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Innovation, technological regimes and organizational selection in industry evolution: a 'history friendly model' of the DRAM industry

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  • Chang-Wook Kim
  • Keun Lee

Abstract

The paper explores the complex relationship among innovation, technological regimes and selection of the firms of different organizational forms in the evolution of an industry. For this purpose, this paper develops a 'history-friendly' model for the dynamic random access memory industry that replicates the history of the industry by simulation. The shift of industry dominance by small, specialized (SS) firms to large, diversified (LD) firms is explained by the technological regimes of the industry, characterized by low cumulativeness and strong impact of innovation on productivity increase. The simulation analysis also found the following causal relationship underlying the evolution of the industry. First, the stronger innovation impact in terms of productivity jump tends to enlarge the productivity difference among the incumbent firms and increase the speed of productivity catch-up by the LD firms. Second, the possibility of entry and eventual dominance by the LD firms increase when the innovation--productivity linkage is stronger and there is less cumulativeness in productivity determination. Copyright 2003, Oxford University Press.

Suggested Citation

  • Chang-Wook Kim & Keun Lee, 2003. "Innovation, technological regimes and organizational selection in industry evolution: a 'history friendly model' of the DRAM industry," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 12(6), pages 1195-1221, December.
    • Handle: RePEc:oup:indcch:v:12:y:2003:i:6:p:1195-1221
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    Citations

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    Cited by:

    1. Chang, Sungyong & Kim, Hyunseob & Song, Jaeyong & Lee, Keun, 2021. "Dynamics of Imitation versus Innovation in Technological Leadership Change: Latecomers’ Catch-up Strategies in Diverse Technological Regimes," SocArXiv b8fae, Center for Open Science.
    2. Garavaglia, Christian, 2010. "Modelling industrial dynamics with "History-friendly" simulations," Structural Change and Economic Dynamics, Elsevier, vol. 21(4), pages 258-275, November.
    3. R. Fontana & L. Zirulia, 2015. "then came Cisco, and the rest is history : a history friendly model of the Local Area Networking industry," Working Papers wp993, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Li, Daitian & Capone, Gianluca & Malerba, Franco, 2019. "The long march to catch-up: A history-friendly model of China’s mobile communications industry," Research Policy, Elsevier, vol. 48(3), pages 649-664.
    5. Muscio, Alessandro & Nardone, Gianluca & Stasi, Antonio, 2012. "Perceived Technological Regimes: An Empirical Analysis of the Apulian Wine Industry," 2012 International European Forum, February 13-17, 2012, Innsbruck-Igls, Austria 144969, International European Forum on System Dynamics and Innovation in Food Networks.
    6. Peili Yu & Junguo Shi & Bert M. Sadowski & Önder Nomaler, 2020. "Catching Up in the Face of Technological Discontinuity: Exploring the Role of Demand Structure and Technological Regimes in the Transition from 2G to 3G in China," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 815-841, July.
    7. Chao Bi & Jingjing Zeng & Wanli Zhang & Yonglin Wen, 2020. "Modelling the Coevolution of the Fuel Ethanol Industry, Technology System, and Market System in China: A History-Friendly Model," Energies, MDPI, vol. 13(5), pages 1-26, February.
    8. Roberto Fontana & Lorenzo Zirulia, 2015. "“…then came Cisco, and the rest is history”: a ‘history friendly’ model of the Local Area Networking industry," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 875-899, November.
    9. Grunsven Leo van & Witte Inge, 2012. "Emergence through branching and evolution," ZFW – Advances in Economic Geography, De Gruyter, vol. 56(1-2), pages 168-184, October.
    10. Gianluca Capone & Franco Malerba & Richard R. Nelson & Luigi Orsenigo & Sidney G. Winter, 2019. "History friendly models: retrospective and future perspectives," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 9(1), pages 1-23, March.
    11. Sungho Rho & Keun Lee & Seong Hee Kim, 2015. "Limited Catch-up in China’s Semiconductor Industry: A Sectoral Innovation System Perspective," Millennial Asia, , vol. 6(2), pages 147-175, October.
    12. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 227-244, October.
    13. Toly Chen & Chungwei Ou & Yu-Cheng Lin, 2019. "A fuzzy polynomial fitting and mathematical programming approach for enhancing the accuracy and precision of productivity forecasting," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 85-107, June.

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