A Bayesian network-based model to understand the role of soft requirements in technology acceptance

Soft requirements (such as human values, motivations, and personal attitudes) can strongly influence technology acceptance. As such, we need to understand, model and predict decisions made by end users regarding the adoption and utilization of software products, where soft requirements need to be taken into account. Therefore, we address this need by using a novel Bayesian network approach that allows the prediction of end users' decisions and ranks soft requirements' importance when making these decisions. The approach offers insights that help requirements engineers better understand which soft requirements are essential for particular software to be accepted by its target users. We have implemented a Bayesian network to model hidden states and their relationships to the dynamics of technology acceptance. The model has been applied to the healthcare domain using the NHS COVID-19 Test and Trace app (COVID-19 app). Our findings show that soft requirements such as Responsibility and Trust (e.g. Trust in the supplier/brand) are relevant for the COVID-19 app acceptance. However, the importance of soft requirements is also contextual and time-dependent. For example, Fear of infection was an essential soft requirement, but its relevance decreased over time. The results are reported as part of a two stage-validation of the model.