The influence of reference effect on pricing strategies in revenue management settings

Abstract

This paper studies the reference effect on dynamic pricing in revenue management for cases with limited capacity and stochastic demand. We first present a single-period fixed pricing (FP) model in finite horizon with fixed capacity and stochastic demand, and show that there is a unique optimal solution. The model is then extended to a discrete-time dynamic pricing (DP) model as a benchmark case. We subsequently propose a DP model with reference effect (DPR), investigate the properties of the revenue function, and present a computational scheme to compute the dynamic optimal price. Numerical experiments are conducted to exhibit how the reference effect may influence the initial price, pricing trend, price dispersion, and expected revenue, with the FP, DP, and DPR policies in the same environment of a fixed capacity facing stochastic demand. We also present, through numerical examples, a comparison between deterministic demand and stochastic demand scenarios under reference effect, and with a fixed initial price versus a variable initial price.

A joint dynamic pricing and production model with asymmetric reference price effect

“…Lin [25] considered the price promotion in a supply chain when taking into account the reference price effects and showed that the reference price effects could mitigate double marginalization effects. Yang et al [51] studied dynamic pricing strategies with reference effect for a seller facing a classical revenue management problem with stochastic demand, finite horizon, and fixed capacity.…”

Pricing and introductory scarcity strategies based on consumers' response

“…Within this literature track, various issues have been studied including, for example, right capacity and the conditions under which rationing is optimal (Liu and van Ryzin, 2008); the sufficient condition for optimal capacity rationing when customers have bounded rational expectations (Huang and Liu, 2015); and the pricing policy and rationing level of a firm in markup and markdown scenarios (Liu and Shum, 2013). Compared to this vast literature (other examples include Li et al, 2015;Bajwa et al, 2016;Duan et al, 2016;Yang et al, 2017;Yu et al, 2017;Feng, 2018Feng, , 2019, we focus on the supplier-induced scarcity initiated during the introductory period, and its impact on both consumers' preferences and pricing policies.…”

Editorial to the Special Issue on Optimization of Industrial Systems with Market Disruptions

“…Considering market demand disruptions and the reference effect on consumer purchasing decisions, Yang et al. () investigate how a firm facing limited capacity and stochastic demand should dynamically set optimal prices in the presence of reference effect scenarios. By developing and comparing three pricing models, a single‐period fixed pricing ( FP ) model, a discrete‐time dynamic pricing ( DP ) model, and a DP model with reference effect ( DPR ), they explore the properties of the optimal pricing strategies for these models and investigate the relationships between the maximum expected revenue, the initial stock, and the remaining selling horizon.…”