Technology Opportunity Identification Based on RFM Model and Stochastic Actor-oriented Model
Zhang Zhengang1,2,3, Luo Taiye1
1.School of Business Administration, South China University of Technology, Guangzhou 510640 2.Guangzhou Digital Innovation Research Center, Guangzhou 510640 3.Science and Technology Revolution and Technology Forecasting Think Tank of Guangdong Province, Guangzhou 510640
1 李牧南. 技术预见研究热点的演进分析: 内容挖掘视角[J]. 科研管理, 2018, 39(3): 141-153. 2 Kim G, Bae J. A novel approach to forecast promising technology through patent analysis[J]. Technological Forecasting and Social Change, 2017, 117: 228-237. 3 Yayavaram S, Ahuja G. Decomposability in knowledge structures and its impact on the usefulness of inventions and knowledge-base malleability[J]. Administrative Science Quarterly, 2008, 53(2): 333-362. 4 Guan J C, Liu N. Exploitative and exploratory innovations in knowledge network and collaboration network: a patent analysis in the technological field of nano-energy[J]. Research Policy, 2016, 45(1): 97-112. 5 Grant R M. Toward a knowledge-based theory of the firm[J]. Strategic Management Journal, 1996, 17(S2): 109-122. 6 Carnabuci G, Operti E. Where do firms' recombinant capabilities come from? Intraorganizational networks, knowledge, and firms’ ability to innovate through technological recombination[J]. Strategic Management Journal, 2013, 34(13): 1591-1613. 7 Lee F. Recombinant uncertainty in technological search[J]. Management Science, 2001, 47(1): 117-132. 8 Arthur W B. The structure of invention[J]. Research Policy, 2007,36(2): 274-287. 9 Athreye S, Keeble D. Technological convergence, globalisation and ownership in the UK computer industry[J]. Technovation, 2000, 20(5): 227-245. 10 Park I, Yoon B. Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network[J]. Journal of Informetrics, 2018,12(4): 1199-1222. 11 Han E J, Sohn S Y. Technological convergence in standards for information and communication technologies[J]. Technological Forecasting and Social Change, 2016, 106: 1-10. 12 Noh H, Song Y K, Lee S. Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations[J]. Telecommunications Policy, 2016, 40(10/11): 956-970. 13 Choi S, Jun S. Vacant technology forecasting using new Bayesian patent clustering[J]. Technology Analysis & Strategic Management, 2014, 26(3): 241-251. 14 Son C, Suh Y, Jeon J, et al. Development of a GTM-based patent map for identifying patent vacuums[J]. Expert Systems with Applications, 2012, 39(3): 2489-2500. 15 Rotolo D, Hicks D, Martin B R. What is an emerging technology?[J]. Research Policy, 2015, 44(10): 1827-1843. 16 Joung J, Kim K. Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data[J]. Technological Forecasting and Social Change, 2017, 114: 281-292. 17 Moehrle M G, Caferoglu H. Technological speciation as a source for emerging technologies: Using semantic patent analysis for the case of camera technology[J]. Technological Forecasting and Social Change, 2019, 146: 776-784. 18 Cheng C H, Chen Y S. Classifying the segmentation of customer value via RFM model and RS theory[J]. Expert Systems with Applications, 2009, 36(3): 4176-4184. 19 Yan C, Sun H T, Liu W, et al. An integrated method based on hesitant fuzzy theory and RFM model to insurance customers’ segmentation and lifetime value determination[J]. Journal of Intelligent & Fuzzy Systems, 2018, 35(1): 159-169. 20 Seymen O. Detecting the churn and non-churner customers using AHP based RFM Segmentation[C]// Proceedings of the Multidisciplinary Academic Conference on Management, Marketing and Economics. Prague, 2016: 273-281. 21 马宝龙, 李飞, 王高, 等. 随机RFM模型及其在零售顾客价值识别中的应用[J]. 管理工程学报, 2011, 25(1): 102-108. 22 Snijders T A B, van de Bunt G G, Steglich C E G. Introduction to stochastic actor-based models for network dynamics[J]. Social Networks, 2010, 32(1): 44-60. 23 Cao D P, Li H, Wang G B, et al. Dynamics of project-based collaborative networks for BIM implementation: analysis based on stochastic actor-oriented models[J]. Journal of Management in Engineering, 2017, 33(3): 04016055. 24 吴江, 李姗姗, 周露莎, 等. 基于随机行动者模型的在线医疗社区用户关系网络动态演化研究[J]. 情报学报, 2017, 36(2): 213-220. 25 Finger K, Lux T. Network formation in the interbank money market: an application of the actor-oriented model[J]. Social Networks, 2017, 48: 237-249. 26 Mohrenberg S. Studying policy diffusion with stochastic actor-oriented models[M]// Networked Governance. Cham: Springer, 2017: 163-188. 27 Kavaler D, Filkov V. Stochastic actor-oriented modeling for studying homophily and social influence in OSS projects[J]. Empirical Software Engineering, 2017, 22(1): 407-435. 28 张振刚, 黄洁明, 陈一华. 基于专利计量的人工智能技术前沿识别及趋势分析[J]. 科技管理研究, 2018, 38(5):36-42. 29 Brennecke J, Rank O. The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study[J]. Research Policy, 2017, 46(4): 768-783. 30 Park Y, Yoon J. Application technology opportunity discovery from technology portfolios: use of patent classification and collaborative filtering[J]. Technological Forecasting and Social Change, 2017, 118: 170-183. 31 Wang C L, Rodan S, Fruin M, et al. Knowledge networks, collaboration networks, and exploratory innovation[J]. Academy of Management Journal, 2014, 57(2): 484-514. 32 Cho Y, Kim M. Entropy and gravity concepts as new methodological indexes to investigate technological convergence: patent network-based approach[J]. PLoS One, 2014, 9(6): e98009.