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A machine learning based approach for predicting blockchain adoption in supply Chain

机译：基于机器学习方法，用于预测供应链中的区块链采用

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The purpose of this paper is to provide a decision support system for managers to predict an organization's probability of successful blockchain adoption using a machine learning technique. The study conceptualizes blockchain technology as a dynamic capability that should be possessed by the organization to remain competitive. The factors influencing the blockchain adoption behavior were modeled using the theoretical lens of the Technology Acceptance Model and Technology-organisation-Environment framework. The findings identify competitor pressure, partner readiness, perceived usefulness, and perceived ease of use as the most influencing factors for blockchain adoption. A predictive decision support system was developed using a Bayesian network analysis featuring the significant factors that can be used by the decision-makers for predicting the probability of blockchain adoption in their organization. The prior probability values reported in the study may be used as indicators by the practitioners to predict their blockchain adoption probability. The practitioner will be required to substitute these probability values (high or low), as applicable to their organization to estimate the adoption probability. The use of the decision support system is likely to help the decision-makers to assess their adoption probability and develop future adoption strategies.
机译：本文的目的是为管理人员提供决策支持系统，以预测使用机器学习技术的组织成功的区块链采用的概率。该研究将区块链技术视为一种动态能力，该组织应该拥有持竞争力。影响区块链采用行为的因素采用技术验收模型和技术组织环境框架的理论镜头进行了建模。调查结果识别竞争对手压力，合作伙伴准备，感知的有用性，以及感知易用性作为区间集会采用的最大影响因素。使用贝叶斯网络分析开发了预测决策支持系统，该网络分析具有可供决策者使用的重要因素，以预测其组织在其组织中的区块链采用的可能性。该研究中报告的现有概率值可作为从业者的指标来预测其区块链的采用概率。将要求从业者替代这些概率值（高或低），适用于其组织来估计采用概率。决策支持系统的使用可能有助于决策者评估他们的采用概率并制定未来的采用策略。

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