Cooperative strategy for a dual-channel supply chain with the influence of free-riding customers

https://doi.org/10.1016/j.elerap.2020.101001Get rights and content

Highlights

  • Customer’s diverse purchasing behavior when facing multiple channels is considered.

  • Customer free-riding behavior is paid significant attention.

  • The exact influence of free-riding behavior on pricing and profits is explored.

  • The proposed strategy can remove negative influence of free-riding on the retailer.

  • The proposed strategy can realize members’ win-win situation when free-riding exists.

Abstract

In this paper, we consider that customers differ in their channel preference when facing multiple shopping channels. We focus on research shoppers who do not have a particular preference for either channel and are likely to become free-riders. To explore the influence of free-riding behavior, we build profit models with and without free-riding and compare the optimal solutions under the two scenarios. We find that free-riding can contribute to an increase in the total market demand by affecting members’ pricing decisions. However, it makes a divisional effect on the offline demand and intensifies channel conflict, which finally benefits the manufacturer but undermines the retailer’s profit. To solve this contradictory situation, we propose a cooperative strategy. We find that the cooperative strategy can effectively remove the negative influence of free-riding and make both members achieve Pareto improvements. Numerical examples are presented to show the effectiveness and feasibility of the cooperative strategy.

Introduction

In recent years, commerce on the Internet has grown at an attractive rate. A survey from China electronic commerce center reports that Chinese online retail sales were $1,032.3 billion in 2017, realizing an increase of 39.2% over 2016. With the mature state and prevalence of e-commerce, an increasing number of manufacturers including Haier, Nike, Apple, and Lenovo have engaged in online channels in the presence of pre-existing traditional channels, constructing dual-channel distribution systems for themselves (Li et al., 2016). Adding an online channel can help these manufacturers reach more customers and gain higher profits (Xu et al., 2014, Chen, 2015, Lu and Liu, 2015). However, it may also result in direct competition between the online and offline channels and cause channel conflict (Li and Li, 2016, Wang et al., 2017). That is, the online channels will inevitably cannibalize sales of the pre-existing traditional channels (Lu and Liu, 2013, Dan et al., 2014, Kim and Chun, 2018). In order to compete for customers, retailers build their competitive advantages by providing experience-based services in their offline channels, which are not available via the online channels. For instance, the retailers can provide customers with try-on samples, face-to-face communications with and instructions from salespersons, and so on (Hu and Li, 2012).

When facing multiple shopping channels, customers display diverse purchasing behaviors. Here, we focus on the multi-channel customers who are good at taking advantage of channel-specific characteristics to maximize their purchasing benefits (Balasubramanian et al., 2005, Verhoef et al., 2007). For example, these customers may enjoy the comfortable environment and face-to-face Q&A offered by specialized salespeople in a physical store, and are accustomed to receiving free pre-sale services (Chiou et al., 2012). They use the services to experience a product and obtain enough knowledge to make a purchasing decision. When they find out that the product matches their needs, they conduct a price comparison in the online channel. Since the operating costs in the online channel are usually lower than that in the offline channel, the price is generally lower (Brynjolfsson and Smith, 2000, Chiou et al., 2012, Luo et al., 2016).

Multi-channel customers face a trade-off problem in deciding which channel to purchase from. If they choose to stay in the offline channel and purchase, they can own the product that they have touched and carefully inspected, but will pay a higher price. However, if they choose to switch to the online channel for their purchase, they can benefit from a lower price, but incur risks and face uncertainties such as payment security, the additional time and effort required for searching for and checking the product, and so on. Thus, multi-channel customers need to solve the trade-off problem by comparing the consuming surpluses between the online and offline channels. Once they find out that the consuming surplus of purchasing online is larger, they will choose to switch to the online channel and become free-riding customers. The existence of customer free-riding behavior has been documented in the real world through empirical research and social surveys (van Baal and Dach, 2005, Balakrishnan et al., 2014).

Through this process, we can see that free-riding is a kind of rational behavior that customers conduct to maximize their benefits. However, such behavior makes the brick-and-mortar retailer feel harmed and victimized. He perceives that the manufacturer is taking away his would-be sales by offering the same products in an online channel. This may weaken his enthusiasm for providing services, and further negatively impact the supply chain cooperative relationship and its performance. So, we can see that from the perspective of customers, free-riding is simply a kind of customer behavior. However, from the perspective of the dual-channel supply chain, free-riding may play a negative role in the relationship between its members, and its overall performance. Therefore, it is important for the manufacturer, who is the owner of the dual-channel supply chain, to investigate the exact influence of customer free-riding behavior. Moreover, the manufacturer must think about how to adopt measures to alleviate channel conflict and improve the performance of the dual-channel supply chain in the presence of free-riding.

To address this problem, we consider a dual-channel supply chain consisting of a manufacturer and a retailer in which the manufacturer simultaneously distributes her product through her online channel and the retailer’s traditional channel. We employ the customer utility theory to characterize diverse purchasing behaviors when customers are faced with multiple shopping channels, especially the multi-channel customers. On this basis, we investigate the following issues:

  • (i)

    What is the exact influence of customer free-riding behavior on supply chain members’ optimal decisions, channel demand, and channel profits?

  • (ii)

    Under what conditions can one or both of the supply chain members benefit from customer free-riding behavior?

  • (iii)

    If the negative influence of free-riding exists, how can supply chain members alleviate its negative influence, and can they achieve their Pareto improvements by putting forward an effective strategy?

The rest of this paper is organized as follows. Section 2 reviews the related literature. Section 3 describes the customer channel choice model and builds supply chain members’ decision models. Section 4 solves the models. Section 5 investigates the influence of customer free-riding behavior. Section 6 demonstrates the role of the proposed strategy in realizing both members’ Pareto improvements, and Section 7 draws the related conclusions.

Section snippets

Literature review

In recent years, scholars have paid much attention to the research on the dual-channel supply chain. Once a manufacturer opens up a direct online channel, the relationship between supply chain members becomes more complicated than that of the single-channel supply chain. The manufacturer becomes not only a supplier, but also a direct competitor to the retailer. Such a complicated relationship may have a negative influence on the members’ relationship and undermine supply chain performance. Then

Modeling framework

This section first introduces the background of the problems we investigate. It then illustrates how customers make channel choices, and how the decision model is built.

Optimal solutions in the presence of free-riding

In this section, we first obtain supply chain members’ optimal solutions in the presence of free-riding, and then analyze the influence of the fraction of research shoppers on members’ optimal solutions. We consider a decentralized dual-channel supply chain (denoted by superscript D) where both members make individual decisions to maximize their own profit. The manufacturer acts as the leader and announces pm and w first. The retailer acts as the follower and determines pr to maximize his own

Analysis of the influence of customer free-riding behavior

In this section, we investigate the influence of customer free-riding behavior. We first obtain members’ optimal solutions in the absence of free-riding. On this basis, we then investigate the influence of free-riding by comparing the optimal solutions and profits under the two scenarios.

Cooperative strategy

According to the analyses in subsection 5.2, we can see that customer free-riding behavior increases the manufacturer’s profit, but undermines the retailer’s profit. Such a contradictory situation leads to a tense relationship between members and negatively impacts the steady and effective operation of the supply chain system. Thus, as the owner of the dual-channel supply chain, the manufacturer is motivated to alleviate the tense relationship and maintain a steady operation through feasible

Summary

In this paper, we consider customers’ multiple purchasing behaviors when facing multiple shopping channels. We classify customers into three types based on whether they have a preference for a particular channel and focus on research shoppers who do not have a particular preference for either channel. We analyze the impacts of the fraction of research shoppers and free-riding behaviors research shoppers may conduct on channel pricing, customer purchasing decision, channel demand and profit.

CRediT authorship contribution statement

Can Liu: Conceptualization, Methodology, Investigation, Formal analysis, Investigation, Writing - original draft, Funding acquisition. Yiran Dan: Writing - review & editing. Bin Dan: Supervision, Project administration, Funding acquisition. Guangye Xu: Methodology, Resources.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural Science Foundation of China [grant numbers 71572020]; the Major Program of the National Social Science Foundation of China [grant number 15ZDB169]; the Youth Foundation of Humanities and Social Sciences of Ministry of Education of China[grant number 19XJC630005]; the Research Program of Humanities and Social Sciences of Chongqing Municipal Education Commission [grant number 20SKGH175]; the Doctoral Program of Social Science Planning in Chongqing

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