Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease

Jing Ma1, Yaohui Pan2, Chih-Yi Su3
1College of Management, Shenzhen University, Shenzhen, China
2College of Economics and Management, China Jiliang University, Hangzhou, China
3College of Management, Guilin University of Electronic Technology, Guilin, China

Tóm tắt

This study aims to investigate how to test and assess the dichotomy of roles from an organization-oriented perspective for technology opportunity analysis, in context of the development of technological knowledge networks. We present a future oriented framework based on the link prediction methods. An empirical study of Alzheimer’s disease (AD) related patents was conducted to illustrate this framework. The results show that link prediction indices are feasible and effective for predicting emerging links. Organizations differ in their predictive ability as knowledge providers and being predicted as knowledge consumers. The framework and results in this study offer a new clue to understand innovation activities and broaden organizations’ technological frontiers.

Từ khóa


Tài liệu tham khảo

Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the Web. Social Networks, 25(3), 211–230. https://doi.org/10.1016/s0378-8733(03)00009-1 Antons, D., Grunwald, E., Cichy, P., & Salge, T. O. (2020). The application of text mining methods in innovation research: Current state, evolution patterns, and development priorities. R & D Management, 50(3), 329–351. https://doi.org/10.1111/radm.12408 Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. In International AAAI Conference on Weblogs and Social Media Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics-Theory and Experiment. https://doi.org/10.1088/1742-5468/2008/10/P10008 Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(1), 69–87. https://doi.org/10.1016/s0048-7333(02)00004-5 Fujita, K., Kajikawa, Y., Mori, J., & Sakata, I. (2014). Detecting research fronts using different types of weighted citation networks. Journal of Engineering and Technology Management, 32, 129–146. https://doi.org/10.1016/j.jengtecman.2013.07.002 Gao, L., Porter, A. L., Wang, J., Fang, S., Zhang, X., Ma, T., Wang, W., & Huang, L. (2013). Technology life cycle analysis method based on patent documents. Technological Forecasting and Social Change, 80(3), 398–407. https://doi.org/10.1016/j.techfore.2012.10.003 Gerken, J. M., & Moehrle, M. G. (2012). A new instrument for technology monitoring: Novelty in patents measured by semantic patent analysis. Scientometrics, 91(3), 645–670. https://doi.org/10.1007/s11192-012-0635-7 Guan, J., & Shi, Y. (2012). Transnational citation, technological diversity and small world in global nanotechnology patenting. Scientometrics, 93(3), 609–633. https://doi.org/10.1007/s11192-012-0706-9 Guns, R., & Rousseau, R. (2014). Recommending research collaborations using link prediction and random forest classifiers. Scientometrics, 101(2), 1461–1473. https://doi.org/10.1007/s11192-013-1228-9 Hanley, J. A., & Mcneil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (Roc) curve. Radiology, 143(1), 29–36. https://doi.org/10.1148/radiology.143.1.7063747 Hur, W. (2017). The patterns of knowledge spillovers across technology sectors evidenced in patent citation networks. Scientometrics, 111(2), 595–619. https://doi.org/10.1007/s11192-017-2329-7 Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., Itzkovitz, S., Colonna, M., Schwartz, M., & Amit, I. (2017). A unique microglia type associated with restricting development of Alzheimer’s disease. Cell, 169(7), 1276-1290 e1217. https://doi.org/10.1016/j.cell.2017.05.018 Kim, K., Park, K., & Lee, S. (2018). Investigating technology opportunities: The use of SAOx analysis. Scientometrics, 118(1), 45–70. https://doi.org/10.1007/s11192-018-2962-9 Lane, C. A., Hardy, J., & Schott, J. M. (2018). Alzheimer’s disease. European Journal of Neurology, 25(1), 59–70. https://doi.org/10.1111/ene.13439 Lee, C., & Lee, G. (2019). Technology opportunity analysis based on recombinant search: Patent landscape analysis for idea generation. Scientometrics, 121(2), 603–632. https://doi.org/10.1007/s11192-019-03224-7 Lee, J., Kim, C., & Shin, J. (2017). Technology opportunity discovery to R&D planning: Key technological performance analysis. Technological Forecasting and Social Change, 119, 53–63. https://doi.org/10.1016/j.techfore.2017.03.011 Lee, J. H., Ko, N., Yoon, J., & Son, C. H. (2021). An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks. Technological Forecasting and Social Change, 168, 120746. https://doi.org/10.1016/j.techfore.2021.120746 Lee, W. S., Han, E. J., & Sohn, S. Y. (2015). Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents. Technological Forecasting and Social Change, 100, 317–329. https://doi.org/10.1016/j.techfore.2015.07.022 Liu, H. B., & Song, C. (2016). An application of the patent co-citation visualization in the analysis of front and hotspot technologies in the field of shale gas. Energy and Mechanical Engineering, 635–641 Lu, L., Jin, C. H., & Zhou, T. (2009). Similarity index based on local paths for link prediction of complex networks. Physical Review e: Statistical, Nonlinear, and Soft Matter Physics, 80(4 Pt 2), 046122. https://doi.org/10.1103/PhysRevE.80.046122 Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica a: Statistical Mechanics and Its Applications, 390(6), 1150–1170. https://doi.org/10.1016/j.physa.2010.11.027 Luo, J. X., Yan, B. W., & Wood, K. (2017). InnoGPS for data-driven exploration of design opportunities and directions: The case of google driverless car project. Journal of Mechanical Design, 139(11), 111416. https://doi.org/10.1115/1.4037680 Ma, J., Abrams, N. F., Porter, A. L., Zhu, D., & Farrell, D. (2019). Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures. Technological Forecasting and Social Change, 146, 767–775. https://doi.org/10.1016/j.techfore.2018.08.002 Niemann, H., Moehrle, M. G., & Frischkom, J. (2017). Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application. Technological Forecasting and Social Change, 115, 210–220. https://doi.org/10.1016/j.techfore.2016.10.004 Oh, S., Choi, J., Ko, N., & Yoon, J. (2020). Predicting product development directions for new product planning using patent classification-based link prediction. Scientometrics, 125(3), 1833–1876. https://doi.org/10.1007/s11192-020-03709-w Park, I., & Yoon, B. (2018). Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network. Journal of Informetrics, 12(4), 1199–1222. https://doi.org/10.1016/j.joi.2018.09.007 Perez-Molina, E., & Loizides, F. (2021). Novel data structure and visualization tool for studying technology evolution based on patent information: The DTFootprint and the TechSpectrogram. World Patent Information, 64, 102009. Porter, A. L., & Detampel, M. J. (1995). Technology opportunities analysis. Technological Forecasting and Social Change, 49(3), 237–255. https://doi.org/10.1016/0040-1625(95)00022-3 Porter, A. L., Jin, X. Y., Gilmour, J. E., Cunningham, S., Xu, H. D., Stanard, C., & Wang, L. (1994). Technology opportunities analysis—Integrating technology monitoring, forecasting, and assessment with strategic-planning. Sra-Journal of the Society of Research Administrators, 26(2), 21–31. Ren, H. Y., & Zhao, Y. H. (2021). Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks. Technovation, 101, 19. https://doi.org/10.1016/j.technovation.2020.102196 Shibata, N., Kajikawa, Y., & Sakata, I. (2012). Link prediction in citation networks. Journal of the American Society for Information Science and Technology, 63(1), 78–85. https://doi.org/10.1002/asi.21664 Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78(2), 274–282. https://doi.org/10.1016/j.techfore.2010.07.006 Sung, H. Y., Yeh, H. Y., Lin, J. K., & Chen, S. H. (2017). A visualization tool of patent topic evolution using a growing cell structure neural network. Scientometrics, 111(3), 1267–1285. https://doi.org/10.1007/s11192-017-2361-7 Wang, X., Ma, P., Huang, Y., Guo, J., Zhu, D., Porter, A. L., & Wang, Z. (2017). Combining SAO semantic analysis and morphology analysis to identify technology opportunities. Scientometrics, 111(1), 3–24. https://doi.org/10.1007/s11192-017-2260-y Yoon, B., & Magee, C. L. (2018). Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction. Technological Forecasting and Social Change, 132, 105–117. https://doi.org/10.1016/j.techfore.2018.01.019 Yoon, B., Park, I., & Coh, B.-Y. (2014). Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining. Technological Forecasting and Social Change, 86, 287–303. https://doi.org/10.1016/j.techfore.2013.10.013 Zhang, Y., Porter, A. L., Hu, Z. Y., Guo, Y., & Newman, N. C. (2014). “Term clumping” for technical intelligence: A case study on dye-sensitized solar cells. Technological Forecasting and Social Change, 85, 26–39. https://doi.org/10.1016/j.techfore.2013.12.019 Zhang, Y., Zhou, X., Porter, A. L., & Gomila, J. M. V. (2014). How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: "Problem & solution’’ pattern based semantic TRIZ tool and case study. Scientometrics, 101(2), 1375–1389. https://doi.org/10.1007/s11192-014-1262-2 Zhao, J., Miao, L. L., Yang, J., Fang, H. Y., Zhang, Q. M., Nie, M., Holme, P., & Zhou, T. (2015). Prediction of links and weights in networks by reliable routes. Scientific Reports, 5, 12261. https://doi.org/10.1038/srep12261 Zhou, T., Lu, L. Y., & Zhang, Y. C. (2009). Predicting missing links via local information. European Physical Journal B, 71(4), 623–630. https://doi.org/10.1140/epjb/e2009-00335-8 Zhou, W., Gu, J. Y., & Jia, Y. F. (2018). h-Index-based link prediction methods in citation network. Scientometrics, 117(1), 381–390. https://doi.org/10.1007/s11192-018-2867-7