S. Nandhini, S.D. Sivakumar, N. Palanichamy, V. Anandhi, P. Balasubramanian, R. Vasanthi
{"title":"Determinants of Blockchain Technology Adoption in Agricultural Supply Chain","authors":"S. Nandhini, S.D. Sivakumar, N. Palanichamy, V. Anandhi, P. Balasubramanian, R. Vasanthi","doi":"10.59467/ijass.2024.20.211","DOIUrl":null,"url":null,"abstract":"Blockchain technology adoption in agriculture can bring a win-win situation for all stakeholders in supply chain, which aids in the security of transactions and information in both forward and backward linkages in agricultural supply chain. Furthermore, blockchain platform benefits producers by enhancing their reputation, increasing their competitiveness and reducing fraud by eliminating undesirable practices in the supply chain. This research paper aims to analyze the determinants of blockchain technology adoption in agricultural supply chain. Grey relational analysis is employed to identify the determinant factors of blockchain technology adoption in the supply chain. The factors for blockchain technology adoption in supply chain are identified based on the literature review collected and focus group discussion with experts. Experts are insisted to rate the factors based on the five point likert scale. The findings revealed that data transparency, traceability, interoperability, data immutability and high value pricing, product type, organization trust, and government support can influence the blockchain adoption in supply chain. The findings conclude that priorities must be given to those influential factors which will help the organizational stakeholders in formulating suitable strategy for effectively implementing blockchain technology in the agricultural supply chain.. KEYWORDS :Blockchain technology, Adoption, Factors, Agricultural supply chain, Grey relational analysis.","PeriodicalId":50344,"journal":{"name":"International Journal of Agricultural and Statistical Sciences","volume":"117 15","pages":""},"PeriodicalIF":0.1000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Agricultural and Statistical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59467/ijass.2024.20.211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Blockchain technology adoption in agriculture can bring a win-win situation for all stakeholders in supply chain, which aids in the security of transactions and information in both forward and backward linkages in agricultural supply chain. Furthermore, blockchain platform benefits producers by enhancing their reputation, increasing their competitiveness and reducing fraud by eliminating undesirable practices in the supply chain. This research paper aims to analyze the determinants of blockchain technology adoption in agricultural supply chain. Grey relational analysis is employed to identify the determinant factors of blockchain technology adoption in the supply chain. The factors for blockchain technology adoption in supply chain are identified based on the literature review collected and focus group discussion with experts. Experts are insisted to rate the factors based on the five point likert scale. The findings revealed that data transparency, traceability, interoperability, data immutability and high value pricing, product type, organization trust, and government support can influence the blockchain adoption in supply chain. The findings conclude that priorities must be given to those influential factors which will help the organizational stakeholders in formulating suitable strategy for effectively implementing blockchain technology in the agricultural supply chain.. KEYWORDS :Blockchain technology, Adoption, Factors, Agricultural supply chain, Grey relational analysis.