E-commerce supply chain decisions under platform digital empowerment-induced demand

Highlights

• Develop a game model between an e-commerce platform and multiple retailers.

• Explore the effects of platform network on e-commerce supply chain management.

• Investigate how platform digital empowerment affects decisions of platform company and offline retailers.

• Achieve a win-win situation by using revenue-sharing or profit sharing contract.

Abstract

We develop a game model for a supply chain consisting of one e-commerce platform and multiple retailers. The platform with operational data of each retail store can effectively help retailers select marketable products and then improve their operational efficiency. We first investigate the motivation of retailers in accepting the platform digital empowerment (PDE) and joining the platform. We find that the retailer should join the platform and choose PDE only when the sales effort cost coefficient is high and the effect of sales effort on demand is weak. Second, we investigate the influence of the number of retailers on the platform network effect and find that the more retailers on the platform, the more profits the retailers and the e-commerce platform will make. Besides, the retailers’ sales efforts and the PDE level will increase. Third, we study the PDE decisions under the unconstrained and decentralized control system (DC), the decentralized control system with revenue sharing contract (RS) and the decentralized control system with profit sharing contract (PS), respectively. We find that the platform’s profit in PS is the highest among these systems if the sales effort cost coefficient is high. Furthermore, for these systems, the PDE level under DC is always the highest if the effort cost is low; otherwise, the PDE level under PS is the highest. In addition, the sales effort under DC is always the highest if PDE is employed, that is, nether RS nor PS can effectively promote the retailer’s sales effort.

Introduction

In the rapidly evolving retail environment, consumers’ needs still drive their purchasing decisions. With the development of the Internet, the extant research shows that the current main management mode is gradually shifting to the digital system management mode. With customer behaviors being gradually complicated, e-commerce platform companies are increasingly relying on digital technologies to interact with their customers (Parida, Sjödin, Lenka, & Wincent, 2015). Store-level data contains useful information about consumer needs that are more extensive and easier to obtain. (Besanko, Dubé, & Gupta, 2003). However, small grocers in less developed cities do not have the ability to choose marketable products based on consumers’ purchasing information, nor do they have the ability to fully explore the beneficial value behind consumption data and purchase behavior. Retailers have always been inundated with data. Therefore, some platforms start to help these traditional small grocery stores to improve their operational performance through excellent data resources and powerful data analysis technology. For example, Alibaba recently launched the ‘Ruyi’ POS machines, which rely on digital technology to collect consumption data from millions of small offline grocers. The system’s processing of consumption data can feed back the brand preference of different target groups to store owners. Not only that, it can even offer stores with inventory warning services and the most reasonable pricing suggestion. This e-commerce platform’s behavior is often referred to as the platform digital empowerment (PDE). According to a report from Alibaba, the average daily revenue growth of the renovated stores is more than 30%, and the average daily turnover can reach 5000–6000 Yuan (https://baijiahao.baidu.com/s?id=1610643212101852891&wfr=spider&for=pc). By the end of 2018, there are more than one million offline stores and the GMV (Gross Merchandise Volume) traded has tripled since 2017 (https://www.ebrun.com/20190213/320086.shtml).

E-commerce platform can enhance market demand through PDE (similar to an investment). In this way, platforms can improve consumers’ shopping experience in offline stores, which is quite popular at present. For example, in 2016, Amazon launched Amazon Go, an unmanned convenience store (Grewal, Roggeveen, & Nordfält, 2017). Customers scan the products by their smartphone when they enter the store, and Amazon can then track them with cameras and various sensors as they browse. Once they take an item off the shelf, it will be simultaneously added into a virtual shopping cart. Groceries will then charge the customers through their Amazon accounts when they leave with their goods (https://www.thenewstribune.com/news/business/article195890379.html). This advanced shopping experience of “grab it and go without queuing” has been widely acknowledged since it appears. Besides, Amazon improves its sales performance by collecting consumption data of offline stores and constantly optimizing operation decisions such as products display and replenishment. In China, many e-commerce platforms have also started to use PDE to improve the offline stores’ ability to utilize data. For instance, Freshippo, the leader of the new retailing fresh food e-commerce platforms in China, owns hundreds of offline stores. It improves its retail efficiency through in-depth mining of online and offline consumption big data, as well as digitization of membership, marketing, payment, operation and management systems. For offline franchisee outlets, store managers can master data of important user behaviors by analyzing customers’ age range, gender and whether they are regular customers, and by monitoring the customers’ attention span of each product on the shelf to determine the popularity of the product. Some similar PDE phenomena are also showing a rapid development trend. For example, Suning store and JD.com convenience store are respectively empowered by Suning retail platform and JD.com platform to provide digital processing technologies for the offline stores.

Recently, some business giants are starting to focus more on offline business to find new outlets. In this paper, we consider the PDE level as an investment degree. By improving the ability to use data and providing targeted sales guidance according to consumption data on the platform, the grocers’ market demand and operational efficiency could be improved. Intuitively, the platform will have a strong effect of publicity if there are many retailers on the platform. This effect will induce more retailers to join the platform and thus influence the operational decisions of e-commerce supply chain members. To be specific, e-commerce platforms can help small grocers select marketable products and substantially improve consumers’ shopping experience and retailers’ operating efficiency, so as to achieve precise marketing and increase market demand. For some large e-commerce platforms, they can rely on big data analysis to study consumption data and predict consumer behaviors to design more attractive products and improve profitability (Bradlow, Gangwar, Kopalle, & Voleti, 2017). As we know, both JD.com and Alibaba employ undifferentiated wholesale price schemes to different retailers, and the retailers selling the same products place their orders through Zhang Guibao (APP provided by JD.com) and Ling Shoutong (APP provided by Alibaba) based on the undifferentiated wholesale price. Since there is no process of price negotiation between retailers and the APPs platform, the platform usually provides multiple retailers with an undifferentiated wholesale price. Our study attempts to address the following research questions.

• First, are the retailers willing to accept PDE? Or under what conditions will the retailers accept it?

• Second, what is the optimal PDE level for retail stores in several common systems?

• Third, is there a win-win situation between the platform and the downstream retailers?

This paper constructs a supply chain model based on the PDE operation characteristics of e-commerce, which provides references and insights for further researches on supply chain contracts between e-commerce platforms and their downstream retailers. First, based on the e-commerce platform, we study how the optimal decisions (including the PDE level and retailers’ sales efforts) are affected by different supply chain systems, i.e., supply chain system without constraint (DC), supply chain system with revenue sharing contract (RS) or with profit sharing contract (PS). We show that if the sales effort is inefficient or have a limited effect on demand, it is necessary for them to join the platform and accept PDE to improve their data utilization. However, if the impact of the sales effort on the demand is high, all the retailers will reject to join the platform. Second, we investigate the influence of the number of retailers on platform network effect, and find that both the retailers and the platform can gain more profit as the number of retailers on the platform increases. Third, through comparison, we find that the DC system will be the best option for both the platform and the retailers among these systems when the sales effort has a great impact on demand. When the sales effort cost is high or the impact of sales effort on demand is low, the e-commerce platform can obtain the highest profit regardless of the profit cutting ratio. However, it is only when the profit cutting ratio is in a low range that the retailers under the PS system will obtain the highest profit among these systems.