A review of data analytics in technological forecasting

Elsevier BV - Tập 166 - Trang 120646 - 2021
Changyong Lee1
1Graduate School of Management of Technology, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul, 04107, Republic of Korea

Tài liệu tham khảo

Abbas, 2014, A literature review on the state-of-the-art in patent analysis, World Pat. Inf., 37, 3, 10.1016/j.wpi.2013.12.006 Aharonson, 2016, Mapping the technological landscape: measuring technology distance, technological footprints, and technology evolution, Res. Policy, 45, 81, 10.1016/j.respol.2015.08.001 Albert, 2015, Technology maturity assessment based on blog analysis, Technol. Forecast. Soc., 92, 196, 10.1016/j.techfore.2014.08.011 Altuntas, 2015, Analysis of patent documents with weighted association rules, Technol. Forecast. Soc., 92, 249, 10.1016/j.techfore.2014.09.012 Anderson, 2008, Technology forecasting for wireless communication, Technovation, 28, 602, 10.1016/j.technovation.2007.12.005 Aristodemou, 2018, The state-of-the-art on intellectual property analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data, World Pat. Inf., 55, 37, 10.1016/j.wpi.2018.07.002 Bengisu, 2006, Forecasting emerging technologies with the aid of science and technology databases, Technol. Forecast. Soc., 73, 835, 10.1016/j.techfore.2005.09.001 Bergmann, 2008, Evaluating the risk of patent infringement by means of semantic patent analysis: the case of DNA chips, R&D Manage, 38, 550, 10.1111/j.1467-9310.2008.00533.x Bessen, 2008, The value of US patents by owner and patent characteristics, Res. Policy, 37, 932, 10.1016/j.respol.2008.02.005 Blei, 2003, Latent dirichlet allocation, J. Mach. Learn. Res., 3, 993 Chen, 2012, Business intelligence and analytics: from big data to big impact, MIS Q., 36, 1165, 10.2307/41703503 Chen, 2015, The impact of customer experience and perceived value on sustainable social relationship in blogs: an empirical study, Technol. Forecast. Soc., 96, 40, 10.1016/j.techfore.2014.11.011 Choi, 2009, Monitoring the organic structure of technology based on the patent development paths, Technol. Forecast. Soc., 76, 754, 10.1016/j.techfore.2008.10.007 Choi, 2013, An SAO-based text-mining approach for technology roadmapping using patent information, R&D Manag., 43, 52, 10.1111/j.1467-9310.2012.00702.x Coates, 2001, On the future of technological forecasting, Technol. Forecast. Soc., 67, 1, 10.1016/S0040-1625(00)00122-0 Coccia, 2019, The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting, Technol. Forecast. Soc., 141, 289, 10.1016/j.techfore.2018.12.012 Coccia, 2020, Deep learning technology for improving cancer care in society: new directions in cancer imaging driven by artificial intelligence, Technol. Soc., 60, 10.1016/j.techsoc.2019.101198 Coccia, 2020, A theory of the evolution of technology: technological parasitism and the implications for innovation management, J. Eng. Technol. Manag., 55, 10.1016/j.jengtecman.2019.11.003 Cunningham, 2009, Analysis for radical design, Technol. Forecast. Soc., 76, 1138, 10.1016/j.techfore.2009.07.014 Daim, 2016 Daim, 2006, Forecasting emerging technologies: use of bibliometrics and patent analysis, Technol. Forecast. Soc., 73, 981, 10.1016/j.techfore.2006.04.004 Ernst, 2003, Patent information for strategic technology management, World Pat. Inf., 25, 233, 10.1016/S0172-2190(03)00077-2 Fayyad, 1996, From data mining to knowledge discovery in databases, AI Mag., 17, 37 Fleming, 2001, Technology as a complex adaptive system: evidence from patent data, Res. Policy, 30, 1019, 10.1016/S0048-7333(00)00135-9 Furukawa, 2015, Identifying the evolutionary process of emerging technologies: a chronological network analysis of World Wide Web conference sessions, Technol. Forecast. Soc., 91, 280, 10.1016/j.techfore.2014.03.013 Gao, 2013, Technology life cycle analysis method based on patent documents, Technol. Forecast. Soc., 80, 398, 10.1016/j.techfore.2012.10.003 Geissinger, 2020, Digital disruption beyond uber and airbnb—tracking the long tail of the sharing economy, Technol. Forecast. Soc., 155, 10.1016/j.techfore.2018.06.012 Gerken, 2012, A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis, Scientometrics, 91, 645, 10.1007/s11192-012-0635-7 Geum, 2016, How to generate creative ideas for innovation: a hybrid approach of WordNet and morphological analysis, Technol. Forecast. Soc., 111, 176, 10.1016/j.techfore.2016.06.026 Giles, 2006, Internet encyclopaedias go head to head, Nature, 438, 900, 10.1038/438900a Guo, 2016, Subject–action–object-based morphology analysis for determining the direction of technological change, Technol. Forecast. Soc., 105, 27, 10.1016/j.techfore.2016.01.028 Hacklin, 2013, Strategic choices in converging industries, MIT Sloan Manag. Rev., 55, 65 Harhoff, 1999, Citation frequency and the value of patented inventions, Rev. Econ. Stat., 81, 511, 10.1162/003465399558265 Hoisl, 2015, Forecasting technological discontinuities in the ICT industry, Res. Policy, 44, 522, 10.1016/j.respol.2014.10.004 Iqbal, 2020, Big data analytics: computational intelligence techniques and application areas, Technol. Forecast. Soc., 153, 10.1016/j.techfore.2018.03.024 Jeong, 2015, Development of patent roadmap based on technology roadmap by analyzing patterns of patent development, Technovation, 39, 37, 10.1016/j.technovation.2014.03.001 Kayser, 2017, Extending the knowledge base of foresight: the contribution of text mining, Technol. Forecast. Soc., 116, 208, 10.1016/j.techfore.2016.10.017 Kim, 2017, Concentric diversification based on technological capabilities: link analysis of products and technologies, Technol. Forecast. Soc., 118, 246, 10.1016/j.techfore.2017.02.025 Kim, 2016, Futuristic data-driven scenario building: incorporating tet mining and fuzzy association rule mining into fuzzy cognitive map, Expert Syst. Appl., 57, 311, 10.1016/j.eswa.2016.03.043 Kim, 2019, Anticipating technological convergence: link prediction using wikipedia hyperlinks, Technovation, 79, 25, 10.1016/j.technovation.2018.06.008 Kim, 2016, A visual scanning of potential disruptive signals for technology roadmapping: investigating keyword cluster, intensity, and relationship in futuristic data, Technol. Anal. Strateg., 28, 1225, 10.1080/09537325.2016.1193593 Kim, 2015, Dynamic patterns of industry convergence: evidence from a large amount of unstructured data, Res. Policy, 44, 1734, 10.1016/j.respol.2015.02.001 Kim, 2008, Visualization of patent analysis for emerging technology, Expert Syst. Appl., 34, 1804, 10.1016/j.eswa.2007.01.033 Kogan, 2017, Technological innovation, resource allocation, and growth, Q. J. Econ., 132, 665, 10.1093/qje/qjw040 2012 Kwon, 2017, Applying LSA text mining technique in envisioning social impacts of emerging technologies: the case of drone technology, Technovation, 60, 15, 10.1016/j.technovation.2017.01.001 Kwon, 2018, Proactive development of emerging technology in a socially responsible manner: data-driven problem solving process using latent semantic analysis, J. Eng. Technol. Manag., 50, 45, 10.1016/j.jengtecman.2018.10.001 Kyebambe, 2017, Forecasting emerging technologies: a supervised learning approach through patent analysis, Technol. Forecast. Soc., 125, 236, 10.1016/j.techfore.2017.08.002 Lee, 2012, A stochastic patent citation analysis approach to assessing future technological impacts, Technol. Forecast. Soc., 79, 16, 10.1016/j.techfore.2011.06.009 Lee, 2020, Navigating a product landscape analysis for technology opportunity analysis: a word2vec approach using an integrated patent-product database, Technovation, 96–97 Lee, 2015, Novelty-focused patent mapping for technology opportunity analysis, Technol. Forecast. Soc., 90, 355, 10.1016/j.techfore.2014.05.010 Lee, 2016, Stochastic technology life cycle analysis using multiple patent indicators, Technol. Forecast. Soc., 106, 53, 10.1016/j.techfore.2016.01.024 Lee, 2018, Early identification of emerging technologies: a machine learning approach using multiple patent indicators, Technol. Forecast. Soc., 127, 291, 10.1016/j.techfore.2017.10.002 Lee, 2013, How to assess patent infringement risks: a semantic patent claim analysis using dependency relationships, Technol. Anal. Strateg., 25, 23, 10.1080/09537325.2012.748893 Lee, 2015, An instrument for scenario-based technology roadmapping: how to assess the impacts of future changes on organisational plans, Technol. Forecast. Soc., 90, 285, 10.1016/j.techfore.2013.12.020 Lee, 2014, Pre-launch new product demand forecasting using the Bass model: a statistical and machine learning-based approach, Technol. Forecast. Soc., 86, 49, 10.1016/j.techfore.2013.08.020 Lee, 2009, An approach to discovering new technology opportunities: keyword-based patent map approach, Technovation, 29, 481, 10.1016/j.technovation.2008.10.006 Lee, 2015, Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents, Technol. Forecast. Soc., 100, 317, 10.1016/j.techfore.2015.07.022 Li, 2019, Forecasting technology trends using text mining of the gaps between science and technology: the case of perovskite solar cell technology, Technol. Forecast. Soc., 146, 432, 10.1016/j.techfore.2019.01.012 Li, 2019, Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: the case of perovskite solar cell technology, Technol. Forecast. Soc., 146, 687, 10.1016/j.techfore.2018.06.004 Lim, 2009, Identification of technological knowledge intermediaries, Scientometrics, 84, 543, 10.1007/s11192-009-0133-8 Linton, 2007, MOT TIM journal rankings 2006, Technovation, 27, 91, 10.1016/j.technovation.2006.12.001 Martino, 2003, A review of selected recent advances in technological forecasting, Technol. Forecast. Soc., 70, 719, 10.1016/S0040-1625(02)00375-X Mendonça, 2004, Trademarks as an indicator of innovation and industrial change, Res. Policy, 33, 1385, 10.1016/j.respol.2004.09.005 Moehrle, 2019, Technological speciation as a source for emerging technologies. Using semantic patent analysis for the case of camera technology, Technol. Forecast. Soc., 146, 776, 10.1016/j.techfore.2018.07.049 Nagy, 2011, Superexponential long-term trends in information technology, Technol. Forecast. Soc., 78, 1356, 10.1016/j.techfore.2011.07.006 Nallaperuma, 2019, Online incremental machine learning platform for big data-driven smart traffic management, IEEE Trans. Intell. Transp., 20, 4679, 10.1109/TITS.2019.2924883 Porter, 1995, Technology opportunity analysis, Technol. Forecast. Soc., 49, 237, 10.1016/0040-1625(95)00022-3 Reitzig, 2004, Improving patent valuations for management purposes—validating new indicators by analyzing application rationales, Res. Policy, 33, 939, 10.1016/j.respol.2004.02.004 Rendón, 2011, Internal versus external cluster validation indexes, Int. J. Comput. Commun., 5, 27 Rodriguez, 2016, Patent clustering and outlier ranking methodologies for attributed patent citation networks for technology opportunity discovery, IEEE Trans. Eng. Manag., 63, 426, 10.1109/TEM.2016.2580619 Roopa, 2019, Data driven approach for farm re-modeling using prediction analytics, 209 Roper, 2011 Sanders, 2003, The efficacy of using judgmental versus quantitative forecasting methods in practice, Omega Int. J. Manag. Sci., 31, 511, 10.1016/j.omega.2003.08.007 Seo, 2016, Product opportunity identification based on internal capabilities using text mining and association rule mining, Technol. Forecast. Soc., 105, 94, 10.1016/j.techfore.2016.01.011 Shi, 2014, Diffusion of multi-generational high-technology products, Technovation, 34, 162, 10.1016/j.technovation.2013.11.008 Shibata, 2008, Detecting emerging research fronts based on topological measures in citation networks of scientific publications, Technovation, 28, 758, 10.1016/j.technovation.2008.03.009 Shin, 2013, Robust future-oriented technology portfolios: black–Litterman approach, R&D Manag., 43, 409, 10.1111/radm.12022 Silverman, 2006 Son, 2012, Development of a GTM-based patent map for identifying patent vacuums, Expert Syst. Appl., 39, 2489, 10.1016/j.eswa.2011.08.101 Strumsky, 2015, Identifying the sources of technological novelty in the process of invention, Res. Policy, 44, 1445, 10.1016/j.respol.2015.05.008 2004, Technology futures analysis: toward integration of the field and new methods, Technol. Forecast. Soc., 71, 287, 10.1016/j.techfore.2003.11.004 Teoh, 2019, From technical analysis to text analytics: stock and index prediction with GRU, 496 Tran, 2008, A taxonomic review of methods and tools applied in technology assessment, Technol. Forecast. Soc., 75, 1396, 10.1016/j.techfore.2008.04.004 Tranfield, 2003, Towards a methodology for developing evidence-informed management knowledge by means of systematic review, Brit. J. Manag., 14, 207, 10.1111/1467-8551.00375 Tsai, 2015, Big data analytics: a survey, J. Big Data, 2, 2, 10.1186/s40537-015-0030-3 Wang, 2006, Forecasting innovation performance via neural networks—a case of Taiwanese manufacturing industry, Technovation, 26, 635, 10.1016/j.technovation.2004.11.001 Watts, 1997, Innovation forecasting, Technol. Forecast. Soc., 56, 25, 10.1016/S0040-1625(97)00050-4 Woo, 2019, Screening early stage ideas in technology development processes: a text mining and k-nearest neighbours approach using patent information, Technol. Anal. Strateg., 31, 532, 10.1080/09537325.2018.1523386 Yoon, 2005, A systematic approach for identifying technology opportunities: keyword-based morphology analysis, Technol. Forecast. Soc., 72, 145, 10.1016/j.techfore.2004.08.011 Yoon, 2008, Morphology analysis for technology roadmapping: application of text mining, R&D Manag., 38, 51, 10.1111/j.1467-9310.2007.00493.x Yoon, 2002, On the development and application of a self–organizing feature map–based patent map, R&D Manag., 32, 291, 10.1111/1467-9310.00261 Yoon, 2011, Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks, Scientometrics, 88, 213, 10.1007/s11192-011-0383-0 Yoon, 2012, Detecting signals of new technological opportunities using semantic patent analysis and outlier detection, Scientometrics, 90, 445, 10.1007/s11192-011-0543-2 Zhang, 2019, Discovering and forecasting interactions in big data research: a learning-enhanced bibliometric study, Technol. Forecast. Soc., 146, 795, 10.1016/j.techfore.2018.06.007 Zhang, 2014, Term clumping” for technical intelligence: a case study on dye-sensitized solar cells, Technol. Forecast. Soc., 85, 26, 10.1016/j.techfore.2013.12.019 Zhang, 2016, Topic analysis and forecasting for science, technology and innovation: methodology with a case study focusing on big data research, Technol. Forecast. Soc., 105, 179, 10.1016/j.techfore.2016.01.015