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Behavioral drivers of blockchain assimilation in supply chains – A social network theory perspective

Author

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  • Patil, Kiran
  • Ojha, Divesh
  • Struckell, Elisabeth M.
  • Patel, Pankaj C.

Abstract

This study investigates decision drivers for assimilating blockchain technology in supply chains. The benefits of blockchain accrue when there is collaborative acceptance of supply chain members. We ground our hypotheses in tenets of social network theory to propose that supply chain learning, supply chain collaboration, and network prominence will influence blockchain assimilation by organizations. Based on a cross-sectional group of blockchain users (150 supply chain professionals familiar with blockchain), the study is the first to develop an understanding of behavioral drivers of blockchain assimilation at the organizational level and complements earlier studies focused on the intention to adopt the technology and identification of blockchain benefits. In addition to contributing to a nascent body of literature on the blockchain, we provide psychometrically validated scales for blockchain assimilation and network prominence constructs. The findings have implications for blockchain adoption in supply chain literature.

Suggested Citation

  • Patil, Kiran & Ojha, Divesh & Struckell, Elisabeth M. & Patel, Pankaj C., 2023. "Behavioral drivers of blockchain assimilation in supply chains – A social network theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:tefoso:v:192:y:2023:i:c:s0040162523002639
    DOI: 10.1016/j.techfore.2023.122578
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    Cited by:

    1. Simon Wong & John Kun Woon Yeung & Yui-Yip Lau & Tomoya Kawasaki & Raymond Kwong, 2024. "A Critical Literature Review on Blockchain Technology Adoption in Supply Chains," Sustainability, MDPI, vol. 16(12), pages 1-40, June.
    2. Hiran, Kamal Kant & Dadhich, Manish, 2024. "Predicting the core determinants of cloud-edge computing adoption (CECA) for sustainable development in the higher education institutions of Africa: A high order SEM-ANN analytical approach," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

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