The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index.
Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy
Abstract
The adoption of technologies by the operations and supply chain management (OSCM) field is leading to extraordinary disruptions. And with the rapid emergence of cutting-edge and more disruptive technologies, the OSCM is striving to take advantage of such innovations, but they are bringing in their wake a number of challenges. One of those disruptive technologies is blockchain, which is increasingly accepted in virtually all industries. This study aims to investigate the blockchain technology (BCT) adoption behaviour and possible barriers in the Brazilian OSCM context. We developed a model drawing on the unified theory of acceptance and use of technology (UTAUT) model, the supply chain literature, and the emerging literature on BCT. We empirically validated the proposed model with Brazilian operations and supply chain professionals by using the partial least squares structural equation modelling (PLS-SEM). Our findings revealed that facilitating conditions, trust, social influence, and effort expectancy are the most critical constructs that directly affect BCT adoption. Unexpectedly, performance expectancy appeared not decisive in terms of predicting BCT adoption. This study contributes to advancing and stimulating the theory about BCT adoption behaviour in supply chains, as well as important managerial implications, which may be more critical for emerging economies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
The theory of planned behavior
Ajzen, I. (1991) · Organizational Behavior and Human Decision Processes · doi:10.1016/0749-5978(91)90020-t · Cited by 90,587
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
When to Use and How to Report the Results of PLS-SEM
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019) · European Business Review · doi:10.1108/ebr-11-2018-0203 · Cited by 27,541
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Self-Reports in Organizational Research: Problems and Prospects
Podsakoff, P. M., & Organ, D. W. (1986) · Journal of Management · doi:10.1177/014920638601200408 · Cited by 17,102
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
Estimating Nonresponse Bias in Mail Surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.2307/3150783 · Cited by 12,222
Cited by
Rodríguez-Espíndola, O., Chowdhury, S., Dey, P. K., Albores, P., & Emrouznejad, A. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.121562 · Cited by 279
Camilleri, M. A. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123247 · Cited by 261
Rana, N. P., Dwivedi, Y. K., & Hughes, D. L. (2022) · Information Technology & People · doi:10.1108/itp-07-2020-0460 · Cited by 122
Metadata
- Authors
- Queiroz, M. M., Fosso Wamba, S., De Bourmont, M., & Telles, R.
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2020-08-13
- Volume
- 59
- Issue
- 20
- Pages
- 6087-6103
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W3049728687
- Cited by
- 339
- References
- 115
- FWCI
- 40.72
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy. https://app.cochat.ai/papers/10.1080/00207543.2020.1803511
- The paper
- Queiroz, M. M., Fosso Wamba, S., De Bourmont, M., & Telles, R. (2021). Blockchain adoption in operations and supply chain management: empirical evidence from an emerging economy. International Journal of Production Research. https://doi.org/10.1080/00207543.2020.1803511
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.