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

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  • Kamble, Sachin S.
  • Gunasekaran, Angappa
  • Kumar, Vikas
  • Belhadi, Amine
  • Foropon, Cyril

Abstract

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.

Suggested Citation

  • Kamble, Sachin S. & Gunasekaran, Angappa & Kumar, Vikas & Belhadi, Amine & Foropon, Cyril, 2021. "A machine learning based approach for predicting blockchain adoption in supply Chain," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:tefoso:v:163:y:2021:i:c:s0040162520312919
    DOI: 10.1016/j.techfore.2020.120465
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    4. Dehghani, Milad & William Kennedy, Ryan & Mashatan, Atefeh & Rese, Alexandra & Karavidas, Dionysios, 2022. "High interest, low adoption. A mixed-method investigation into the factors influencing organisational adoption of blockchain technology," Journal of Business Research, Elsevier, vol. 149(C), pages 393-411.
    5. Gupta, Somya & Ghardallou, Wafa & Pandey, Dharen Kumar & Sahu, Ganesh P., 2022. "Artificial intelligence adoption in the insurance industry: Evidence using the technology–organization–environment framework," Research in International Business and Finance, Elsevier, vol. 63(C).
    6. Sahebi, Iman Ghasemian & Mosayebi, Alireza & Masoomi, Behzad & Marandi, Fatemeh, 2022. "Modeling the enablers for blockchain technology adoption in renewable energy supply chain," Technology in Society, Elsevier, vol. 68(C).
    7. Jana, Rabin K. & Ghosh, Indranil & Wallin, Martin W., 2022. "Taming energy and electronic waste generation in bitcoin mining: Insights from Facebook prophet and deep neural network," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    8. Ulpan Tokkozhina & Ana Lucia Martins & Joao C. Ferreira, 2023. "Uncovering dimensions of the impact of blockchain technology in supply chain management," Operations Management Research, Springer, vol. 16(1), pages 99-125, March.
    9. Dwivedi, Yogesh K. & Balakrishnan, Janarthanan & Das, Ronnie & Dutot, Vincent, 2023. "Resistance to innovation: A dynamic capability model based enquiry into retailers’ resistance to blockchain adaptation," Journal of Business Research, Elsevier, vol. 157(C).
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    11. Pardis Roozkhosh & Alireza Pooya & Renu Agarwal, 2023. "Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach," Operations Management Research, Springer, vol. 16(2), pages 705-725, June.
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