Purpose – The purpose of this paper is to verify the feasibility and evaluate the dimensional accuracy of two rapid casting (RC) solutions based on 3D printing technology: investment casting starting from 3D-printed starch patterns and the ZCast process for the production of cavities for light-alloys castings. Design/methodology/approach – Starting from the identification and design of a benchmark, technological prototypes were produced with the two RC processes. Measurements on a coordinate measuring machine allowed calculating the dimensional tolerances of the proposed technological chains. The predictive performances of computer aided engineering (CAE) software were verified when applied to the ZCast process modelling. Findings – The research proved that both the investigated RC solutions are effective in obtaining cast technological prototypes in short times and with low costs, with dimensional tolerances that are completely consistent with metal casting processes. Practical implications – The research assessed the feasibility and dimensional performances of two RC solutions, providing data that are extremely useful for the industrial application of the considered technologies. Originality/value – The paper deals with experimental work on innovative techniques on which data are still lacking in literature. In particular, an original contribution to the determination of dimensional tolerances and the investigation on the predictive performances of commercial CAE software is provided.

In recent years information and communication technologies (ICT) have played a significant role in all aspects of modern society and have impacted socioeconomic development in sectors such as education, administration, business, medical care and agriculture. The benefits of such technologies in agriculture can be appreciated only if farmers use them. In order to predict and evaluate the adoption of these new technological tools, the technology acceptance model (TAM) can be a valid aid. This paper identifies the most commonly used external variables in e-learning, agriculture and virtual reality applications for further validation in an e-learning tool designed for EU farmers and agricultural entrepreneurs. Starting from a literature review of the technology acceptance model, the analysis based on Quality Function Deployment (QFD) shows that computer self-efficacy, individual innovativeness, computer anxiety, perceived enjoyment, social norm, content and system quality, experience and facilitating conditions are the most common determinants addressing technology acceptance. Furthermore, findings evidenced that the external variables have a different impact on the two main beliefs of the TAM Model, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). This study is expected to bring theoretical support for academics when determining the variables to be included in TAM extensions.