Investigating the influence of organizational factors on blockchain adoption: An innovation theory perspective
Abstract
Purpose Blockchain possesses the potential to disrupt and reshape a plethora of industries in the next decade. However, blockchain adoption rates in technology developed countries, such as Ireland, are relatively low. Motivated by blockchain’s potential to transform sociotechnical systems, the lack of systematic inquiry pertaining to blockchain studies from an information system perspective, the authors propose the following research question: “How do organizational factors influence blockchain adoption in organizations based in a developed country?” Specifically, the purpose of this paper is to elucidate the impact of organizational factors on the adoption of blockchain and the adoption of blockchain in companies based in Ireland. Design/methodology/approach A comprehensive literature review was conducted, and the methods of qualitative content analysis were used to identify the most important technology–organization–environment (TOE) blockchain adoption factors. Organizational factors are often viewed as the most significant determinants of IT innovation adoption in organizations. Consequently, using a multiple-case study of 20 companies based in Ireland, the authors investigate how the top three organizational factors identified from the blockchain literature affected these companies decision to adopt or not adopt blockchain. Findings The literature review on blockchain adoption identified specific technological, organizational and environmental factors. Furthermore, the case study findings identified three patterns: top management support and organizational readiness are enablers for blockchain adoption, and large companies are more likely to adopt blockchain than small to medium-sized enterprises (SMEs). The authors explain these patterns by examining the nature of blockchain and the characteristics of Ireland as a developed country. Practical and scientific contributions are also presented. Research limitations/implications This study makes several important scientific contributions. First, the findings revealed that top management support and organizational readiness are significant enablers of blockchain adoption. Ireland is recognized as a technology developed country; however, the findings in relation to top management support contradict existing IT adoption literature pertaining to developed countries. Second, previous IT innovation adoption literature suggests that organizations size has a positive influence on a company’s IT innovation adoption process. This study demonstrates that large organizations are more likely to not only adopt blockchain but are also more likely to conduct increased levels of blockchain research and development activities. Finally, and most significantly, the authors identified several patterns, which relate specifically to Ireland as a developed country that influenced the findings. These findings could hold particular relevance to governments and organizations of other developed countries in terms of accelerating blockchain adoption. Practical implications The findings about the low level of blockchain awareness and the lack of information pertaining to viable business use cases indicate that the Irish government could play a more significant role in promoting the benefits of blockchain technologies. Further, the findings could also encourage IT providers to formulate enhanced strategies aimed at disseminating information pertaining to blockchain technologies. Second, the positive influence of top management support and organizational readiness, particularly about core competencies, on blockchain adoption suggests that equipping managers with the requisite knowledge and skills will be crucial in adopting these IT innovations. Finally, organizations who adopted blockchain used cloud-based blockchain platforms and tools to overcome the constraints of their initial low levels of organizational readiness. Originality/value This is one of the first studies to identify specific TOE blockchain adoption factors. Further, the authors examine how the three most identified organizational adoption factors impact organizations decisions to adopt blockchain. Finally, the authors discuss how the resulting three patterns identified by examining the nature of blockchain and the characteristics of Ireland as a technology developed country.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Top management support is positively related to HRM (AI) routinisation.
“Research Proposition 5 a-d: Top management support is positively related to HRM (AI) a) initiation, b) adoption, c) routinisation, and d)extension.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Clohessy & Acton, 2019
Top management support is positively related to HRM (AI) extension.
“Research Proposition 5 a-d: Top management support is positively related to HRM (AI) a) initiation, b) adoption, c) routinisation, and d)extension.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Clohessy & Acton, 2019
Top management support is positively related to HRM (AI) adoption.
“Research Proposition 5 a-d: Top management support is positively related to HRM (AI) a) initiation, b) adoption, c) routinisation, and d)extension.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Clohessy & Acton, 2019
Top management support is positively related to HRM (AI) initiation.
“Research Proposition 5 a-d: Top management support is positively related to HRM (AI) a) initiation, b) adoption, c) routinisation, and d)extension.”
From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Clohessy & Acton, 2019
Cites
Anderson, N., Potočnik, K., & Zhou, J. (2014) · Journal of Management · doi:10.1177/0149206314527128 · Cited by 3,103 · see citing works
A theory of organizational readiness for change
Weiner, B. J. (2009) · Implementation Science · doi:10.1186/1748-5908-4-67 · Cited by 2,268 · see citing works
Blockchain technology: principles and applications
Pilkington, M. (2016) · Research Handbook on Digital Transformations · doi:10.4337/9781784717766.00019 · Cited by 1,627 · see citing works
Zhu, K., Kraemer, K. L., & Xu, S. (2006) · Management Science · doi:10.1287/mnsc.1050.0487 · Cited by 1,440 · see citing works
Schwandt, T. A., Lincoln, Y. S., & Guba, E. G. (2007) · New Directions for Evaluation · doi:10.1002/ev.223 · Cited by 829 · see citing works
Understanding e-business adoption across industries in European countries
Oliveira, T., & Martins, M. F. (2010) · Industrial Management & Data Systems · doi:10.1108/02635571011087428 · Cited by 641 · see citing works
Infrastructuring: Toward an Integrated Perspective on the Design and Use of Information Technology
Siegen, U., Pipek, V., Wulf, V., & Siegen, F.-I. F. A. I. F. A. U. (2009) · Journal of the Association for Information Systems · doi:10.17705/1jais.00195 · Cited by 638 · see citing works
A Reflective Review of Disruptive Innovation Theory
Yu, D., & Hang, C. C. (2010) · International Journal of Management Reviews · doi:10.1111/j.1468-2370.2009.00272.x · Cited by 547 · see citing works
Molla, A., & Licker, P. S. (2005) · International Journal of Electronic Commerce · doi:10.1080/10864415.2005.11043963 · Cited by 429 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
AI-augmented HRM: Antecedents, assimilation and multilevel consequences
Prikshat, V., Malik, A., & Budhwar, P. (2023) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100860 · Cited by 200 · see citing works
- Authors
- Clohessy, T., & Acton, T.
- Affiliations
- Galway-Mayo Institute of Technology (IE)
- Ollscoil na Gaillimhe – University of Galway (IE)
- Venue
- Industrial Management & Data Systems
- Publisher
- Emerald
- Published
- 2019-08-12
- Volume
- 119
- Issue
- 7
- Pages
- 1457-1491
- ISSN
- 0263-5577 (ISSN-L)
- OpenAlex
- W2953858196
- Cited by
- 436
- References
- 9
- Citations by year
- 2026: 60 · 2025: 71 · 2024: 96 · 2023: 76 · 2022: 64 · 2021: 47 · 2020: 20 · 2019: 2
- FWCI
- 41.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Industrial Management & Data Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Keywords
- blockchain adoption, organizational factors, technology-organization-environment framework, top management support, organizational readiness, firm size, small and medium-sized enterprises, developed countries, Ireland, business use cases, government role in technology adoption, socio-technical systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review