Coordinating of Pricing and Quality Strategies by Contracts in O2O Supply Chain With Stochastic Reference Quality Effect

Introduction

With the vigorous development of mobile Internet applications in recent years, the Internet business model has been rapidly replacing the traditional business model with physical and offline channels. To improve the probability of successful marketing, many traditional brick-and-mortar stores have developed a series of new retail models, such as online, mobile phone, and Tencent QQ (with Q meaning cute). In addition, world-famous e-commerce retailers, such as Amazon, Google, and eBay, have also built technology-enabled physical stores. Online-to-offline (O2O) retailing focuses on “a truly integrated approach across the whole retail operation that delivers a seamless response to the consumer experience through all available shopping channels” (Rigby, 2011; Saghiri et al., 2017). Thus, O2O retailing is becoming increasingly pervasive as consumers tend to switch between online and offline channels and exhibit higher levels of satisfaction and loyalty (Wallace et al., 2014). This not only presents unprecedented opportunities for supply chain members but also poses significant challenges to optimizing retail operations in the supply chain, including product pricing and quality investment coordination optimization, which are issues that consumers are particularly concerned about when making purchasing decisions. From academic and practical perspectives, it is necessary to study the coordination of pricing and product quality decisions from the perspective of the O2O supply chain.

Reference quality, as the cognitive quality of consumers, is formed over time through the information from the purchasing experiences of historical products. The reference quality is the product quality level in consumer minds and to which they compare the current product quality. If the current product quality level is below the reference quality level, the demand will experience a negative effect. So, there will be a decrease in demand, and vice versa. The difference between observed quality and reference quality can significantly affect purchasing decisions (Hardie et al., 1993; Mazumdar et al., 2005). Hence, the reference quality is reasonably included as an important factor impacting consumer demand. In addition, the reference quality of consumers exhibits a stochastic characteristic. For different consumer groups, such as brand loyal consumers and brand switchers, the two can make different purchasing decisions (Krishnamurthi et al., 1992). Besides this, even if facing a single homogeneous consumer group, they will still be influenced by external factors such as the quality of substitute products over time, which will impact their memory process and thus affect the reference quality. Especially in the era of O2O retailing with the rapid development of mobile Internet, consumers have a more convenient way to obtain this quality information, which has led to the uncertainty of reference quality (Mayhew & Winner, 1992). Therefore, studying the coordinated decision-making of pricing and quality in the O2O supply chain from the perspective of stochastic reference quality has more important theoretical and practical significance.

Recent studies have considered the supply chain coordination of pricing and quality decision-making based on deterministic reference quality effect (He et al., 2018; Qiu et al., 2022). While there are few studies on joint pricing and quality decision-making based on stochastic reference quality effect, Cao and Duan (2021) investigated the joint pricing and quality strategies of a monopolistic retailer under stochastic reference quality effect, but they did not examine the impact of stochastic reference quality effect on supplier decision-making. Our research adds to the existing literature by analyzing pricing and quality investment decisions within the O2O supply chain, while considering the stochastic reference quality effect. Through our model, based on the Hamilton–Jacobi–Bellman (HJB) equation, we derive the optimal pricing and quality investment strategies for various supply chain configurations.