Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality
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DOI: 10.1007/s11192-023-04652-2
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- Liao, Shu-Chun & Chou, Tzu-Chuan & Huang, Chen-Hao, 2022. "Revisiting the development trajectory of the digital divide: A main path analysis approach," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
- Hiran H. Lathabai & Thara Prabhakaran & Manoj Changat, 2017. "Contextual productivity assessment of authors and journals: a network scientometric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 711-737, February.
- Yang, Chao & Huang, Cui & Su, Jun, 2018. "An improved SAO network-based method for technology trend analysis: A case study of graphene," Journal of Informetrics, Elsevier, vol. 12(1), pages 271-286.
- Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
- Bruno Miranda Henrique & Vinicius Amorim Sobreiro & Herbert Kimura, 2018. "Building direct citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 817-832, May.
- Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Xu, Haiyun & Yang, Guancan, 2022. "A semantic main path analysis method to identify multiple developmental trajectories," Journal of Informetrics, Elsevier, vol. 16(2).
- Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.
- Sang-Bing Tsai & Jie Zhou & Yang Gao & Jiangtao Wang & Guodong Li & Yuxiang Zheng & Peng Ren & Wei Xu, 2017. "Combining FMEA with DEMATEL models to solve production process problems," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-15, August.
- Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
- Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
- Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
- Graham Dixon & P. Sol Hart & Christopher Clarke & Nicole H. O’Donnell & Jay Hmielowski, 2020. "What drives support for self-driving car technology in the United States?," Journal of Risk Research, Taylor & Francis Journals, vol. 23(3), pages 275-287, March.
- Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.
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- Jang, Hyejin & Lee, Suyeong & Yoon, Byungun, 2023. "Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model," Technovation, Elsevier, vol. 126(C).
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Keywords
Main path analysis; Subject-action-object (SAO); Causality; Link weight; DEMATEL;All these keywords.
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