and societal variables adopted from the SVAR model are examined over the course of this study. In addition, this research identifies various coefficients derived from this influential relationship, and draws out evolutionary patterns to verify how they have influenced each other over time. The results indicate that socio-technical systems are generally driven by the coevolution of three main technological variables (tangibility, safety and diffusion) and five social variables (intellectualization, informatization, polarization, connectivity and serviceability). This research contributes towards improving the explanatory power of coevolution patterns between society and technology, and more strongly establishes a set of insightful strategies for technological development.
AB - Socio-technical systems have been highlighted as a new driver of novel idea generation, providing greater insights for new product development (NPD) in different fields. This research implements a broad set of indicators that measure both the technological and social features for a panel of 50 coevolution cases, and uses a structural vector autoregressive (SVAR) model in order to investigate the long-run relationships and coevolution patterns between technology and society. Technology evolution patterns were analysed using text data from patents, and social evolution patterns were investigated using text data from different news outlets. These influential relationships between technical and societal variables adopted from the SVAR model are examined over the course of this study. In addition, this research identifies various coefficients derived from this influential relationship, and draws out evolutionary patterns to verify how they have influenced each other over time. The results indicate that socio-technical systems are generally driven by the coevolution of three main technological variables (tangibility, safety and diffusion) and five social variables (intellectualization, informatization, polarization, connectivity and serviceability). This research contributes towards improving the explanatory power of coevolution patterns between society and technology, and more strongly establishes a set of insightful strategies for technological development.
KW - Coevolution
KW - Socio-technical system
KW - Structural vector autoregressive model
KW - Technology idea generation
UR - http://www.scopus.com/inward/record.url?scp=85121129530&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2021.121431
DO - 10.1016/j.techfore.2021.121431
M3 - Article
AN - SCOPUS:85121129530
SN - 0040-1625
VL - 176
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
M1 - 121431
ER -