Using Task Support Requirements during Socio-Technical Systems Design

Socio-technical systems (STSs) are systems of systems, synthesising human and IT components that jointly operate to achieve specific goals. Such systems are overly complex but, if designed optimally, they can significantly improve STS performance. Critical phases in STS design are defining the functional requirements for automated or software-supported human activities and addressing social and human interaction issues. To define automation support for human operations, STS designers need to ensure that specifications will satisfy not only the non-functional requirements (NFR) of the system but also of its human actors such as human reliability/workload. However, such human factors aspects are not addressed sufficiently with traditional STS design approaches, which could lead to STS failure or rejection. This paper proposes a new STS design method that addresses this problem and introduces a novel type of requirements, namely, Task Support Requirements (TSR) that assists in specifying the functionality that IT systems should have to support human agents in undertaking their tasks by addressing human limitations. The proposed method synthesises a requirements/software engineering approach to STS design with functional allocation and an HCI perspective, which facilitates the application of human factors knowledge in conceptual models and evaluation through VR simulation. A case study methodology is employed in this work that allows in-depth, multi-faceted explorations of the complex issues that characterise STSs. Two case studies are presented in this work; the first is a detailed illustration of how the method is applied during the design of an in-vehicle information system to enhance drivers’ situation awareness. The second is an empirical evaluation of the method using participants that apply it to design a mobile application to minimise the risk of pedestrian travellers conceiving a contagious disease while commuting in public space. The results from the empirical evaluation showed that the method positively contributes to STS design by addressing human factors issues effectively.