Decision Making Under the Uncertainty: Machines better than Humans?
(A2023-113616)

AUTHORS

Zainab Atia, University of Manchester; Hongwei He, Alliance Manchester Business School, The University of Manchester; Panagiotis Sarantopoulos, Alliance Manchester Business School, The University of Manchester

ABSTRACT

Algorithmic pricing has become a wide spread application in the age of technological advancement. With the wide availability of data for retailers, the ability to track consumers using algorithmic pricing has become an integral option in online platforms. Yet, little research has examined empirically consumers’ uncertain decision-making while dealing with AI pricing offers. As such, this paper examines consumers’ purchase deferral in response to two applied algorithmic pricing methods deployed by retailers, and uses the service setting of urgency, as a contextual factor that can mitigate such outcomes. Results reveal that consumers would defer less to AI personalized pricing offers under time pressure. The current research therefore fills the gap of urgent decision-making, to examine its impact on consumers’ deferral to machines-communicated pricing offers. Taken together, these findings provide theoretical and practical insights into human vs machine trade-offs in pricing contexts.