Tech Emergence Bibliography

Technological Forecasting & Social Change: Special Section on Forecasting Technical Emergence.

This special section of 16 papers focuses on research contributing to development of measures (indicators) of “tech emergence.” It draws on papers presented at the 2017 Portland International Conference on Management of Engineering and Technology (PICMET) in Portland, Oregon, USA.

Zhang, Y., Porter, A., Chiavetta, D., Newman, N.C., and Guo, Y. (2019), Forecasting technical emergence: An introduction, Technological Forecasting and Social Change, 146, 626-627 https://www.sciencedirect.com/science/article/pii/S0040162518320444?via%3Dihub

An introduction to this Special Section

Huang, Y., Porter, A.L., Zhang, Y., Lian, X., and Guo, Y. (2019), An Assessment of Technology Forecasting: Revisiting Earlier Analyses on Dye-Sensitized Solar Cells (DSSCs), Technological Forecasting and Social Change, 146: 831-843; https://doi.org/10.1016/j.techfore.2018.10.031

A prior forecast is validated with a revisit of the results of a 10-year dye sensitized solar cell-based forecasting study conducted by the Innovation Co-Lab (Science, Technology and Innovation Program, Georgia Institute of Technology, Manchester Institute of Innovation Research, University of Manchester, and the School of Management and Economics, Beijing Institute of Technology)

Ma, J., Abrams, N., Porter, A.L., Zhu, D., and 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.

A series of indicators are designed to profile research activities and forecast potential trends for clinical translation of scientific discoveries from “bench to bedside” 

Porter, A.L., Garner, J., Carley, S.F., and Newman, N.C. (2018), Emergence scoring to identify frontier R&D topics and key players, Technological Forecasting and Social Change, 146: 628-643; https://doi.org/10.1016/j.techfore.2018.04.016

A method to calculate emergence scores for topical terms derived from scientific documents then applied to a target R&D domain to identify cutting-edge countries, organizations, and individuals

Robinson, D.K.R., Lagnau, A., and Boon, W.P.C. (2019), Innovation pathways in additive manufacturing: Methods for tracing emerging and branching paths from rapid prototyping to alternative applications, Technological Forecasting and Social Change, 146: 733-750;
https://doi.org/10.1016/j.techfore.2018.07.012

An addition to the Forecasting innovation pathways (FIP) toolbox that helps characterize and demarcate boundaries of emerging fields, allowing for deeper analysis

Wang, Z., Porter, A.L., Wang, X., and Carley, S. (2019 online), An approach to identify emergent topics of technological convergence: A case study for 3D printing, Technological Forecasting & Social Change, 146: 723-732.
https://doi.org/10.1016/j.techfore.2018.12.015

Integration of technological convergence with the analytic framework of emerging scores for extracting emergent topics

Zhang, Y., Huang, Y., Porter, A.L., Zhang, G., and Lu, J. (2019), Discovering and forecasting interactions in big data research: A learning-enhanced bibliometric study, Technological Forecasting and Social Change, 146: 795-807;
DOI: 10.1016/j.techfore.2018.06.007
A method called scientific evolutionary pathways profiles technological landscapes and identifies the evolutionary relationships among technological topics in the area of big data techniques

Zhou, X., Huang, L., Porter, A., & Vicente-Gomila, J. M. (2019). Tracing the system transformations and innovation pathways of an emerging technology: solid lipid nanoparticles. Technological Forecasting & Social Change, 146: 785-794; https://doi.org/10.1016/j.techfore.2018.04.026
A series of text mining techniques (e.g., term clumping, subject-action-object analysis, and network analysis) are integrated within the FIP framework to identify key R&D areas and trace technological evolution over time

Tech Emergence Bibliography

The purpose of sharing the bibliography below is to facilitate literature review and broaden our consideration of related concepts, methods, and case analyses.

We encourage all to correct and enrich this compilation.  To do so, please make changes and additions via “COMMENTS.”  We will consolidate such changes and periodically update this resource.

Adner, Ron, and Daniel A. Levinthal. 2002. “The Emergence of Emerging Technolo­gies.” California Management Review 45(1): 50–66.  https://doi.org/10.2307/41166153

Alexander, J.,  Chase, J., Newman, N.C., Porter, A.L., and Roessner, D. (2012).  Emergence as a Conceptual Framework for Understanding Scientific and Technological Progress, PICMET (Portland International Conference on Management of Engineering and Technology), Vancouver, 2012.

Allan, J. (2002). Topic detection and tracking: Event-based information organization. US: Springer.

An, X. Lin, C. Yu, X. Zhang, Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes, Scientometrics 105 (3) (2015) 1605-1634.

Antons, D., Joshi, A.M., Saige, T.O. (2018).  Content, contribution, and knowledge consumption: Uncovering hidden topic structure and rhetorical signals in scientific texts,
https://doi.org/10.1177/0149206318774619

Avila-Robinson, A., & Miyazaki, K. (2013). Dynamics of scientific knowledge bases as proxies for discerning technological emergence – the case of mems/nems technologies. Technological Forecasting and Social Change, 80(6), 1071-1084. doi:10.1016/j.techfore.2012.07.012

Bettencourt, L., Kaiser, D., Kaur, J., Castillo-Chavez, C., & Wojick, D. (2008). Population modeling of the emergence and development of scientific fields. Scientometrics, 75(3), 495–518.

Bildosola, I., Gonzalez, P., Moral, P. (2017). An approach for modelling and forecasting research activity related to an emerging technology. Scientometrics. 112(1) 557–72.

Björk,B.-C.(2005). A life cycle model of the scientific communication process. Learn.Publ. 18, 165–176. doi:10.1087/0953151054636129

Blei,D.M.,Ng,A.Y.,andJordan,M.I.(2003).LatentDirichletallocation. J.Mach. Learn.Res. 3, 993–1022.

Boon, E. Moors, Exploring emerging technologies using metaphors: A study of orphan drugs and pharmacogenomics, Social Science & Medicine, 66 (9), 1915–1927, 2008.

Bornmann, L., & Daniel, H. D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of documentation, 64(1), 45-80.

Bornmann,L., and Mutz,R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references.  J.Assoc.Inf. Sci.Technol. 66, 2215–2222. doi:10.1002/asi.23329

Boyack, K.W. & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society of Information Science and Technology 61(12), 2389–404.

Boyack, K. W., Newman, D., Duhon, R. J., Klavans, R., Patek, M., Biberstine, J. R., . . . Börner, K. (2011). Clustering more than two million biomedical publications: Comparing the accuracies of nine text-based similarity approaches. PLoS One, 6(3), e18029.

Burmaoglu, S., Porter, A.L., & Souminen, A. (2018),  What is technology emergence? A micro level definition for improving tech mining practice, Portland International Conference on Management of Engineering and Technology (PICMET), Honolulu.

Burmaoglu, S., Sartanaer, O., Porter, A., and Li, M. (under review), Analyzing theoretical roots of technological emergence with an evolutionary perspective,

Burmaoglu, S., Sartanaer, O., Porter, A. (2019). Conceptual definition of technology emergence: A long journey from philosophy of science to science policy. Technology in Society, Vol 59.  FULL-TEXT https://doi.org/10.1016/j.techsoc.2019.04.002

Carbonell, J., Sanchez-Esguevillas, A., & Carro, B. (2018). Easing the assessment of emerging technologies in technology observatories. Findings about patterns of dissemination of emerging technologies on the internet (vol 30, pg 113, 2017). Technology Analysis & Strategic Management, 30(1), Iii-Iii. doi:10.1080/09537325.2017.1344005

Carley, Stephen F., Newman, Nils C., Porter, Alan L., and Garner, Jon G. (2017). A measure of staying power: Is the persistence of emergent concepts more significantly influenced by technical domain or scale?  Scientometrics, 111 (3) 2077-2087; doi:10.1007/s11192-017-2342-x.

Carley, S.F., Newman, N.C., Porter, A.L., and Garner, J. (2018).  An indicator of technical emergence, Scientometrics, 115 (1), 35-49; http://link.springer.com/article/10.1007/s11192-018-2654-5.

P-L. Chang, C-C. Wu, H-J. Leu, Using patent analyses to monitor the technological trends in an emerging field of technology: A case of carbon nanotube field emission display, Scientometrics, 82 (1) (2010) 5–19; doi:10.1007/s11192-009-0033-y.

Chen, C. (2004). Searching for intellectual turning points: progressive knowledge domain visualization. Proc. Natl. Acad. Sci. U.S.A. 101, 5303–5310. doi:10.1073/ pnas.0307513100

Chen, C. (2006). Citespace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.

Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191–209.

Chen, K.-Y., Luesukprasert, L., & Seng-cho, T. C. (2007). Hot topic extraction based on timeline analysis and multidimensional sentence modeling. IEEE Transactions on Knowledge and Data Engineering, 19(8).

Choi, Y. Park, Monitoring the organic structure of technology based on the patent development paths, Technological Forecasting and Social Change, 76 (6) (2009) 754–768; doi:10.1016/j.techfore.2008.10.007.

Committee on Defense Intelligence Agency Technology Forecasts and Reviews, Avoiding Surprise in an Era of Global Technology Advances, National Research Council, National Academies Press, Washington, DC, 2005.

Corning, P. A. (2002). The re-emergence of “emergence”: A venerable concept in search of a theory. Complexity, 7(6), 18-30. doi:10.1002/cplx.10043

ozzens, S., Gatchair, S., Kang, J., Kim, K., Lee, H.H., Ordonez, G., and Porter, A.L. (2010).  Emerging technologies: quantitative identification and measurement, Technology Analysis and Strategic Management  22 (3): 361–376.

Crutchfield, J. P. (2013). Is anything ever new? Considering emergence. In M. A. Bedau & P. Humphreys (Eds.), Emergence: Contemporary readings in philosophy and science. MIT Press Scholarship Online: The MIT Press.

Danneels, Erwin. 2004. “Disruptive Technology Reconsidered: A Critique and Re­search Agenda.” Journal of Product Innovation Management 21(4): 246–58.  https://doi.org/10.1111/j.0737-6782.2004.00076.x

T.U. Daim, T.U., Rueda, G., Martin, H., and Gerdsri, P. (2006). Forecasting emerging technologies: use of bibliometrics and patent analysis, Technological Forecasting and Social Change 73:981–1012

de Haan, How emergence arises. Ecological Complexity 3 (4) (2006) 293–301

Ding, W., & Chen, C. (2014). Dynamic topic detection and tracking: A comparison of HDP, C-word, and cocitation methods. Journal of the Association for Information Science and Technology, 65(10), 2084-2097.

Einsiedel, Edna. 2009. “Making Sense of Emerging Technologies.” In Emerging Technologies: from Hindsight to Foresight, ed. Edna Einsiedel, 3–11. Vancouver: UBC Press.

Érdi, P., Makovi, K., Somogyvári, Z., Strandburg, K., Tobochnik, J., Volf, P., et al. (2013). Prediction of emerging technologies based on analysis of the US patent citation network. Scientometrics 95, 225–242. doi:10.1007/s11192-012-0796-4

Förster M., Stelzer, B., and Schiebel, E. (2018). Stochastic analysis of citation time series of emergent research topics, 23^rd International Conference on Science and Technology Indicators (STI), Leiden, The Netherlands, Sep. 12-14, Paper #211.

Foster, J., & Metcalfe, J. S. (2012). Economic emergence: An evolutionary economic perspective. Journal of Economic Behavior & Organization, 82(2-3), 420-432. doi:10.1016/j.jebo.2011.09.008

Funk, R. J., & Owen-Smith, J. (2016). A dynamic network measure of technological change. Management Science, 63(3), 791-817. doi: 10.1287/mnsc.2015.2366

Garechana, G., Rio-Belver, R., Bildosola, I., and Cilleruelo-Carrasco, E. (2018), Using take-off phase data for forecasting the evolution of emergent technologies, 23^rd International Conferene on Science and Technology Indicators (STI2018), Leiden, The Netherlands.

Garfield, E., & Small, H. (1989). Identifying the change frontiers of science. In M. Kranzberg, Y. Elkana, & Z. Tadmor (Eds.), Conference proceedings of innovation: At the crossroads between science and technology (pp. 51–65). Haifa, Israel: The S. Neaman Press.

Garner, J., Carley, S., and Porter, A.L. and Newman, N.C. (2017). Technological emergence indicators using emergence scoring, 2017 Portland International Conference on Management of Engineering and Technology (PICMET), Portland.

Geroski, P. . (2000). Models of technology diffusion. Research Policy, 29(4–5), 603–625. https://doi.org/10.1016/S0048-7333(99)00092-X

Girvan, M.E.J. Newman, Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99 (12) (2002) 7821–7826.

Glänzel, W., and Thijs, B. (2012). Using hybrid methods and ‘core documents’ for detecting and labelling new emerging topics. Scientometrics 91 (2), 399–416. doi:10.1007/s11192-011- 0591-7

Goffman, W. (1966). Mathematical approach to the spread of scientific ideas: The history of mast cell research. Nature, 212(5061), 452–499.

Goffman, W. (1971). A mathematical method for analyzing the growth of a scientific discipline. Journal of Association for Computing Machinery, 18(2), 173–185.

Goffman, W., & Harmon, G. (1971). Mathematical approach to the prediction of scientific discovery. Nature, 229(5280), 103–104.

Goffman, W., & Newill, V. A. (1964). Generalization of epidemic theory: An application to the transmission of ideas. Nature, 204(4955), 225–228.

Goldstein, Emergence as a construct: History and issues. Emergence 1 (1) (1999) 49-72.

Glänzel, W. (2012) Bibliometric Methods for Detecting and Analysing Emerging Research Topics. Profesional de la Informacion, 21(2), 194–201. doi:10.3145/epi.2012.mar.11, EPI SCP

Goldspink, C., & Kay, R. (2010). Emergence in organizations: The reflexive turn. Emergence : Complexity and Organization, 12(3), 47-63.

Goldstein, J. (2003). Emergence, creativity, and the following and negating. The Innovation Journal: The Public Sector Innovation Journal, 10(3), 1-12.

Goldstein, J. (2004). Emergence, creative process, and self-transcending concstructions. In M. Lissack & K. Richardson (Eds.), Managing organizational complexity philosophy, theory and application (Vol. Managing the Complex). Greenwich, Conn.: Information Age Pub.

Guo, H., Weingart, S., and Borner, K. (2011).  Mixed-indicators model for identifying emerging research areas, Scientometrics 89: 421-435.

Guo, Y., Xu, C., Huang, L., and Porter, A.L. (2012), Empirically informing a technology delivery system model for an emerging technology:  Illustrated for dye-sensitized solar cells, R&D Management, 42 (2), 133-149.

Guo, Y., Zou, X., Porter, A.L., and Robinson, D.K.R. (2015), Tech Mining to Generate Indicators of Future National Technological Competitiveness:  Nano-enhanced Drug Delivery (NEDD) in the US and China, Technological Forecasting and Social Change 97, 168-180;  http://dx.doi.org/10.1016/j.techfore.2014.02.026.

Gustafsson, R., Kuusi, O., & Meyer, M. (2015). Examining open-endedness of expectations in emerging technological fields: The case of cellulosic ethanol. Technological Forecasting and Social Change, 91, 179-193. doi:10.1016/j.techfore.2014.02.008

Harper, D. A., & Endres, A. M. (2012). The anatomy of emergence, with a focus upon capital formation. Journal of Economic Behavior & Organization, 82(2), 352-367. doi:https://doi.org/10.1016/j.jebo.2011.03.013

He, J., and Chen, C. (2018), Predictive effects of novelty measured by temporal embeddings on the growth of scientific literature, Frontiers in Research Metrics and Analytics;  doi: 10.3389/frma.2018.00009

He, Q., Chen, B., Pei, J., Qiu, B., Mitra, P., and Giles, L. (2009). “Detecting topic evolution in scientific literature: how can citations help?” in Proceedings of   the 18th ACM Conference on Information and Knowledge Management (ACM), Hong Kong, 957–966.

M.M. Hopkins, J. Siepel,  Just how difficult can it be counting up R&D funding for emerging technologies (and is tech mining with proxy measures going to be any better)? Technology Analysis and Strategic Management, 25 (6) (2013) 655–685;  http://dx.doi.org/10.1080/09537325.2013.801950

Hodgson, G. M. (2002). Darwinism in economics: From analogy to ontology. Journal of Evolutionary Economics, 12(3), 259-281. doi:DOI 10.1007/s00191-002-0118-8

Holmes, C., and Ferrill, M. (2005).  The application of operation and technology roadmapping to aid Singaporean SMEs identify and select emerging technologies, Technological Forecasting and Social Change 72 (3): 349–357.

Huang, Y., Porter, A.L., Cunningham, S.W., Robinson, D.K.R., Liu, J., & Zhu, D. (2017). A Technology Delivery System model for characterizing the supply side of technology emergence: Illustrated for Big Data & Analytics, Technological Forecasting and Social Change. 130 (5): 165-176. doi: 10.1016/j.techfore.2017.09.012.

Huang, J. Schuehle, A.L. Porter, J. Youtie, A systematic method to create search strategies for emerging technologies based on the web of science: illustrated for Big Data, Scientometrics, 105 (3) (2015) 1-18; doi: 10.1007/s11192-015-1638-y.

Huang, Y., Zhu, D., Qian, Y., Zhang, Y., Porter, A.L., Liu, Y. (2017). A hybrid method to trace technology evolution pathways: a case study of 3D printing. Scientometrics, 111(1), 185–204.

Jaric, I., Knezevic-Jaric, J., & Lenhardt, M. (2014). Relative age of references as a tool to identify emerging research fields with an application to the field of ecology and environmental sciences. Scientometrics, 100(2), 519-529. doi:10.1007/s11192-014-1268-9

Joung, J., & Kim, K. (2017). Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data. Technological Forecasting and Social Change, 114, 281-292. doi:10.1016/j.techfore.2016.08.020

Kajikawa, Y., Yoshikawaa, J., Takedaa, Y., & Matsushima, K. (2008). Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy. Technological Forecasting and Social Change, 75(6), 771–782.

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Kajikawa, Y., Yoshikawaa, J., Takedaa, Y., & Matsushima, K. (2008). Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy. Technological Forecasting and Social Change, 75(6), 771–782.

Klavans, R., & Boyack, K. W. (2017). Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge? Journal of the Association for Information Science and Technology, 68(4), 984-998.

Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397.

Klincewicz, K. (2016). The emergent dynamics of a technological research topic: The case of graphene. Scientometrics, 106(1), 319-345. doi:10.1007/s11192-015-1780-6. (1998). Emergence: From Chaos to Order. Reading, MA: Perseus.

Kontostathis, L.M. Galitsky, W.M. Pottenger, W. M., S. Roy, D.J. Phelps, A survey of emerging trend detection in textual data mining. Survey of Text Mining, 185-224 (2004).

Korzinov, V., and Savin, I. (2018). General Purpose Technologies as an emergent property, Technological Forecasting and Social Change 129: 88-104; doi.org/10.1016/j.techfore.2017.12.011.

Kucharavy, D., & De Guio, R. (2015). Application of Logistic Growth Curve. Procedia Engineering, 131, 280–290. https://doi.org/10.1016/J.PROENG.2015.12.390

Kwon, H., Kim, J., & Park, Y. (2017). Applying lsa text mining technique in envisioning social impacts of emerging technologies: The case of drone technology. Technovation, 60-61, 15-28. doi:10.1016/j.technovation.2017.01.001

Kwon, S., Porter, A.L., and Youtie, J. (2016), Navigating the innovation trajectories of technology by combining specialization score analyses for publications and patents– Graphene and Nano-Enabled Drug Delivery; Scientometrics 106 (3), 1057-1071.  http://link.springer.com/article/10.1007%2Fs11192-015-1826-9#page-1.

Lahoti, G., Porter, A.L., Zhang, C., Youtie, J., and Wang, B. (2018 to appear). Tech mining to validate and refine a technology roadmap, World Patent Information,

Lee, C., Kang, B., and Shin, J. (2015), Novelty-focused patent mapping for technology opportunity analysis, Technological Forecasting and Social Change: 90 (B), 355-365.

Lee, C., Kwon, O., Kim, M., & Kwon, D. (2018). Early identification of emerging technologies: A machine learning approach using multiple patent indicators. Technological Forecasting and Social Change, 127, 291-303. doi:10.1016/j.techfore.2017.10.002

Lee, S. Lee, S., Seol, H., and Park, Y. (2008).  Using patent information for designing new product and technology: keyword based technology roadmapping, Research Management 38 (2): 169–188.

Lee, W. H. (2008). How to identify emerging research fields using scientometrics: An example in the field of information security. Scientometrics, 76(3), 1588–2861.

Leydesdorff, L., & Schank, T. (2008). Dynamic animations of journal maps: Indicators of structural changes and interdisciplinary developments. Journal of the American Society for Information Science and Technology, 59(11), 1810–1818.

Li, M., and Porter, A.L. (online), Facilitating the discovery of relevant studies on risk analysis for three-dimensional printing based on an integrated framework, Scientometrics; DOI 10.1007/s11192-017-2570-0.

Li, M., Porter, A.L., and Suominen, A. (2018).  Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective, Technological Forecasting and Social Change 129: 285-296; doi.org/10.1016/j.techfore.2017.09.032.

Li, M., Porter, A.L., and Wang, Z.L. (2017), Evolutionary trend analysis of nanogenerator research based on a novel perspective of phased bibliographic coupling, Nano Energy 34: 93-102.

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Ma, J., Abrams, N., Porter, A.L., Zhu, D., and Farrell, D. (to appear).  Identifying translational indicators and technology opportunities for nanomedical research using tech mining:  The case of gold nanostructures, Technological Forecasting and Social Change,

Ma, J., Porter, A.L., Aminabhavi, T., and Zhu, D. (2015), Nano-enabled drug delivery systems for brain cancer and Alzheimer’s Disease: Research patterns and opportunities, Nanomedicine: Nanotechnology, Biology and Medicine 11 (7), 1763-1771; DOI: 10.1016/j.nano.2015.06.006.

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Peng, H., Xudong, F., and Suominen, A. (2017), Explore the dynamics of emerging technologies through research networks — the case study of Triboelectric Nanogenerator, Portland International Conference on Management of Engineering and Technology (PICMET), Portland, OR (July 11).

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Porter, A.L., Cunningham, S.W., and Sanz, A., (2015), Advancing the Forecasting Innovation Pathways Approach: Hybrid & Electric Vehicles Case,  International Journal of Technology Management 69 (3-4), 275-300. DOI: 10.1504/IJTM.2015.072975.

Porter, A.L., Garner, J., Carley, S.F., and Newman, N.C. (2018), Emergence scoring to identify frontier R&D topics and key players, Technological Forecasting and Social Change,  https://doi.org/10.1016/j.techfore.2018.04.016

Porter, A.L., Garner, J., Newman, N.C., Carley, S.F., Youtie, J., Kwon, S., and Li, Y. (to appear), National nanotechnology research prominence, Technology Analysis and Strategic Management

Porter, A. L., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81(3), 719–745.

Porter, A., Youtie, J., Carley, S., Newman, N., and Murdick, D. (2018).  Contest: Measuring tech emergence, 23^rd International Conference on Science and Technology Indicators (STI), Leiden, The Netherlands, Sep. 12-14, Paper #232.

Qi, Y., Zhu, N., Zhai, Y., and Ding, Y. (2018). The mutually beneficial relationship of patents and scientific literature: topic evolution in nanoscience, Scientometrics 115 (2): 893-911.

Ranaei, S., and Suominen, A. (2017), Using machine learning approaches to identify emergence: Case of vehicle related patent data, PICMET (Portland International Conference on Management of Engineering and Technology), Portland, OR (July).

Ranaei, A. Suominen, A. Porter, S. Carley, Identifying technological emergence using text mining and machine learning, Technological Forecasting and Social Change (in process).

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