The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Blockchain technology adoption barriers in the Indian agricultural supply chain: an integrated approach
Abstract
Abstract Recent outbreaks of food contamination scandals in India have resulted in increased concerns amongst consumers about food safety. Further, to tackle such circumstances, conformation to quality has become a must in Agriculture Supply Chain (ASC). Agro-stakeholders have tried to accomplish this demand through the integration of technology in ASC. One such technology is blockchain, which is still in its infancy in terms of adoption in India. Fueled by this motivation, we aim to investigate the significant barriers for blockchain adoption in Indian ASC. The constructs are identified through a comprehensive literature search and opinion of domain experts from five agro-organizations, academia, and agro-stakeholders. Further identified construct is modeled using an integrated ISM-DEMATEL approach, which not only interrogates interrelationship amongst identified constructs but also evaluates the intensity of the relationship between them. The identified constructs are also clustered into a suitable group using Fuzzy-MICMAC. Furthermore, sensitivity analysis is also performed to check the robustness of the proposed model. The results reveal that “lack of government regulation and lack of trust among agro-stakeholder to use blockchain” are significant adoption barriers of blockchain in Indian ASC. Moreover, the managerial implication about the results is also provided, which will be useful to government and related agencies to take suitable measures to overcome these obstacles for increasing the rate of blockchain adoption in Indian ASC.
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Citations
Cites
Deep learning in agriculture: A survey
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.02.016 · Cited by 5,133
Blockchain technology and its relationships to sustainable supply chain management
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1533261 · Cited by 4,118
Blockchain challenges and opportunities: a survey
Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018) · International Journal of Web and Grid Services · doi:10.1504/ijwgs.2018.095647 · Cited by 3,651
Big Data in Smart Farming – A review
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017) · Agricultural Systems · doi:10.1016/j.agsy.2017.01.023 · Cited by 3,378
(2017) · Cited by 1,724
Blockchain challenges and opportunities: a survey
Wang, H., Zheng, Z., Xie, S., Dai, H., & Chen, X. (2018) · International Journal of Web and Grid Services · doi:10.1504/ijwgs.2018.10016848 · Cited by 1,582
The rise of blockchain technology in agriculture and food supply chains
Kamilaris, A., Fonts, A., & Prenafeta-Boldύ, F. X. (2019) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2019.07.034 · Cited by 1,261
Wireless sensors in agriculture and food industry—Recent development and future perspective
Wang, N., Zhang, N., & Wang, M. (2006) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2005.09.003 · Cited by 1,099
Si, S.-L., You, X.-Y., Liu, H.-C., & Zhang, P. (2018) · Mathematical Problems in Engineering · doi:10.1155/2018/3696457 · Cited by 1,082
An agri-food supply chain traceability system for China based on RFID & blockchain technology
Tian, F. (2016) · 2016 13th International Conference on Service Systems and Service Management (ICSSSM) · doi:10.1109/icsssm.2016.7538424 · Cited by 1,020
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Kumar, S., Raut, R. D., Nayal, K., Kraus, S., Yadav, V. S., & Narkhede, B. E. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.126023 · Cited by 412
Self-citationTaherdoost, H. (2022) · Computers · doi:10.3390/computers11020024 · Cited by 186
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
- Yadav, V. S., Singh, A. R., Raut, R. D., & Govindarajan, U. H.
- Venue
- Resources, Conservation and Recycling
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 161
- Pages
- 104877
- ISSN
- 0921-3449 (ISSN-L)
- OpenAlex
- W3028695454
- Cited by
- 384
- References
- 70
- FWCI
- 51.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yadav, V. S., Singh, A. R., Raut, R. D., & Govindarajan, U. H. (2020). Blockchain technology adoption barriers in the Indian agricultural supply chain: an integrated approach. Resources, Conservation and Recycling. https://doi.org/10.1016/j.resconrec.2020.104877
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