Appropriate trust in and reliance on automation are critical for safe and efficient system operation. This paper fills an important research gap by describing a quantitative model of trust in automation. We extend decision field theory (DFT) to describe the multiple sequential decisions that characterize reliance on automation in supervisory control situations. Extended DFT (EDFT) represents an iterated decision process and the evolution of operator preference for automatic and manual control. The EDFT model predicts trust and reliance, and describes the dynamic interaction between operator and automation in a closed-loop fashion: the products of earlier decisions can transform the nature of later events and decisions. The simulation results show that the EDFT model captures several consistent empirical findings, such as the inertia of trust and the nonlinear characteristics of trust and reliance. The model also demonstrates the effects of different types of automation on trust and reliance. It is possible to expand the EDFT model for multioperator multiautomation situations.

As information-based processes are usually independent of the location or even the processor, they can be oftentimes either automated or relocated to foreign sites to profit from differences in wages. Both strategies bear enormous micro-economic potential in terms of cost savings. However, with the main focus on cost reduction, risk due to the uncertain development of effective labor costs or future transaction volumes are oftentimes either inadequately considered or neglected. This systematically leads to false decisions, in particular since the two strategies – relocation and automation – result in different risk profiles. In this paper, we analyze the conditions for automating or relocating parts of business processes and propose a decision model that suggests a risk/return efficient allocation to the alternatives. In particular, we consider how uncertainties of effective labor costs and transaction volumes influence the decision. As shifting tasks to other locations has effects on the workload at the original location, we also take into account costs for social effects. The practicability of our approach is demonstrated with an example that is based on real data of a major financial services provider.