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Organizational Impact of Blockchain through Decentralized Autonomous Organizations

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  • Soichiro Takagi

    (International University of Japan)

Abstract

There is a growing attention to “Blockchain” as a key technological innovation likely to change a wide spectrum of the economy and organizations. Blockchain, also referred to as “Distributed Ledger Technology (DLT)”, was initially created as a platform technology to enable Bitcoin. Bitcoin and similar digital currencies are issued and maintained by anonymous participants (peers) around the world. Although blockchain was developed to enable Bitcoin, there is a perception that it can be used not only for currencies but also for a wide range of assets, from digital content to real property. In addition, the development of the technology has enabled blockchain to work as a computing platform which conveys software codes in a decentralized network, eventually working as a “networked” or “decentralized” computer. The current prior studies are concentrated on the impact and challenges of Bitcoin or similar digital currencies, but the studies on the impact of the fundamental blockchain technology are limited. On the other hand, blockchain has a possibility to affect wide aspects of the economy, such as intermediary services, digital currency, organizational structures, data management, microtransactions, and newly created industry. Among them, this paper focuses on how blockchain may affect organizational structures and quantitatively analyses which occupations are most suitable for Decentralized Autonomous Organizations (DAO). The quantitative analysis with O*NET data reveals that three main clusters of occupations are the most suitable for DAO: “IT experts”, “Brokerage tasks”, and “Information handling occupations”.

Suggested Citation

  • Soichiro Takagi, 2017. "Organizational Impact of Blockchain through Decentralized Autonomous Organizations," International Journal of Economic Policy Studies, Springer, vol. 12(1), pages 22-41, January.
    • Handle: RePEc:spr:ijoeps:v:12:y:2017:i:1:d:10.1007_bf03405767
    DOI: 10.1007/BF03405767
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    References listed on IDEAS

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    1. Yukako Ono & Victor Stango, 2005. "Outsourcing, firm size, and product complexity: evidence from credit unions," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 29(Q I), pages 2-11.
    2. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    3. Neil Gandal & Hanna Halaburda, 2016. "Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market," Games, MDPI, vol. 7(3), pages 1-21, July.
    4. Adam Hayes, 2015. "A Cost of Production Model for Bitcoin," Working Papers 1505, New School for Social Research, Department of Economics.
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    Cited by:

    1. Olivier Meier & Aurélie Sannajust, 2021. "The smart contract revolution: a solution for the holdup problem?," Small Business Economics, Springer, vol. 57(2), pages 1073-1088, August.
    2. Olivier Meier & Aurélie Sannajust, 0. "The smart contract revolution: a solution for the holdup problem?," Small Business Economics, Springer, vol. 0, pages 1-16.

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