PurposeThe 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/approachThis 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.FindingsThe results reveal perceived benefit and ease of use as the most influential determinants of the TAB.Originality/valueThis research is an initial effort to examine factors influencing TAB in the perspective of SMEs in Palestine using machine learning algorithms.

PurposeThis 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/approachThe 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.FindingsThe 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/valueThis 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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