Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App

Context: With the growing importance and complexity of software-based systems in relevant domain areas such as healthcare, education and e-government, acceptance of software products is essential.Problem / Motivation: We require to understand, model, and predict decisions taken by end users regarding the adoption and utilization of software products, where soft factors (such as human values, motivations and attitudes) need to be taken into account.Idea: In this paper, we address this need by using a novel probabilistic approach that allows the prediction of end users’ decisions and ranks soft factors importance in taking these decisions.Solution and Early Results: We implement a computational Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied in the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). We found that soft factors such as Fear of infection and Altruism were important for the COVID-19 app acceptance. The results are reported as part of a two stage-validation of the model.