Abstract

Trust is an important aspect for policy makers in recommending the implementation of Industry 4.0 in food and beverage small and medium-sized enterprises (SMEs). SMEs? trust in the implementation of Industry 4.0 is defined as the level of belief in applying appropriate technology for Industry 4.0 based on their knowledge, familiarity, agreement and preference. Trust is a complex construct involving several Kansei words, or human mentality parameters. Artificial neural network modeling was utilized to model SMEs? trust in implementation of Industry 4.0. The research objectives were: 1) to analyze the trust of SMEs in the implementation of Industry 4.0 using Kansei Engineering; 2) to model the trust of SMEs in the implementation of Industry 4.0 using an artificial neural network (ANN). A questionnaire was developed using Kansei words that were generated from adjectives to represent human mentality parameters, which were stimulated by visual samples of Industry 4.0 technology. The questionnaires were distributed among 190 respondents from the three large islands of Indonesia. The data were recapitulated for training, validating and testing the ANN model based on the backpropagation supervised learning method. The best model architecture was eight input neurons, eight hidden neurons, and one output neuron (8-8-1) The output was classification of trust as ?distrust?, ?trust? or ?overtrust?. The research results indicated that the SMEs? trust was influenced by education, knowledge, familiarity, benefit, preference ranking and verbal components.