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SSRN Electronic Journal
Article . 2024 . Peer-reviewed
Data sources: Crossref
EconStor
Research . 2024
Data sources: EconStor
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Adoption and Diffusion of Blockchain Technology

Authors: Gschnaidtner, Christoph; Dehghan, Robert; Hottenrott, Hanna; Schwierzy, Julian;

Adoption and Diffusion of Blockchain Technology

Abstract

A widespread approach to measuring the innovative capacity of companies, sectors, and regions is the analysis of patents and trademarks or the use of surveys. In emerging digital technologies this approach may, however, not be sufficient for mapping technology diffusion. This applies to blockchain technology which is in essence, a decentralized and distributed database (management system) that is increasingly used well beyond its originally intended purpose as the underlying infrastructure for a peer-to-peer payment system. In this article, we use an alternative method based on web-analysis and deep learning techniques that allow us to identify companies that use blockchain technology to determine its diffusion. Our analysis shows that blockchain is still a niche technology with only 0.88% of the analyzed firms using it. At the same time, certain sectors, namely ICT, banking & finance, and (management) consulting, show higher adoption rates ranging from 3.50% to 4.50%. Most blockchain companies are located at or close to one of the financial centers. Young firms whose business model is (partly) based on blockchain technology also locate themselves close to these centers. Thus, despite blockchain technology often being explicitly characterized as decentralized and distributed in nature, these adoption and strategic location decisions lead to "blockchain clusters".

Country
Germany
Keywords

O33, R30, 330, ddc:330, blockchain technology, geographical distribution of firms, technology adoption, C45, natural language programming

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    5
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Top 10%
Average
Top 10%
Green