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Enabling Technologies and the Role of Private Firms: A Machine Learning Matching Analysis

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  • Jason M. Rathje

    (AF Ventures, Alexandria, Virginia 22310; and Department of Management Science and Engineering, Stanford University, Stanford, California 94305)

  • Riitta Katila

    (Department of Management Science and Engineering, Stanford University, Stanford, California 94305)

Abstract

Investments in enabling technologies—including the fifth-generation technology standard for broadband cellular networks (5G), artificial intelligence (AI), and light detection and ranging (LIDAR) technology—are important strategic decisions for firms. This paper asks how inventions that private firms developed with (versus without) public-sector partners differ in their enabling technology trajectory. Using a novel method of machine learning matching, we compare patented technologies generated from more than 30,000 public–private relationships with comparable technologies invented by private firms alone during a 21-year period. To measure the enabling potential of a technology, we introduce a new enabling technology index. The findings show that private-firm relationships with the public sector—in particular cooperative agreements and grants with mission agencies (National Aeronautics and Space Administration and Department of Defense)—are likely starting points for enabling technology trajectories. We thus put a spotlight on organizational arrangements that combine the breadth of exploration (agreements, grants) with deep exploitation in a particular domain (mission agency). A key contribution is a better understanding of the types of private-firm efforts that are associated with enabling technologies. We also challenge the common assumption that enabling technologies have their origins only in public-sector projects and show how private firms are involved. Our significant contribution is to show how private firms can change evolution of ecosystems through technology development.

Suggested Citation

  • Jason M. Rathje & Riitta Katila, 2021. "Enabling Technologies and the Role of Private Firms: A Machine Learning Matching Analysis," Strategy Science, INFORMS, vol. 6(1), pages 5-21, March.
  • Handle: RePEc:inm:orstsc:v:6:y:2021:i:1:p:5-21
    DOI: 10.1287/stsc.2020.0112
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    1. Rahul Kapoor & David J. Teece, 2021. "Three Faces of Technology’s Value Creation: Emerging, Enabling, Embedding," Strategy Science, INFORMS, vol. 6(1), pages 1-4, March.
    2. Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Kroff & Emin Dinlersoz & Lucia S. Foster & Nikolas Zolas, 2023. "AI Adoption in America: Who, What, and Where," NBER Working Papers 31788, National Bureau of Economic Research, Inc.
    3. Sruthi Thatchenkery & Riitta Katila, 2023. "Innovation and profitability following antitrust intervention against a dominant platform: The wild, wild west?," Strategic Management Journal, Wiley Blackwell, vol. 44(4), pages 943-976, April.
    4. Goldfarb, Avi & Taska, Bledi & Teodoridis, Florenta, 2023. "Could machine learning be a general purpose technology? A comparison of emerging technologies using data from online job postings," Research Policy, Elsevier, vol. 52(1).
    5. Waßenhoven, Anna & Rennings, Michael & Laibach, Natalie & Bröring, Stefanie, 2023. "What constitutes a “Key Enabling Technology” for transition processes: Insights from the bioeconomy's technological landscape," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    6. Milan Miric & Nan Jia & Kenneth G. Huang, 2023. "Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents," Strategic Management Journal, Wiley Blackwell, vol. 44(2), pages 491-519, February.
    7. Nylund, Petra A. & Brem, Alexander & Agarwal, Nivedita, 2022. "Enabling technologies mitigating climate change: The role of dominant designs in environmental innovation ecosystems," Technovation, Elsevier, vol. 117(C).

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