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Adoption of Emerging Digital General-purpose Technologies: Determinants and Effects

Author

Listed:
  • Kim Nguyen

    (Reserve Bank of Australia)

  • Jonathan Hambur

    (Reserve Bank of Australia)

Abstract

This paper examines the factors associated with the adoption of cloud computing and artificial intelligence/machine learning, two emerging digital general-purpose technologies (GPT), as well as firms' post-adoption outcomes. To do so we identify adoption of GPT based on references to these technologies in listed company reports, and merge this with data on their Board of Directors, their hiring activities and their financial performance. We find that firms that have directors with relevant technological backgrounds, or female representation on their Board, are more likely to profitably adopt GPT, with the former being particularly important. Worker skills also appear important, with firms that adopt GPT, particularly those that do so profitably, being more likely to hire skilled staff following adoption. Finally, while early adopters of GPT experience a dip in profitability following adoption, this is not evident for more recent adopters. This suggests that GPT may have become easier to adopt over time, potentially due to changes in the technologies or the availability of relevant skills, which is encouraging in terms of future productivity outcomes.

Suggested Citation

  • Kim Nguyen & Jonathan Hambur, 2023. "Adoption of Emerging Digital General-purpose Technologies: Determinants and Effects," RBA Research Discussion Papers rdp2023-10, Reserve Bank of Australia.
    • Handle: RePEc:rba:rbardp:rdp2023-10
    DOI: 10.47688/rdp2023-10
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    References listed on IDEAS

    as
    1. Nicholas Crafts, 2021. "Artificial intelligence as a general-purpose technology: an historical perspective," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 521-536.
    2. Tania Babina & Anastassia Fedyk & Alex X. He & James Hodson, 2023. "Firm Investments in Artificial Intelligence Technologies and Changes in Workforce Composition," NBER Working Papers 31325, National Bureau of Economic Research, Inc.
    3. Kim Nguyen, 2022. "The Real Effects of Debt Covenants: Evidence from Australia," RBA Research Discussion Papers rdp2022-05, Reserve Bank of Australia.
    4. Tania Babina & Anastassia Fedyk & Alex X. He & James Hodson, 2023. "Firm Investments in Artificial Intelligence Technologies and Changes in Workforce Composition," NBER Chapters, in: Technology, Productivity, and Economic Growth, National Bureau of Economic Research, Inc.
    5. Jonathan Hambur, 2023. "Did Labour Market Concentration Lower Wages Growth Pre-COVID?," RBA Research Discussion Papers rdp2023-02, Reserve Bank of Australia.
    6. Niccolò Gordini & Elisa Rancati, 2017. "Gender diversity in the Italian boardroom and firm financial performance," Management Research Review, Emerald Group Publishing Limited, vol. 40(1), pages 75-94, January.
    7. Niccolò Gordini & Elisa Rancati, 2017. "Gender diversity in the Italian boardroom and firm financial performance," Management Research Review, Emerald Group Publishing Limited, vol. 40(1), pages 75-94, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    technology adoption; productivity; management capability; skills;
    All these keywords.

    JEL classification:

    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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