Abstract

It is expected that blockchain technology will bring a disruptive paradigm shift in the manner in which transactions are conducted in the manufacturing and service enterprises. By eliminating the drawbacks of trust-related issues in a business chain, the distributed database of blockchain can bring transparency with pseudonymity and irreversibility of records. In this paper, we advance the limited literature on DLT and its adoption in the manufacturing and service enterprises. The proposed model is based on the integration of three traditional adoption theories namely Technology Acceptance Model (TAM), Technology Readiness Index (TRI) and Theory of Planned Behavior (TPB). Based on a survey of 211 experts of Pakistan, the proposed model was tested using structural equation modelling. The study result confirms that Theory of Planned Behavior and TAM play a key role in the disruptive technology implementation. It is one of the early studies on blockchain technology adoption in the manufacturing and service enterprises and the study results indicate that more manufacturing and service industries are transforming to intelligent operations. Smart manufacturing system through blockchain applications has become the focus of attention of businesses.

Details

Title
Predictors for distributed ledger technology adoption: integrating three traditional adoption theories for manufacturing and service operations
Author
Ullah, Nazir 1   VIAFID ORCID Logo  ; Al-rahmi, Waleed Mugahed 2   VIAFID ORCID Logo  ; Alkhalifah, Ali 3 

1  School of Management and Engineering, Department of Management Science and Engineering, Nanjing University, Nanjing, China 
2  Faculty of Social Sciences and Humanities, School of Education, Universiti Teknologi Malaysia, Johor Bahru, Malaysia 
3  Department of Information Technology, College of Computer, Qassim University, Saudia Arabia 
Pages
178-205
Publication year
2021
Publication date
Dec 2021
Publisher
Taylor & Francis Ltd.
e-ISSN
21693277
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1080/21693277.2021.1976963
ProQuest document ID
2628401213
Copyright
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.