Forecasting blockchain adoption in supply chains based on machine learning: evidence from Palestinian food SMEs

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Abstract
Purpose: This paper seeks to discover whether the technical, organisational and technology acceptance model (TAM) factors will significantly affect the adoption of blockchain technology (ABT) amongst SMEs. Design/methodology/approach: The research employs structural equation modelling (SEM) and a machine learning approach to identify factors influencing the ABT behaviour that leaders can use to predict the prospect of the ABT in their enterprises. Information was collected from 255 respondents representing 166 SMEs in the food industry, Palestine. Findings: The analyses reveal that the ABT is positively and significantly shaped by TAM factors: (1) perceived benefits and (2) perceived ease of using blockchain. Simultaneously, the former is significantly influenced by compatibility and upper management support, while the latter is affected by complexity. Finally, education and training affect both factors. Originality/value: This paper is amongst the first attempts to examine the ABT behaviour in the food industry using the integration of SEM and machine learning approach.

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