Forecasting Blockchain Adoption in the Automotive Industry - A Delphi Study

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Xinyue, Wang (2020) Forecasting Blockchain Adoption in the Automotive Industry - A Delphi Study. [Dissertation (University of Nottingham only)]

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Abstract

With the rapid development of blockchain technology, blockchain may provide solutions to many of the challenges currently faced by the automotive industry. The automotive industry will adopt blockchain technology on a large scale in the future. To realise the potential benefits, companies should look at their strategic goals and assess and analyse which blockchain capabilities are best for them to invest in and how. This study examines how blockchain works in the automotive industry and the blockchain deployments of the major car companies and makes predictions about the blockchain applications in the automotive industry that are likely to take hold in the next five years. There have been many academics who have developed architectures for specific blockchain application scenarios in the automotive industry, and many reviews of the overall state of the application in the industry. However, there are very limited predictions possibilities of the automotive industry, and there are no predictions about the speed of the future development process based on the opinions of industry experts. This study uses the Delphi method to interview 20 experts in the relative industry, and thorough analysisa of primary data, finds that the application of blockchain in the automotive industry is widely recognized, particularly in the areas of supply chain management, automotive aftermarket, and new energy vehicles, but the application in the areas of autonomous driving and connected vehicles still faces technical and economic challenges. This study provides a reference for future blockchain deployments in automotive companies and sets a precedent for Delphi method research in this area.

Item Type: Dissertation (University of Nottingham only)
Depositing User: Wang, Xinyue
Date Deposited: 25 Apr 2023 11:44
Last Modified: 25 Apr 2023 11:44
URI: https://eprints.nottingham.ac.uk/id/eprint/66520

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