Arbeitspapier

Adoption and diffusion of blockchain technology

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".

Language
Englisch

Bibliographic citation
Series: ZEW Discussion Papers ; No. 24-018

Classification
Wirtschaft
Neural Networks and Related Topics
Technological Change: Choices and Consequences; Diffusion Processes
Real Estate Markets, Spatial Production Analysis, and Firm Location: General
Subject
technology adoption
blockchain technology
geographical distribution of firms
natural language programming

Event
Geistige Schöpfung
(who)
Gschnaidtner, Christoph
Dehghan, Robert
Hottenrott, Hanna
Schwierzy, Julian
Event
Veröffentlichung
(who)
ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung
(where)
Mannheim
(when)
2024

Last update
16.03.2025, 5:41 PM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Gschnaidtner, Christoph
  • Dehghan, Robert
  • Hottenrott, Hanna
  • Schwierzy, Julian
  • ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung

Time of origin

  • 2024

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