A machine learning based approach for predicting blockchain adoption in supply chain.
A discrete Bayesian network to predict the probability of blockchain adoption in an organization. Probabilities were given within the referenced paper. The vertices are:
Blockchain adoption (Low, High);
Compatibility (Low, High);
Complexity (Low, High);
Competitive pressure (Low, High);;
Perceived ease of use (Low, High);
Perceived financial benefits (Low, High);
Partner readiness (Low, High);
Perceived usefulness (Low, High);
Relative advantage (Low, High);
Training and education (Low, High);
Technical know-how (Low, High);
Top management support (Low, High);
@return An object of class bn.fit. Refer to the documentation of bnlearn for details.
Kamble, S. S., Gunasekaran, A., Kumar, V., Belhadi, A., & Foropon, C. (2021). A machine learning based approach for predicting blockchain adoption in supply chain. Technological Forecasting and Social Change, 163, 120465.