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    <name>Article</name>
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              <text>Modelling of critical success factors for blockchain technology adoption readiness in the context of agri-food supply chain</text>
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          <name>Subject</name>
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              <text>AFSC; agri-food supply chain; best-worst method; blockchain technology; BWM; fuzzy; trust</text>
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              <text>The agri-food supply chain is continuously facing several challenges; the most severe are food quality and safety issues. These issues debilitate the performance of the supply chain and often harm the consumer's health. Therefore, there is an urgent need to address food quality and safety assurance in the supply chain. Blockchain technology (BCT) holds the potential to resolve these issues by enhancing security and transparency. The present study explores the critical success factors (CSFs) of BCT adoption readiness in the AFSC. Initially, CSFs are identified through a literature survey and finalised by experts' opinion. The finalised factors are prioritised using the fuzzy best-worst method, followed by sensitivity analysis. The results reflect that 'food quality control', 'provenance tracking and traceability', and 'partnership and trust' as the top three success factors. The study's findings will assist policymakers, managers, and practitioners in strategising the decision-making process while BCT dissemination. Copyright  2023 Inderscience Enterprises Ltd.</text>
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          <name>Creator</name>
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              <text>Shardeo V.; Patil A.; Dwivedi A.; Madaan J.</text>
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              <text>International Journal of Industrial and Systems Engineering, Vol-43, No. 1, pp. 80-102.</text>
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          <name>Publisher</name>
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              <text>Inderscience Publishers</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1504/IJISE.2021.10037223" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1504/IJISE.2021.10037223&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147494043&amp;amp;doi=10.1504%2FIJISE.2021.10037223&amp;amp;partnerID=40&amp;amp;md5=5bfaa346729a736baf3bd7b42e00a3d2" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147494043&amp;amp;doi=10.1504%2fIJISE.2021.10037223&amp;amp;partnerID=40&amp;amp;md5=5bfaa346729a736baf3bd7b42e00a3d2&lt;/a&gt;</text>
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              <text>ISSN: 17485037</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Shardeo V., Department of Operations and Analytics, Lal Bahadur Shastri Institute of Management, Delhi, India, Department of Management Studies, Indian Institute of Technology, Delhi, India; Patil A., Department of Management Studies, Indian Institute of Technology, Delhi, India, School of Business and Management, Christ (Deemed to be University), Bangalore, India; Dwivedi A., Jindal Global Business School, O.P. Jindal Global University, Sonipat, India; Madaan J., Department of Management Studies, Indian Institute of Technology, Delhi, India</text>
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