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article · Advances in Civil Engineering

Modelling the Impact of Blockchain Technology Barriers on Construction Procurement Systems in Nigeria

Abstract

Blockchain is a decentralised system based on cryptographic procedures and smart contracts that records every transaction and shares it with all parties involved, thereby handling issues of delays and trust. However, as identified in past studies, there are barriers to its adoption in the construction industry, especially in developing countries. There are no studies that assessed the impact of these barriers on the various procurement systems used in Nigeria; hence, this study is aimed at developing the statistical impact models of these barriers on critical procurement systems in Nigeria, with a view of identifying the most available and least‐affected system that is best fit for blockchain adoption. The study adopted the quantitative research approach through a questionnaire survey. Data was collected from 182 stakeholders in the Nigerian construction industry through a snowball sampling technique; these stakeholders were grouped as clients, contractors and consultants. The retrieved data were analysed using descriptive statistics and linear regression, while Cronbach’s alpha was used to test for reliability. The R 2 and the significance level of the three models indicated that they are significant, and the barriers significantly influence the use of blockchain smart contracts for the three procurement systems. The findings also predicted that the design and build (DB) procurement system is best suited for the easier adoption of blockchain smart contracts in the Nigerian architectural, engineering and construction (AEC) industry. This novel study has developed statistical impact models of blockchain barriers on the critical procurement systems in the Nigerian AEC industry, giving a road map for blockchain smart contracts usage.

Research topics

  • Blockchain Technology Applications and Security
  • Public Procurement and Policy
  • Organizational and Employee Performance

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DOI: 10.1155/adce/6625481

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