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A machine learning method to predict the technology adoption of blockchain in Palestinian firms

Author

Listed:
  • Ihab K.A. Hamdan
  • Eli Sumarliah
  • Fauziyah Fauziyah

Abstract

Purpose - The study aims to deliver a decision support system for business leaders to estimate the potential for effective technological adoption of the blockchain (TAB) with a machine learning approach. Design/methodology/approach - This study uses a Bayesian network examination to develop an extrapolative system of decision support, highlighting the influential determinants that managers can employ to predict the TAB possibilities in their companies. Data were gathered from 167 SMEs in the largest industrial sectors in Palestine. Findings - The results reveal perceived benefit and ease of use as the most influential determinants of the TAB. Originality/value - This research is an initial effort to examine factors influencing TAB in the perspective of SMEs in Palestine using machine learning algorithms.

Suggested Citation

  • Ihab K.A. Hamdan & Eli Sumarliah & Fauziyah Fauziyah, 2021. "A machine learning method to predict the technology adoption of blockchain in Palestinian firms," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 17(4), pages 1008-1029, December.
    • Handle: RePEc:eme:ijoemp:ijoem-05-2021-0769
    DOI: 10.1108/IJOEM-05-2021-0769
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    Cited by:

    1. Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023. "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 549-568, March.

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