E-commerce supply chain decisions under platform digital empowerment-induced demand
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.
- (i)
First, are the retailers willing to accept PDE? Or under what conditions will the retailers accept it?
- (ii)
Second, what is the optimal PDE level for retail stores in several common systems?
- (iii)
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.
Section snippets
Literature review
This paper is closely related to platform digital empowerment (PDE) and network effect, e-commerce supply chain, and supply chain contracts. To highlight our contributions, we review only the literature that is representative and particularly relevant to our study.
Symbol description
We present a single-period supply chain game model with one e-commerce platform and n independent retailers, and all supply chain members are assumed to be risk-neutral. We assume that n retailers all have their own exclusive territory and there is no price or inventory competition among these retailers (Moon and Feng, 2017, Yu et al., 2009). In practice, each rural retail store has an exclusive area, which means there is no price and sales competition, e.g. stores renewed by JD.com. What’s
Supply chain decisions
In this part, we first discuss supply chain decisions without PDE in Section 4.1. For the supply chain with PDE, Section 4.2 provides the centralized decisions as a benchmark, and 4.3 DC system: Unconstrained and decentralized control system with PDE, 4.4 RS system: Decentralized control system under revenue-sharing (RS) contract with PDE, 4.5 PS system: Decentralized control system under profit-sharing (PS) contract with PDE investigate the equilibrium solutions for the different decentralized
Decision comparisons
To shed more insigts on supply chain decisions and profits under the difffernt supply chain systems, we analyze the scenario where all the retailers have the same demand scale. The following Propositions 7 and 8 give analytical comparisons among the above models with the same demand scale for each retailer. Proposition 7 The decisions of sales effort under these supply chain systmes satisfy the relationship as .
Proposition 7 demonstrates that the value of sales effort under the DC system is
Numerical examples
This section further analyzes the effect of key parameters on supply chain decisions and profits of supply chain members through numerical examples. Because the equilibrium solutions under the RS system take the same form as those under the PS system except for the unit wholesale price decision. Here, we use a numerical example of the RS system to obtain some insights According to the requirements of parameters in the model as well as the actual situation of the firm, we assume the default
Conclusions
Our paper addresses the issue of decision-making mechanism between a platform and multiple retailers, where the market demand is influenced by the platform’s digital empowerment and retailers’ sales efforts. We give the conditions that the retailers are willing to join the platform, and we find that each retailer’s profit is related to its sales effort cost coefficient and the effect of sales effort on demand. The result confirms that the retailer should join the platform and choose PDE when
CRediT authorship contribution statement
Di Xiao: Investigation, Conceptualization, Supervision. Xiansheng Kuang: Methodology, Writing - original draft. Kebing Chen: Formal analysis, Writing - review & editing.
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.
Acknowledgements
The authors thank the anonymous referees and an editor for their numerous constructive comments and encouragement in developing the paper. The work was supported by (i) the Natural Science Foundation of Zhejiang Province, China [Grant LY18G020002]; (ii) the National Natural Science Foundation of China [Grants 71971113 and 71571100]; (iii) Special Funds Project for Promoting the Level of Running Local Colleges and Universities in Zhejiang Province (Interdisciplinary Innovation Team Building of
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