Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 1,079

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA

Queiroz, Maciel M., Fosso Wamba, Samuel·2019·International Journal of Information Management

Abstract

Abstract The digitalization phenomenon is leveraging new relationship models through the entire supply chain network. In this outlook, blockchain is a cutting-edge technology that is already transforming and remodeling the relationships between all members of logistics and supply chain systems. Yet, while studies on blockchain have gained a relative pace over the recent years, the literature on this topic does not report sufficient research cases on blockchain adoption behavior at the individual level. The present study, therefore, aims to bridge this gap, notably by helping understand the individual blockchain adoption behavior in the logistics and supply chain field in India and the USA. Drawing on the emerging literature on blockchain, supply chain and network theory, as well as on technology acceptance models (TAMs), we have developed a model based on a slightly-altered version of the classical unified theory of acceptance and use of technology (UTAUT). The model being developed was then estimated using the Partial least squares structural equation modeling (PLS-SEM). As the model was eventually supported, the results obtained revealed the existence of distinct adoption behaviors between India-based and USA-based professionals. In parallel, the findings appear as a useful contribution to and a sign of progress for the literature on IT adoption, SCM, and blockchain.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ijinfomgt.2018.11.021

Cites

30 of 30 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 30 references

Research works printed but not linked: 85

  1. Ahmad, 2017, The adoption of M-government services from the user’s perspectives: Empirical evidence from the United Arab Emirates, International Journal of Information Management, 37, 367, 10.1016/j.ijinfomgt.2017.03.008 · DOI · Paper page
  2. Akter, 2017, Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality, Production Planning and Control, 28, 1011, 10.1080/09537287.2016.1267411 · DOI · Paper page
  3. Alalwan, 2017, Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust, International Journal of Information Management, 37, 99, 10.1016/j.ijinfomgt.2017.01.002 · DOI · Paper page
  4. Al-Sayyed, 2016, Interventional factors affecting instructors adoption of e-learning system: A case study of palestine, Journal of Theoretical and Applied Information Technology, 83, 119
  5. Awaysheh, 2010, The impact of supply chain structure on the use of supplier socially responsible practices, International Journal of Operations & Production Management, 30, 10.1108/01443571011094253 · DOI · Paper page
  6. Banerjee, 2018, A blockchain future to Internet of Things security: A position paper, Digital Communications and Networks, 1
  7. Bartlett, 2007, Improving supply chain performance through improved visibility, The International Journal of Logistics Management, 18, 294, 10.1108/09574090710816986 · DOI
  8. Batara, 2017, Are government employees adopting local e-government transformation?, Transforming Government: People Process and Policy, 11, 612, 10.1108/TG-09-2017-0056 · DOI
  9. Borgatti, 2003, The network paradigm in organizational research: A review and typology, Journal of Management, 29, 991, 10.1016/S0149-2063(03)00087-4 · DOI · Paper page
  10. Borgatti, 2009, On social network analysis in a supply chain context, The Journal of Supply Chain Management, 45, 5, 10.1111/j.1745-493X.2009.03166.x · DOI · Paper page
  11. Brown, 2010, Predicting collaboration technology use: Integrating technology adoption and collaboration research, Journal of Management Information Systems, 27, 9, 10.2753/MIS0742-1222270201 · DOI · Paper page
  12. Burt, 1980, Models of network structure, Annual Review of Sociology, 6, 79, 10.1146/annurev.so.06.080180.000455 · DOI · Paper page
  13. Büyüközkan, 2018, Digital supply chain: Literature review and a proposed framework for future research, Computers in Industry, 97, 157, 10.1016/j.compind.2018.02.010 · DOI · Paper page
  14. Carter, 2015, Toward the theory of the supply chain, The Journal of Supply Chain Management, 51, 89, 10.1111/jscm.12073 · DOI
  15. Chae, 2014, The impact of supply chain analytics on operational performance: A resource-based view, International Journal of Production Research, 52, 4695, 10.1080/00207543.2013.861616 · DOI · Paper page
  16. Chin, 1998, Issues and opinion on structural equation modeling, MIS Quarterly, 22
  17. Choi, 2009, Supply networks: Theories and models, The Journal of Supply Chain Management, 45, 25, 10.1111/j.1745-493X.2009.03168.x · DOI
  18. Choi, 2001, Supply networks and complex adaptive systems: Control versus emergence, Journal of Operations Management, 19, 351, 10.1016/S0272-6963(00)00068-1 · DOI · Paper page
  19. Chua, 2018, Elucidating social networking apps decisions performance expectancy, effort, Nankai Business Review International, 9, 118, 10.1108/NBRI-01-2017-0003 · DOI
  20. Compeau, 1995, Computer self-efficacy: Development of a measure and initial test, MIS Quarterly, 19, 189, 10.2307/249688 · DOI · Paper page
  21. Farooq, 2017, Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2, Interactive Technology and Smart Education, 14, 329, 10.1108/ITSE-06-2016-0015 · DOI · Paper page
  22. Fosso Wamba, 2017, Role of intrinsic and extrinsic factors in user social media acceptance within workspace: Assessing unobserved heterogeneity, International Journal of Information Management, 37, 1, 10.1016/j.ijinfomgt.2016.11.004 · DOI
  23. Fosso Wamba, 2018, Bitcoin, Blockchain, and FinTech: A systematic review and case studies in the supply chain, Production Planning and Control
  24. Goldsby, 2016, Technology innovation and new business models: Can logistics and supply chain research accelerate the evolution?, Journal of Business Logistics, 37, 80, 10.1111/jbl.12130 · doi:10.1111/jbl.12130
  25. Granovetter, 1973, The strength of weak ties, The American Journal of Sociology, 78, 1360, 10.1086/225469 · DOI · Paper page
  26. Granovetter, M. S. (1985). Economic Action and Social Structure: The Problem of Embeddedness Author (s): Mark Granovetter Source: American Journal of Sociology, Vol. 91, No. 3 (Nov., 1985), pp. 481-510 Published by: The University of Chicago Press Stable URL: http://w, 91(3), 481–510.
  27. Guide Jr. V. D. R., & Ketokivi, M. (2015). Notes from the Editors: Redefining some methodological criteria for, 37. https://doi.org/10.1016/S0272-6963(15)00056-X. · DOI · Paper page
  28. Gulati, 1998, Vol. 19, 293
  29. Hair, 2014, Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research, European Business Review, 26
  30. Hair, 2017, A primer on partial least squares structural equation modeling, Vol. 46
  31. Han, 2017, Reconceptualization of information technology flexibility for supply chain management: An empirical study, International Journal of Production Economics, 187, 196, 10.1016/j.ijpe.2017.02.018 · DOI
  32. Hazen, 2017, Enterprise architecture: A competence-based approach to achieving agility and firm performance, International Journal of Production Economics, 193, 566, 10.1016/j.ijpe.2017.08.022 · DOI
  33. Hew, 2015, What catalyses mobile apps usage intention: An empirical analysis, Industrial Management & Data Systems, 115, 1269, 10.1108/IMDS-01-2015-0028 · DOI · Paper page
  34. Huang, 2012, An empirical investigation of factors influencing the adoption of data mining tools, International Journal of Information Management, 32, 257, 10.1016/j.ijinfomgt.2011.11.006 · DOI
  35. Irani, 2009, Understanding consumer adoption of broadband: An extension of the technology acceptance model, The Journal of the Operational Research Society, 60, 1322, 10.1057/jors.2008.100 · DOI
  36. Jeble, 2017, Impact of big data & predictive analytics capability on supply chain sustainability, The International Journal of Logistics Management, 29, 513, 10.1108/IJLM-05-2017-0134 · DOI · Paper page
  37. Kamble, 2018, Understanding the Blockchain technology adoption in supply chains-Indian context, International Journal of Production Research, 0, 1, 10.1080/00207543.2018.1518610 · DOI · Paper page
  38. Kano, 2018, A novel approach to solve a mining work centralization problem in blockchain technologies, International Journal of Pervasive Computing and Communications, 14, 15, 10.1108/IJPCC-D-18-00005 · DOI
  39. Kim, 2017, A perspective on blockchain smart contracts: Reducing uncertainty and complexity in value exchange, 2017 26th International Conference on Computer Communications and Networks, ICCCN 2017
  40. Kshetri, 2017, Blockchain’s roles in strengthening cybersecurity and protecting privacy, Telecommunications Policy, 41, 1027, 10.1016/j.telpol.2017.09.003 · DOI · Paper page
  41. Lamming, 2001, Transparency in supply relationships: Concept and practice, The Journal of Supply Chain Management, 37, 4, 10.1111/j.1745-493X.2001.tb00107.x · DOI · Paper page
  42. Li, 2018, Toward open manufacturing, Industrial Management & Data Systems, 118, 303, 10.1108/IMDS-04-2017-0142 · DOI · Paper page
  43. Liébana-Cabanillas, 2017, A SEM-neural network approach for predicting antecedents of m-commerce acceptance, International Journal of Information Management, 37, 14, 10.1016/j.ijinfomgt.2016.10.008 · DOI · Paper page
  44. Makanyeza, 2018, Consumers’ acceptance and use of plastic money in Harare, Zimbabwe, International Journal of Bank Marketing, 36, 379, 10.1108/IJBM-03-2017-0044 · DOI
  45. Mamonov, 2017, Exploring factors affecting social e-commerce service adoption: The case of Facebook Gifts, International Journal of Information Management, 37, 590, 10.1016/j.ijinfomgt.2017.05.005 · DOI
  46. Martins, 2014, Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application, International Journal of Information Management, 34, 1, 10.1016/j.ijinfomgt.2013.06.002 · DOI · Paper page
  47. Maruping, 2017, Going beyond intention: Integrating behavioral expectation into the unified theory of acceptance and use of technology, Journal of the Association for Information Science and Technology, 68, 623, 10.1002/asi.23699 · DOI
  48. Mayer, 1995, An integrative model of organizational trust, The Academy of Management Review, 20, 709, 10.5465/amr.1995.9508080335 · doi:10.5465/amr.1995.9508080335 · Paper page
  49. Morgan, 2018, Supplier transparency: Scale development and validation, The International Journal of Logistics Management, 10.1108/IJLM-01-2017-0018 · DOI
  50. Mortenson, 2016, A computational literature review of the technology acceptance model, International Journal of Information Management, 36, 1248, 10.1016/j.ijinfomgt.2016.07.007 · DOI
  51. Oliveira, 2014, Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM, International Journal of Information Management, 34, 689, 10.1016/j.ijinfomgt.2014.06.004 · DOI · Paper page
  52. Chen, 2018, A traceability chain algorithm for artificial neural networks using T–S fuzzy cognitive maps in blockchain, Future Generation Computer Systems, 80, 198, 10.1016/j.future.2017.09.077 · DOI
  53. Reyna, 2018, On blockchain and its integration with IoT. Challenges and opportunities, Future Generation Computer Systems, 88, 173, 10.1016/j.future.2018.05.046 · DOI · Paper page
  54. Riffai, 2012, Big TAM in Oman: Exploring the promise of on-line banking, its adoption by customers and the challenges of banking in Oman, International Journal of Information Management, 32, 239, 10.1016/j.ijinfomgt.2011.11.007 · DOI · Paper page
  55. Sabi, 2016, Conceptualizing a model for adoption of cloud computing in education, International Journal of Information Management, 36, 183, 10.1016/j.ijinfomgt.2015.11.010 · DOI · Paper page
  56. Sampson, 2015, Process coordination within a health care service supply network, Journal of Business Logistics, 36, 355, 10.1111/jbl.12106 · doi:10.1111/jbl.12106
  57. Shim, 2018, Rival precedence and open platform adoption: An empirical analysis, International Journal of Information Management, 38, 217, 10.1016/j.ijinfomgt.2017.10.001 · DOI
  58. Stolze, 2015, The role of social mechanisms in demand and supply integration: An individual network perspective, Journal of Business Logistics, 36, 49, 10.1111/jbl.12069 · DOI
  59. Sun, 2017, The construct of information systems use benefits: Theoretical explication of its underlying dimensions and the development of a measurement scale, International Journal of Information Management, 37, 400, 10.1016/j.ijinfomgt.2017.04.010 · DOI
  60. Svensson, 2001, Perceived trust towards suppliers and customers in supply chains of the Swedish automotive industry, International Journal of Physical Distribution & Logistics Management, 31, 647, 10.1108/09600030110408152 · DOI
  61. Svensson, 2018, Developing a theory of focal company business sustainability efforts in connection with supply chain stakeholders, Supply Chain Management, 23, 16, 10.1108/SCM-12-2015-0461 · DOI
  62. Sydow, 2013, Labor, risk, and uncertainty in global supply networks – Exploratory insights, Journal of Business Logistics, 34, 236, 10.1111/jbl.12022 · DOI
  63. Thompson, 1991, Personal computing: Toward a conceptual model of utilization, MIS Quarterly, 15, 125, 10.2307/249443 · DOI · Paper page
  64. Tian, 2017, A supply chain traceability system for food safety based on HACCP, blockchain & internet of things, 14th International Conference on Services Systems and Services Management, ICSSSM 2017 - Proceedings, 10.1109/ICSSSM.2017.7996119 · DOI · Paper page
  65. Tichy, 1979, Social network analysis for organizations, The Academy of Management Review, 4, 507, 10.5465/amr.1979.4498309 · doi:10.5465/amr.1979.4498309 · Paper page
  66. Toyoda, 2017, A Novel blockchain-based Product Ownership Management System (POMS) for anti-counterfeits in the post supply chain, IEEE Access, XXX, 1
  67. Tsanos, 2016, The effects of behavioural supply chain relationship antecedents on integration and performance, Supply Chain Management, 21, 678, 10.1108/SCM-06-2016-0211 · DOI
  68. Tsou, 2015, Performance effects of technology-organization-environment openness, service co-production, and digital-resource readiness: The case of the IT industry, International Journal of Information Management, 35, 1, 10.1016/j.ijinfomgt.2014.09.001 · DOI
  69. Venkatesh, 2001, A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges, MIS Quarterly, 25, 71, 10.2307/3250959 · DOI · Paper page
  70. Venkatesh, 2000, A theoretical extension of the technology acceptance model: Four longitudinal field studies, Management Science, 46, 186, 10.1287/mnsc.46.2.186.11926 · DOI · Paper page
  71. Venkatesh, 2008, Predicting different conceptualizations of system use: The competing roles of behavioral intention, facilitating conditions, and behavioral expectation, MIS Quarterly, 32, 483, 10.2307/25148853 · DOI · Paper page
  72. Veuger, 2018, Trust in a viable real estate economy with disruption and blockchain, Facilities, 36, 103, 10.1108/F-11-2017-0106 · DOI
  73. Viryasitavat, 2018, Blockchain-based Business Process Management (BPM) framework for service composition in industry 4.0, Journal of Intelligent Manufacturing
  74. Wang, 2013, What drives purchase intention in the context of online content services? The moderating role of ethical self-efficacy for online piracy, International Journal of Information Management, 33, 199, 10.1016/j.ijinfomgt.2012.09.004 · DOI · Paper page
  75. Wang, 2018, Developing and validating a technology upgrade model, International Journal of Information Management, 38, 7, 10.1016/j.ijinfomgt.2017.07.003 · DOI
  76. Warshaw, 1985, Disentangling Behavioral Intention and Behavioral Expectation, 228, 213
  77. Weerakkody, 2013, Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation, International Journal of Information Management, 33, 716, 10.1016/j.ijinfomgt.2013.05.001 · DOI · Paper page
  78. Whipple, 2013, Conceptualizations of trust: Can we trust them?, Journal of Business Logistics, 34, 117, 10.1111/jbl.12014 · DOI
  79. Wu, 2011, A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type, International Journal of Information Management, 31, 572, 10.1016/j.ijinfomgt.2011.03.004 · DOI · Paper page
  80. Chen, 2018, Blockchain tokens and the potential democratization of entrepreneurship and innovation, Business Horizons, 61, 567, 10.1016/j.bushor.2018.03.006 · DOI · Paper page
  81. Yadlapalli, 2018, Socially responsible governance mechanisms for manufacturing firms in apparel supply chains, International Journal of Production Economics, 196, 135, 10.1016/j.ijpe.2017.11.016 · DOI
  82. Yin, 2017, M2M security technology of CPS based on blockchains, Symmetry, 9, 10.3390/sym9090193 · DOI
  83. Zhang, 2018, The relationships between electronic banking adoption and its antecedents: A meta-analytic study of the role of national culture, International Journal of Information Management, 40, 76, 10.1016/j.ijinfomgt.2018.01.015 · DOI · Paper page
  84. Zhu, 2018, How supply chain analytics enables operational supply chain transparency, International Journal of Physical Distribution & Logistics Management, 48, 47, 10.1108/IJPDLM-11-2017-0341 · DOI · Paper page
  85. Zou, 2018, A proof-of-Trust consensus protocol for enhancing accountability in crowdsourcing services, IEEE Transactions on Services Computing, 1374, 1, 10.1109/TSC.2018.2823705 · DOI · Paper page

Cited by

See all 1,079

Metadata

Authors
Queiroz, M. M., & Fosso Wamba, S.
Venue
International Journal of Information Management
Publisher
Elsevier BV
Published
2019-06
Volume
46
Pages
70-82
ISSN
0268-4012 (ISSN-L)
OpenAlex
W2903429044
Cited by
1,079
References
30
FWCI
105.26
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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 challenges in supply chain: An empirical investigation of the main drivers in India and the USA. https://app.cochat.ai/papers/10.1016/j.ijinfomgt.2018.11.021
The paper
Queiroz, M. M., & Fosso Wamba, S. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2018.11.021

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.