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EVOLUTION OF INNOVATION IN INDUSTRY LIFE CYCLES: A COMPLEX NETWORK PERSPECTIVE
Published online by Cambridge University Press: 19 June 2023
Abstract
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Technological life cycles are driven by the changes in the shape and level of innovation, yet innovation rate is not directly observable and is difficult to trace. Innovation measured by patent-citation networks (PCN) cannot be estimated solely by quantity, whereas quality-adjusted quantity measurements are still prone to bias. Our paper complement PCN data analysis with an agent-based simulation (ABS) on networks to uncover the latent innovation that automatically accounts for the quality and quantity components instead of decoupling them. We build dynamic PCN for radio frequency 'CMOS' technology and subsequently develop ABS to replicate underlying innovation network formation. Comparing the real and synthetic data, we isolate latent innovation rate in PCN and by mapping pivotal patent assignees — innovators, we calculate the diversity of innovators in the technology market. Identifying innovation patterns, we show that, early on, innovation structures are less diverse and exploratory, but this grows and matures eventually until value creation become an expensive endeavor. Contrary to what is observed, we show how the abundance that appears are less significant publications mostly driven by exploitation in the technology market.
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.- Copyright
- The Author(s), 2023. Published by Cambridge University Press
References
Abidi, A.A. (1999), “Cmos wireless transceivers: The new wave”, IEEE Communications Magazine, Vol. 37 No. 8, pp. 119–124.CrossRefGoogle Scholar
Bekkers, R. and Martinelli, A. (2012), “Knowledge positions in high-tech markets: Trajectories, standards, strategies and true innovators”, Technological Forecasting and Social Change, Vol. 79 No. 7, pp. 1192–1216, http://doi.org/10.1016/jj.techfore.2012.01.009.CrossRefGoogle Scholar
Burnap, A., Gerth, R., Gonzalez, R. and Papalambros, P.Y. (2017), “Identifying experts in the crowd for evaluation of engineering designs”, Journal of Engineering Design, Vol. 28 No. 5, pp. 317–337, http://doi.org/10.1080/09544828.2017.1316013.CrossRefGoogle Scholar
Dosi, G. (1982), “Technological paradigms and technological trajectories. a suggested interpretation of the determinants and directions of technical change”, Research Policy, Vol. 11 No. 3, pp. 147–162, http://doi.org/10.1016/0048-7333(82)90016-6.CrossRefGoogle Scholar
Galen Russell Hancock (2016), Three Essays on Network Analysis and Patent Citation Networks, University of California, Berkeley, Ph.D. thesis.Google Scholar
Heydari, B., Bohsali, M., Adabi, E. and Niknejad, A.M. (2007), “Millimeter-wave devices and circuit blocks up to 104 ghz in 90 nm cmos”, IEEE Journal of Solid-State Circuits, Vol. 42 No. 12, pp. 2893–2903, http://doi.org/10.1109/JSSC.2007.908743.CrossRefGoogle Scholar
James, S.D., Leiblein, M.J. and Lu, S. (2013), “How Firms Capture Value From Their Innovations”, Journal of Management, Vol. 39 No. 5, pp. 1123–1155, http://doi.org/10.1177/0149206313488211.CrossRefGoogle Scholar
Jiang, S. and Luo, J. (2022), “Technology fitness landscape for design innovation: a deep neural embedding approach based on patent data”, Journal of Engineering Design, Vol. 33 No. 10, pp. 716–727, http://doi.org/10.1080/09544828.2022.2143155.CrossRefGoogle Scholar
Lanjouw, J.O. and Schankerman, M. (2004), “Patent quality and research productivity: Measuring innovation with multiple indicators*”, The Economic Journal, Vol. 114 No. 495, pp. 441–465, http://doi.org/10.1111/J.1468-0297.2004.00216.X.CrossRefGoogle Scholar
Lee, S.M., Olson, D.L. and Trimi, S. (2012), “Co-innovation: Convergenomics, collaboration, and co-creation for organizational values”, Management Decision, Vol. 50 No. 5, pp. 817–831, http://doi.org/10.1108/00251741211227528.CrossRefGoogle Scholar
Lee, T.H. (2003), The design of CMOS radio-frequency integrated circuits, Cambridge university press.CrossRefGoogle Scholar
Malhotra, A., Schmidt, T.S. and Huenteler, J. (2019), “The role of inter-sectoral learning in knowledge development and diffusion: Case studies on three clean energy technologies”, Technological Forecasting and Social Change, Vol. 146, pp. 464–487, http://doi.org/10.1016/j.techfore.2019.04.018.CrossRefGoogle Scholar
Moser, P., Ohmstedt, J. and Rhode, P.W. (2018), “Patent citations-an analysis of quality differences and citing practices in hybrid corn”, Management Science, Vol. 64 No. 4, pp. 1926–1940, http://doi.org/10.1287/mnsc.2016.2688.CrossRefGoogle Scholar
Peri, G. (2005), “Determinants of Knowledge Flows and Their Effect on Innovation”, The Review of Economics and Statistics, Vol. 87 No. 2, pp. 308–322, http://doi.org/10.1162/0034653053970258.CrossRefGoogle Scholar
Redelinghuys, C. (2000), “Proposed criteria for the detection of invention in engineering design”, Journal of Engineering Design, Vol. 11 No. 3, pp. 265–282, http://doi.org/10.1080/095448200750021021.CrossRefGoogle Scholar
Roach, M. and Cohen, W.M. (2013), “Lens or Prism? Patent citations as a measure of knowledge flows from public research”, Management Science, Vol. 59 No. 2, pp. 504–525, http://doi.org/10.1287/mnsc.1120.1644.CrossRefGoogle ScholarPubMed
Schaller, R.R. (1997), “Moore's law: past, present and future”, IEEE spectrum, Vol. 34 No. 6, pp. 52–59, http://doi.org/10.1109/6.591665.CrossRefGoogle Scholar
Steinberg, P.J., Asad, S. and Lijzenga, G. (2022), “Narcissistic CEOs' dilemma: The trade-off between exploration and exploitation and the moderating role of performance feedback”, Journal of Product Innovation Management, http://doi.org/10.1111/jpim.12644.CrossRefGoogle Scholar
Teece, D.J. (2008), “Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy”, in: The Transfer and Licensing of Know-How and Intellectual Property: Understanding the Multinational Enterprise in the Modern World, pp. 67–88, http://doi.org/10.1142/9789812833181_0005.CrossRefGoogle Scholar
Valencia-Romero, A. and Grogan, P.T. (2020), “Structured to succeed? Strategy dynamics in engineering systems design and their effect on collective performance”, Journal of Mechanical Design, Transactions of the ASME, Vol. 142 No. 12, http://doi.org/10.1115/L4048115. https://asmedigitalcollection.asme.org/mechanicaldesign/article/142/12/121404/1086264/Structured-to-Succeed-Strategy-Dynamics-inCrossRefGoogle Scholar
Valverde, U.Y., Nadeau, J.P. and Scaravetti, D. (2017), “A new method for extracting knowledge from patents to inspire designers during the problem-solving phase”, Journal of Engineering Design, Vol. 28 No. 6, pp. 369–407, http://doi.org/10.1080/09544828.2017.1316361.CrossRefGoogle Scholar
Von Wartburg, I., Teichert, T. and Rost, K. (2005), “Inventive progress measured by multi-stage patent citation analysis”, Research Policy, Vol. 34 No. 10, pp. 1591–1607, http://doi.org/10.1016/j.respol.2005.08.001.CrossRefGoogle Scholar
Wang, Z., Azarm, S. and Kannan, P.K. (2011), “Strategic design decisions for uncertain market systems using an agent based approach”, Journal of Mechanical Design, Transactions of the ASME, Vol. 133 No. 4, http://doi.org/10.1115/1.4003843.CrossRefGoogle Scholar
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