Research on Buy-Back Contract in Supply Chains Based on Mental Accounting Theory

Xiaole Zhang

doi:10.4236/jssm.2016.93027

Abstract

Adopting the concept of mental accounting, the paper analyzes loss-aversion retailer’s optimal order quantity under buy-back contract in the two-stage supply chain, and proves the existence and uniqueness of the optimal order quantity. Conclusions show: under the certain degree of loss aversion, there exists a unique repurchase price to achieve supply chain coordination; meanwhile, the system’s optimal order quantity decreases with the retailer’s loss aversion and increases with the buy-back price.

Highlights

By studying people’s irrational decision-making behavior in risk scenarios, Kahneman and Tversky put forward the prospect theory
Mental accounting is the set of cognitive operations used by individuals to organize, evaluate, and keep track of financial activities
A second component of mental accounting involves the assignment of activities to special accounts

Summary

Introduction

By studying people’s irrational decision-making behavior in risk scenarios, Kahneman and Tversky put forward the prospect theory. Ho et al study the multi-regional coordination problem of supply chain system with the assumption that retailers’ out stock cost bigger than overbooking and the paper conducts an experiment to verify that the theoretical model can better predict actual retailers’ order quantity [7]. Davis et al study how inventory risk sharing affects the efficiency of channels and distribution of profits by experimental methods, and the value predicted by the model based on mental accounting theory shows that APD contract cannot coordinate the supply chain under bounded rationality environment [10]. The major purposes of this paper include: 1) to establish the utility function adopting mental accounting theory, and to find out the optimal strategies of retailer and supplier; 2) to illustrate how variation of parameters influence on optimal decisions of supply chain members

Assumptions and Models

Model Analysis

Conclusions