The DOI matches its Crossref record; 145 of the 145 references it cites were found in a registry or an index.
Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation
Abstract
Abstract The logistics and supply chain management (SCM) field is experimenting with the integration of blockchain, a cutting-edge, and highly disruptive technology. Yet, blockchain is still nascent, and the extant literature on this technology is scarce, especially as regards the relationship between blockchain and SCM. Additionally, existing studies have not yet addressed sufficiently the enablers of blockchain adoption and the interplay with supply chain performance. In order to reduce this gap, this study aims to examine the potential influence of blockchain on supply chain performance. We draw on the literature on technology adoption and supply chain performance, as well as on the emerging blockchain literature, to develop and test a model in two countries, namely India and the US. Accordingly, we administered a survey in order to review the opinions and views of supply chain practitioners. The results support the model and indicate that blockchain applications can improve supply chain performance. In particular, our findings suggest that knowledge sharing and trading partner pressure play an important role in blockchain adoption, and that supply chain performance is significantly influenced by supply chain transparency and blockchain transparency. Another finding was the inexistence of evidence for a moderation effect of the industry variable on the outcomes. The research conclusions have substantial managerial and theoretical implications. Our model contributes mainly to the theoretical advancement of SCM-blockchain, thus allowing scholars to adapt our validated model.
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
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
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
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
lavaan : An R Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709
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
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology · doi:10.2307/2072165 · Cited by 17,442
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
Cited by
Chowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.01.069 · Cited by 531
Queiroz, M. M., Fosso Wamba, S., De Bourmont, M., & Telles, R. (2021) · International Journal of Production Research · doi:10.1080/00207543.2020.1803511 · Cited by 339
Self-citationIndustry 4.0 and the supply chain digitalisation: a blockchain diffusion perspective
Wamba, S. F., & Queiroz, M. M. (2022) · Production Planning & Control · doi:10.1080/09537287.2020.1810756 · Cited by 298
Self-citationRodríguez-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
Taherdoost, H. (2022) · Computers · doi:10.3390/computers11020024 · Cited by 186
Metadata
- Authors
- Fosso Wamba, S., Queiroz, M. M., & Trinchera, L.
- Venue
- International Journal of Production Economics
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 229
- Pages
- 107791
- ISSN
- 0925-5273 (ISSN-L)
- OpenAlex
- W3022170296
- Cited by
- 473
- References
- 145
- FWCI
- 54.86
- 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: Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. https://app.cochat.ai/papers/10.1016/j.ijpe.2020.107791
- The paper
- Fosso Wamba, S., Queiroz, M. M., & Trinchera, L. (2020). Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2020.107791
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.