Power structure and channel integration strategy for online retailers

Power structure and channel integration strategy for online retailers

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Power structure and channel integration strategy for online retailers Yihong Hu, Shengnan Qu, Guo Li, Suresh Sethi PII: DOI: Reference:

S0377-2217(19)30902-6 https://doi.org/10.1016/j.ejor.2019.10.050 EOR 16136

To appear in:

European Journal of Operational Research

Received date: Accepted date:

25 January 2019 31 October 2019

Please cite this article as: Yihong Hu, Shengnan Qu, Guo Li, Suresh Sethi, Power structure and channel integration strategy for online retailers, European Journal of Operational Research (2019), doi: https://doi.org/10.1016/j.ejor.2019.10.050

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Highlights • We examine an online retailer’s integration strategy with an express company. • Six scenarios under three power structures are discussed and compared. • We identify three important factors influencing the integration strategy. • The online retailer prefers integration only in the online Stackelberg game. • A mixed channel relaxes the strict condition for integration.

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Power structure and channel integration strategy for online retailers Yihong Hu, Shengnan Qu School of Economics and Management, Tongji University, Shanghai 200092, China

Guo Li* School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China, [email protected] Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China

Suresh Sethi Naveen Jindal School of Management, The University of Texas at Dallas, Dallas, TX 75080, USA

With the boom of e-commerce, express delivery has been increasingly regarded as a bottleneck and key factor for achieving success. Additionally, whether to include such express delivery service or not is an important yet outstandingly unsolved problem for online retailers. In this regard, this paper uses a game-theoretic framework to investigate the channel structure, in which an offline retailer competes with an online retailer selling products to consumers through its partner express company. The consumers purchase from either an online or offline channel considering the delivery service as well as the inconvenience of shopping from physical stores. We consider three power structures: online retailer Stackelberg game, offline retailer Stackelberg game and Nash game. Under each power structure, we characterize the channel integration strategy for the online retailer. Interestingly, our results show that the online channel integration is not beneficial for the online retailer in most cases. Online retailers prefer to use express companies as intermediaries to avoid large logistics operations costs in the offline retailer Stackelberg game and Nash game. Only in the online retailer Stackelberg game, where the online retailer has the first-move advantage in the market, together with a moderate store-visiting inconvenience cost and a delivery service cost coefficient, will vertical integration improve the online channel’s profit. Dominant market power ensures sufficient profit to cover the logistics cost, and the moderate inconvenience cost and service cost coefficient promise a moderate logistics cost. Under this condition, the online retailer will choose the vertical integration strategy. We show in the extension that this strict condition can be relaxed when the online retailer owns a mixed channel. The online retailer with a mixed channel has more incentive to integrate than a pure online retailer does, as the mixed channel adds his power and helps to gain more market shares and profit. Our analysis generates managerial insights into the relationship between online retailers and express companies and provides a guide for implementing the vertical integration strategy in the online retailing industry. Key words : E-commerce; Power structure; Dual channel; Vertical integration; Express delivery service

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1.

Introduction

Online retailers rely heavily on express delivery companies to deliver goods to customers. Those express delivery companies handle not only traditional functions such as packaging, transportation and last-mile delivery but also some of the retailer’s functions such as collecting payment at the door, providing clothes and shoes try-on, taking return packages, collecting face-to-face feedback from consumers, etc. It seems that the traditional retailer’s functions are now divided into two in online retailing. Some of the online stores even degenerate into a mere advertising and marketing function, as advertised by Amazon’s “Fulfillment by Amazon” service, outsourcing operations’ logistics functions to it and focusing on customers and the market. The express delivery companies have grown into important intermediaries between retailers and consumers. They are thought of as necessities primarily for their functions such as independently investing in infrastructure, expanding and maintaining the network, and providing delivery service efficiently. It is usually believed that third-party service providers are preferred for their efficiency in performing the tasks. In this study, we show that even when the retailer performs the delivery task as efficiently as the outside service provider, decentralization is still preferred in most cases. That is, when considering trading off the cost of bearing the logistics operations cost against losing complete control over how the products are delivered, most online retailers would choose putting delivery service companies between them and the customers. Recently, the industry has seen a tendency for giant online retailers to purchase their logistics service providers. In 2016, Amazon was reported to have a full acquisition of a package-delivery company in France (Weise, 2016; Rao, 2016). In 2017, Amazon, together with its branch Souq.com, purchased a logistics service company in the Middle East (Lunden, 2017). In early 2018, it announced launching “Shipping with Amazon” service first in Los Angeles. These activities were believed to be parts of a larger move by Amazon to own and manage shipping and distribution services instead of relying on others. Its competitors followed suit. Walmart’s online business announced its purchase of Parcel – a same-day delivery service in New York – in Aug 2017 (Locklear, 2017). Target’s online business announced a $550 million purchase of Shipt, which would bring same-day delivery to approximately half of its stores by early 2018 (Isidore, 2017). Suning, the largest retailer in China, purchased the 7th largest company in the Chinese express industry at 4.25 billion RMB in 2017 (Ouyang, 2017). These retailers serve as good examples for vertical integration inside the online retailing channel. JD.com, one of the largest e-commerce platforms in China, is another example of centralization through establishing their own delivery team from the very beginning. Its very efficient logistics service with a twelve-hour delivery promise greatly enhances its competitiveness in the market. All of these practices show online retailers’ ambition in completely controlling their delivery service and integrating the whole channel.

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However, this channel integration is not always successful. For instance, Vancl.com, a famous online fashion retailer in China, used to have its own logistics express team, Rufengda, and boasted about its delivery service performance. However, it finally sold the delivery business to another large express company in China in the fall of 2014 (Heima, 2014). In an interview, Vancl’s CEO admitted that the logistics delivery and online retailing business did not match very well, and separating them was the best choice (Lei, 2014). Motivated by these emerging phenomena in the online retailing industry, we pursue the following research questions. (1) In a competitive environment with traditional stores, is the vertical integration between online retailers and express companies beneficial or not? (2) What drives these retailers to integrate or not integrate downstream service companies? (3) Given the different power structures, what are online retailers’ preferred strategies? To answer these questions, we consider a dual-channel supply chain composed of one online retailer with an express delivery company and one brick-and-mortar store. Consumers make their choice between online and traditional channels considering the delivery service level, as well as the relative inconvenience cost of shopping from physical stores. The service level and service charge are determined by the express company. We assume that online retailers and traditional stores are price competitors in the market and have a power imbalance between them. Online retailers may be the leader or the follower in the market. Three possible power structures are studied together with two integrated or decentralized decision scenarios. Comparing the equilibrium results, we derive the conditions that encourage vertical integration in online retailing. Our results show that it is not always optimal for the online retailers to integrate with logistics delivery companies. Instead, online retailers may use independent intermediaries to avoid a large logistics handling cost burden, especially when they cannot dominate the market. In competitive dual channels, decentralization introduces two effects: the direct negative effect and the indirect positive effect. The direct negative effect comes from the double-marginalization effect raising the online channel’s price and lowering the demand. A large delivery service cost coefficient enhances the direct effect and prevents integration. The indirect positive effect concerns inducing a rising equilibrium price of the competitive channel and that can have a beneficial effect of raising the online retailer’s demand. Moreover, dominant market power and a large inconvenience cost for visiting the store both reduce the indirect positive effect. The final result depends on whether the direct effect outperforms the indirect effect or, in other words, the mixed effect from three factors: service cost coefficient, market power and inconvenience cost. Among these three factors, market power has the most important influence. When the online retailer does not dominate the market, it always prefers decentralization. Only when the online

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retailer has the first-move advantage to control the competitor’s equilibrium price and win sufficient profit to cover the service cost, integration becomes a possible choice. At that time, the inconvenience cost and service cost coefficient both should not be too large to promise the adoption of an integration strategy. A large inconvenience cost indicates the store is selling bulky products or has customers in rural areas. It softens price competition intensity and encourages online channel integration. A large service cost coefficient represents a large operations cost in logistics delivery and, therefore, prevents integration. We identify the condition that combines two opposite effects to allow for the retailer to employ vertical integration. We also find that a rising consumers’ sensitivity towards service level relaxes the condition for integration. We further analyse the impact of decentralization on consumers and the store and find that brick-and-mortar stores always benefit from online channel decentralization, while consumers are mostly hurt from decentralization. In the extension, we consider the integration problem of an online retailer owning a mixed channel (i.e., both the online channel and the offline channel) when he competes with either a brick-andmortar store or an online retailer. We find that the mixed channel relaxes the strict condition and the online retailer still has incentive to integrate. Specifically, in the case of competing with an offline retailer, the online retailer either has the same integration strategy as in the base model when he has a weak or equal offline channel, or can integrate in three games when he has a strong offline channel. The strong offline channel helps the online retailer to gain a portion of the offline market share and obtain more profit. The reason is that the strong offline channel strengthens the online retailer’s market power in addition to the power in decision sequence and therefore encourages the integration. This supports our theory in the base model that market power is predominant among three factors. In contrast, in connection with the case of competing with the online retailer, the mixed-channel online retailer always has incentive to integrate as he earns more profit from both channels with integration than from only the online channel without integration where the offline channel generates zero profit. Here the mixed channel again helps to obtain more profit to cover the logistics cost. In summary, the mixed channel has a beneficial impact on the integration strategy. Our contribution is threefold. First, to our best knowledge, we are the first to study the online retailer’s strategic decision regarding the integration of a downstream express company in a competitive dual-channel environment. Second, previous studies in channel power structure usually focus on a vertical power imbalance inside the channel instead of on a horizontal imbalance between the same level in two channels. We model different power structures at the horizontal retailer level, assuming two retailers are the price leader and follower, or vice versa, in the market. Then, we discuss how this power imbalance impacts the decision of integration or decentralization in the online channel. Third, we find that the market dominance, the right to determine the price first, together with an inconvenience cost and a service cost that are not too large to encourage the

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online retailer to adopt vertical integration. The findings provide insights for online retailers and industry practitioners that a vertical integration strategy needs strict conditions to be successful; otherwise, it will lead to failure with a large probability. The remainder of this paper is organized as follows. Section 2 provides a literature review. Section 3 describes the basic model settings. In Section 4, we analyse the equilibrium results with centralized and decentralized modes of the online channel composed of one online retailer and one express shipping company under three different power structures. Then, in Section 5, we compare the results and analyse the impact of centralization and power structure. We derive the conditions for centralization between the online retailer and express company. In Section 6, we extend our base model. Finally, concluding remarks are presented in Section 7. All proofs are provided in the Appendix.

2.

Literature review

This paper addresses a channel integration problem for an online retailer with a logistics delivery company when competing with a traditional retailer under different power structures in a dualchannel setting. Therefore, it relates to four streams of literature: dual-channel competition, chainto-chain competition, logistics delivery service in supply chains, and the impact of power structure in supply chains. There has been a large volume of literature on the competition between online channels and traditional channels, trying to understand the underlying mechanism when online retailing is introduced as a competitor to traditional retailing. A comprehensive review can be found in Tsay & Agrawal (2004b) and Cattani et al. (2004). Tsay & Agrawal (2004a) and Cattani et al. (2006) both demonstrate that the introduction of direct online sales by a supplier brings conflicts between dual channels. Yoo & Lee (2011) show that the impact of the internet channel introduction on channel members’ performance and consumer welfare varies considerably across channel structures. Chiang et al. (2003) find that the manufacturer’s direct online channel in relation to the traditional retailing channel would bring benefits to both firms, with the supplier having additional revenue and the retailer receiving a wholesale price reduction. David & Adida (2015) study a dual channel where a supplier operates a direct channel and sells to multiple retailers, finding that the supplier benefits from having more retailers in the market. Dumrongsiri et al. (2008) study price and service qualities-based competition in a dual channel, finding that an increase in the retailer’s service quality may increase the profit for the manufacturer. Chen et al. (2008) study a dual-channel service competition when online delivery lead time and offline store product availability are important for consumers. Other dual-channel studies focus on service competition strategy, for example, retail warranty service and value-added service in a dual-channel supply chain (Dan et al., 2012; Dan

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et al., 2018), free-riding and service-cost sharing between dual channels (Zhou et al., 2018), and retailers’ service commitment strategies, i.e., whether to provide a service guarantee (Xiao et al., 2012). In contrast to these works that all study competition and conflicts between manufacturers and retailers, we particularly introduce the delivery service company that distinguishes the online channel from the traditional channel, discuss the relationship between the online retailer and delivery company in a dual channel setting, and explore the impact of retailers’ integration with delivery companies on channel members’ performance. Our work is also relevant to chain-to-chain competition since our model assumes parallel dual channels with no manufacturers. The literature in this field discusses vertical integration in the channel and finds in some cases that decentralized chains outperform integrated chains. McGuire & Staelin (1983), in their seminal paper, model two entirely separate chains, each chain containing a manufacturer and a retailer. Their results show that it is not optimal for manufacturers to vertically integrate with retailers or for the chain to be completely coordinated. Their explanation is that the upstream manufacturer may want to use intermediaries as a buffer from a price war to obtain a higher channel profit. Coughlan (1985) generalizes the work of McGuire & Staelin (1983) to a wider set of demand functions and tests the conclusions by empirical data from the semiconductor industry. Moorthy (1988) reexamines the conditions of channel structure decision. He finds that it is not the demand substitute but the coupling between demand dependence and strategic dependence that makes decentralization occur. Choi (1991) examines the impact of demand functions and the power structure on the channel structure when a common retailer sells both manufacturers’ products. Trivedi (1998) extends exclusive retailership to include store substitutability, which represents competitiveness at the retailer level. Anderson & Bao (2010) generalize the work of McGuire & Staelin (1983) from two separate supply chains to an arbitrary number of supply chains. Yang et al. (2015) extend the work to asymmetric products that differ in substitutability and brand equity and three possible types of competition, price, quantity and price-quantity competition. Xiao & Yang (2008) study two separate supply chains’ prices and service competition with uncertain demand. In addition to downstream channel integration, Liu & Tyagi (2011) show how downstream firms can benefit from upward channel decentralization when product positioning is endogenous. All of the work in this field studies decentralization or integration decisions between manufacturers and retailers. In contrast, our work studies channel structures between the retailer and logistics delivery company that carries a part of the traditional retailer’s function. Our conclusion is similar to those of previous studies that vertical integration is not always optimal but differs in the conditions allowing for decentralization to occur because of our different settings in demand, cost structure and power structures. The conditions rely on power structures between online and offline channels, the service cost coefficient and inconvenience cost. Our paper contributes to channel

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integration literature by providing conditions for a company integrating with its service provider in a competitive environment, which is not studied in previous literature. Logistics delivery service-related operational problems have attracted much research interest (Wang et al., 2015). Liu et al. (2016a) study coordination strategy with a logistics service supply chain consisting of two functional logistics service providers and a logistics service integrator. To address delivery service delays in hot selling seasons, Liu et al. (2016b) design the retailer’s option contract to solve the logistics overloading problem, and Zhang et al. (2016) study the online retailer’s pricing strategy considering an express delivery disruption possibility during the selling season. When considering customer returns, Hua et al. (2010) design optimal shipping strategies on whether to provide a no-reason return policy and how to charge for return service, and Choi (2013) studies the optimal return service charge for the retailers who allow customers to return a fashion good for a full refund minus a service charge. The shipping fee design problem is also widely discussed because it accounts for a large part of online retailers’ operation cost, e.g., G¨ um¨ us et al. (2013), Yao & Zhang (2012), Belavina et al. (2016), Leng & Becerril-Arreola (2010). Although all of the above papers have demonstrated delivery service’s importance in online shopping, shipping companies are not modelled as decision makers and are not included in the supply chain analytical framework. Our work explicitly models the logistics delivery company for the first time in a supply chain and studies its pricing and service quality decision problems, which used to be considered retailers’ decision problems. By separating retailers and delivery companies, we provide an insight into the relationship between retailers and shipping companies and help to understand existing conflicts and coordination between them in the industry. Also of interest is research on the impact of power structure on the supply chain performance. Power is usually modelled through different timing rules with respect to the sequence of actions of supply chain members. We follow this stream and model the power between supply chain members, assuming that the first mover has more power than the second mover. Choi (1991) investigates how different power structures affect manufacturers’ decisions on selling through a retailer in a twoechelon supply chain. Here, the retailer specifies the retail price first, and then, the manufacturer determines the wholesale price. Cai (2010) studies the impact of channel structure in two singlechannel and two dual-channel supply chains. Ertek & Griffin (2002) examine the impacts of power structure on price, sensitivity of market price and profits in a two-stage supply chain. Nagarajan & Soˇsi´c (2009) study how power structures affect the alliances by comparing the results under supplier Stackelberg, Nash and assembler Stackelberg games. Shi et al. (2013) analyse how power structure and demand models affect supply chain members’ performance. They show that a firm benefits from its power only for linear but not for constant elastic expected demand. Raju & Zhang (2005) use a retailer Stackelberg game to investigate how a manufacturer can best coordinate a

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channel in the presence of a power retailer. To model different power structures between online and offline members, Chen & Wang (2015) study a pricing strategy between two channels on channel selection in the smartphone industry. Huang et al. (2016) examine the pricing competition with three power structures in a supply chain with one manufacturer and two retailers. They find that two retailer’s collusion behaviour will increase the price and reduce the quantities of product, regardless of the power structures. All of these works discuss the vertical power imbalance inside the channel instead of the horizontal imbalance between the same levels in the supply chain. In contrast, we model the power structure at the retailer level assuming two retailers are one price leader and one follower, or vice versa, in the market. We discuss how this power imbalance impacts the decision of centralization or decentralization in the online channel. In conclusion, our work differs from the above papers in that we introduce the delivery company into the game as a decision maker and model the conflicts between online retailers and express companies, while most of the previous literature focuses on the relationship between manufacturers and retailers. We analyse the online retailer’s channel structure decision while considering price competition with the traditional store in the market, assuming there are three different power structures between two retailers. We analyse the impact of the channel integrations decision on the supply chain performance and consumers’ surplus and, therefore, enrich the literature in related fields.

3.

Model

To construct the model, in the following we first introduce the channel structure in Section 3.1 and then describe the express delivery company as a special player in the game in Section 3.2. Next, we introduce the demand function based on the Hotelling model in Section 3.3 and explain its intuitive meaning in Section 3.4. Finally, we describe the game sequences under three possible power structures in Section 3.5. 3.1.

Channel structure

We investigate a channel structure consisting of an online retailer, an express service provider and an offline retailer (also referred to as a brick-and-mortar store). The channel structure is depicted in Figure 1. The online retailer and offline retailer sell products competitively to consumers with the express service provider providing logistics delivery service to consumers. In the following paper, we use the subscript e to denote the express company, and subscripts o and s to denote online and offline retailers, respectively. Without loss of generality, we follow Coughlan (1985) and Liu & Tyagi (2011) and assume that the product purchasing costs are equal to zero.

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Symbol b θ x t β ω po ps Do Ds πe πo πs π G CS

Figure 1

3.2.

Notations and variables

Description consumers’ sensitivity to the express service level the express service cost coefficient the distance from the consumer to the offline store consumers’ inconvenience cost to buy from offline store the express delivery service level the delivery service charge the price of online product the price of offline product consumers’ demand for online products consumers’ demand for offline products the express company’s profit the online retailer’s profit the offline store’s profit the sum of online retailer and express company’s profit the total money consumers paid in centralized online channel consumer surplus

Channel structure

Express delivery service

The express service company contracts with the online retailer to provide the logistics service. It determines the service price ω and service level β to deliver products to consumers. The express company has a fixed delivery service cost θβ 2 , where θ is a cost coefficient. The quadratic form of the fixed service cost is commonly used in the literature (Tsay & Agrawal, 2000; Jiang & Zhang, 2011). The cost increases with the service level very quickly. Improving the marginal service level is difficult and requires much more investment in infrastructure and manpower. 3.3.

Demand specification

To derive the demand function, we will first calculate the consumer utility and then characterize the consumer choice from buying online and offline. All consumers obtain the same value v from the product purchased either from the online retailer or the offline store. Additionally, they pay for

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either online products at price po or offline products at price ps , po > 0, ps > 0. For online products, the consumers additionally pay for the delivery service at a price ω that is greater than zero, ω > 0. The sensitivity of the consumer to the delivery service level is denoted as b, b > 0. A high delivery service level would enhance the utility the consumer experienced, which is already acknowledged by many researchers. Therefore, the consumer derives the following utility uo from buying online: uo = v − po − ω + bβ. Here, we use the Hotelling model (Hotelling, 1929) to describe the consumer’s offline purchasing cost. The location index x, representing the offline store distance from the consumer, is uniformly distributed between 0 and 1. Similar to Chen et al. (2008), the coefficient t represents the average cost of visiting the store, including the money, time and effort needed to travel to the store and bring the products home. Consumers incur an inconvenience cost tx when purchasing from the offline store. The inconvenience cost may be higher for bulky products and rural areas. The consumer with index x has the utility us from buying offline. us = v − ps − tx. Without loss of generality, we assume the aggregate demand in the market is 1. Consumers make their decisions by comparing the utilities from two channels. Let x0 denote the location index of the marginal consumer who is indifferent between purchasing from online retailers and offline stores, i.e., uo = us . Moreover, we focus attention only on markets where 0 < x0 < 1 to guarantee existence of both online and offline channels. Figure 2 illustrates how x0 divides consumers into two segments. The consumer with the index x < x0 would choose buying from an offline channel, while the consumer with index x > x0 would choose the alternative online channel. The demands for two channels are therefore calculated as follows: Do =

t + ps − po − ω + bβ , t

(1)

po + ω − ps − bβ . t

(2)

Ds =

To guarantee both of the demands are positive, i.e., Do > 0, Ds > 0, we assume that the condition t>

b2 2θ

holds in all the cases throughout the whole paper. The similar assumptions have also been

used by Kwark et al. (2017), Huang et al. (2018) and Yan et al. (2018) for ensuring non-negative order quantity/demand. To ensure the consumer’s utility is positive, we also assume these conditions hold: v − ps ≥ 0 and

uo ≥ 0. In other words, the initial value of product v is sufficiently large to cover the purchasing cost and bring consumers a positive utility. These conditions hold true throughout the entire paper.

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Figure 2

3.4.

Consumer segmentation

Intuitive observations

If we consider po + ω − bβ an effective price p˜o for purchasing products from an online channel, we can rewrite the demand function into a linear function of the prices as follows: Do = 1 + Ds =

ps − p˜o , t

p˜o − ps . t

Similarly, to ensure positive demands, we have p˜o > ps because if p˜o < ps , the consumers would never buy an offline store product because in addition to the product price ps they have to pay an extra cost to visit the store. From the above formulation, we easily see that 1/t represents the intensity of the effective price competition between two channels. A large t softens the channel competition and, hence, reduces the necessity of decentralization according to the channel integration literature. In other words, selling bulk products or selling to consumers in rural areas encourages online retailers to integrate because the store’s competitiveness is reduced. 3.5.

Power structure and game sequence

We study three possible power structures between online and offline channels: online retailer as leader, offline store as leader and equal power. The power structure is represented by the different sequences in which the online and offline prices are determined by the online retailer and brickand-mortar store, respectively. In particular, we use Stackelberg games to model three situations dominated by different online and offline retailers. In all three scenarios, once the online retailer’s price is determined, the express service price and service level is then determined by the express service provider simultaneously. Here, we assume in an online channel the express firm always follows the decision of the online retailer. Online retailers employ express firms to fulfill customer orders, and express companies usually would offer retailers a lower price than the listed service price, making the service fee a decision variable. Additionally, the simultaneous decision of the service price and service level is studied in a large number of studies in the literature.

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Therefore, the entire process could be described as a three-stage game involving three players. (1) Online Stackelberg model. In this scenario, the online retailer is dominant in the market and determines his price first. Then, the brick-and-mortar store will follow to decide on the retail price based on the online channel’s decision. Afterward, the express firm determines the service level and service price. (2) Nash model. In this scenario, the online and offline retailer have the same power and decide on the retail price simultaneously. Then, the express firm determines the service level and service price. (3) Offline Stackelberg model. In this scenario, the brick-and-mortar store moves first as the Stackelberg leader setting the retail price. Next, the online retailer determines the price. Then, the express firm determines the service level and service price. We summarize the notations for ease of reference in Table 1. The game sequence is similar to the M2 structure in McGuire & Staelin (1983), which is a mixed structure with one supply chain integrated and one supply chain decentralized. In that structure, two manufacturers first compete with each other by determining the wholesale price and then the retailer determines the retail price conditional on those manufactures’ actions. In McGuire & Staelin (1983)’s work, manufacturers hold equal power, while in our work, we consider power imbalance between two retailers and, therefore, derive three power structures and three game sequences.

4.

Equilibrium results

In the following section, we characterize the equilibria of the three games under decentralized and centralized decision modes in the online channel, which consists of one online retailer and one express company. By comparing the results, we can explore the conditions encouraging or discouraging online channel integration, i.e., the merge or alliance between online retailers and express firms. In the following paper, we use superscript OD to represent the decentralized case in an online retailer Stackelberg game and OC for the centralized case in an online retailer Stackelberg game. In the same way, the superscript N D is for the decentralized case in a Nash game, N C for the centralized case in a Nash game, SD for the decentralized case in an offline store Stackelberg game, and SC for the centralized case in an offline store Stackelberg game. 4.1.

Online Stackelberg game

In this section, we examine the first power structure scenario: online retailers as the leader. In the era of e-commerce, it is rather common that online retailers such as JD.com or Amazon hold more market power than small offline stores and take the move first. Those offline stores decide their retailer prices after observing online retailers’ actions.

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4.1.1.

Decentralized online channel (OD)

In a decentralized online Stackelberg game, the game sequence is a three-stage game as described in Section 3. The online retailer determines his price first. Then, the brick-and-mortar store determines the price based on the online channel’s price. The express company follows to decide on the service level and service price. We solve the problem by backward induction. We start by solving the express firm’s problem. The express firm determines the service price as a solution to

t + ps − po − ω + bβ − θβ 2 . (3) ω,β t The express company’s profit function is a strictly joint concave function with respect to decision max

πe = ω

variables β and ω because the Hessian matrix is negative definite under the condition t > −2θ b/t > 0. |H | = b/t −2/t

b2 : 2θ

Equating the first-order expressions to zero and solving them, we have optimal results as follows:

ω=

2θt(t+ps −po ) , 4θt−b2

β=

b(t+ps −po ) . 4θt−b2

Substituting the result into the brick-and-mortar store’s profit function, which is the offline demand multiplying the store’s price, we have: πs = p s

max ps

po + ω − ps − bβ . t

(4)

Using the first-order condition with respect to ps , the store’s optimal price is ps =

2θt−b2 4θ

+ p20 .

Finally, we solve the online retailer’s problem, maximizing its concave profit function: πo = po

max po

t + ps − po − ω + bβ . t

Using the first-order condition again, we have po =

6θt−b2 . 4θ

Therefore, ps =

(5) 10θt−3b2 . 8θ

All of the final

results are listed in Table 2. 4.1.2.

Centralized online channel (OC)

As a benchmark, we assume the online retailer and the express service firm are one centralized decision maker. Then, the game is simplified to a two-stage Stackelberg game. The online channel first determines the service level, product price and service price to maximize its profit. Then, the offline store decides on the retail price. We solve the problem by backward induction. At this time, consumers pay the online channel for purchasing the product and the delivery service. The sum of the online product price and service price is represented by G, i.e., G = po + ω. In addition, we also assume π = πe + πo . The offline store’s decision problem is now max ps

πs = p s

G − ps − bβ , t

(6)

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and the solution is ps =

G−bβ . 2

The online channel decides the service level, product price and service price as solutions to the following problem:

t + ps − G + bβ − θβ 2 . (7) t It is a jointly concave function for β and G. Replacing the above result of ps into the profit function max

π=G

β,G

and taking derivatives with respect to β and G and making them equal to zero, we have β = G = t + bβ . Therefore, G = 2

t , 1−b2 /8tθ

β=

2bt , 8tθ−b2

ps =

2

4tθ−b t. 8tθ−b2

bG , 4tθ

See all of the results in Table 2.

4.2. Nash mode 4.2.1. Decentralized online channel (ND) In this scenario, we assume there is no dominant power existing in the market. The online retailer and the offline store have equal power. The game sequence is a two-stage game as described in Section 3. The retailer and store have a Nash game between each other on the retail price. Afterward, the express firm determines the service price and service level. We examine the express company’s decision problem first. Its problem is as follows: max ω,β

πe = ω

t + ps − po − ω + bβ − θβ 2 , t

(8)

which is the same as problem (3). As such, we omit the analysis and use the optimal solution derived from (3) directly. Then, we solve the Nash game between the online retailer and the brick-and-mortar store. Each one wants to maximize its profit as follows: t + ps − po − ω + bβ , (9) t po − ps + ω − bβ max πs = ps . (10) ps t It is obvious that the two profit functions, πo and πs , are concave in ps and po . Therefore, using max po

πo = po

the reactive functions of each player, the results are shown as follows: po = β=

b(6θt−b2 ) , 6θ(4θt−b2 )

4.2.2.

ω=

6θt−b2 , 6θ

ps =

3θt−b2 . 3θ

t(6θt−b2 ) . 3(4θt−b2 )

Centralized online channel (NC)

In the centralized online channel, the game is reduced to a one-stage two-player Nash game. The online channel and offline store make their decisions simultaneously as solutions to t + ps − G + bβ − θβ 2 , (11) β,G t G − ps − bβ max πs = ps . (12) ps t Note that the profit function π is jointly concave in G and β when b2 < 2θt, and πs is concave in max

π=G

ps . The Nash equilibrium can be derived from the first-order conditions. The equilibrium results are shown as follows: ps =

(2θt2 −b2 )t , 6θt−b2

G=

4θt2 , 6θt−b2

β=

2bt . 6θt−b2

16

4.3. Offline store Stackelberg game 4.3.1. Decentralized online channel (SD) In addition to the online retailer Stackelberg game, there is also an opposite situation in which some traditional retailers such as Walmart and TESCO dominate the market. In such a scenario, the online retailer has less power than the offline store and will determine the price after the offline store’s decision. The game sequence is a three-stage game as described in Section 3. The express company decides the service price and level to maximize its profit: max Its optimal solution is ω =

t + ps − po − ω + bβ − θβ 2 . t

πe = ω

ω,β

2θt(t+ps −po ) , 4θt−b2

β=

b(t+ps −po ) , 4θt−b2

(13)

with the condition b2 < 4θt2 .

Then, the online retailer decides on the retail price, max

πo = p o

po

t + ps − po − ω + bβ . t

(14)

The optimal po is po = (t + ps )/2. Then, the store decide on its price. Substituting all previous results into the profit function: πs = p s

max ps

po + ω − ps − bβ , t

and using the first-order condition, we have ps = All of the results are shown as follows: po = 4.3.2.

(15)

3θt−b2 . 2θ

5θt−b2 , 4θ

ps =

3θt−b2 , 2θ

β=

b(5θt−b2 ) , 4θ(4θt−b2 )

ω=

t(5θt−b2 ) . 2(4θt−b2 )

Centralized online channel (SC)

In a centralized scenario, the online retailer and express company are one decision maker. The game is a two-stage Stackelberg game between the online channel and offline store. The store decides the retail price first, and then, the online channel decides on the product price, service fee and service level. We solve the online channel’s problem first as follows: max β,G

π=G

t + ps − G + bβ − θβ 2 . t

By the profit function’s concavity and first-order conditions, we have G =

(16) 2θt(t+ps ) , 4θt−b2

β=

b(t+ps ) . 4θt−b2

Then, solving the offline store’s problem: max

πs = p s

ps

The optimal solution is ps =

2θt−b2 , 4θ

G=

t(6θt−b2 ) , 2(4θt−b2 )

All of the results are shown in Table 2.

G − ps − bβ . t ps =

2θt−b2 , 4θ

β=

(17) b(6θt−b2 ) . 4θ(4θt−b2 )

17 Table 2

Equilibrium results under three power structures

Symbol

OD

OC

ND

NC

ω

(6θt−b2 )t 4(4θt−b2 )

β

b(6θt−b2 ) 8θ(4θt−b2 )

po

6θt−b2 4θ

ps

10θt−3b2 8θ

(4θt−b2 )t 8θt−b2

3θt−b2 3θ

(2θt−b2 )t 6θt−b2

3θt−b2 2θ

2θt−b2 4θ

G

(5θt−b2 )(6θt−b2 ) 4θ(4θt−b2 )

8θt2 8θt−b2

(6θt−b2 )2 6θ(4θt−b2 )

4θt2 6θt−b2

(5θt−b2 )(6θt−b2 ) 4θ(4θt−b2 )

(6θt−b2 )t 2(4θt−b2 )

Do

6θt−b2 4(4θt−b2 )

4θt 8θt−b2

6θt−b2 3(4θt−b2 )

4θt 6θt−b2

5θt−b2 2(4θt−b2 )

6θt−b2 2(4θt−b2 )

Ds

10θt−3b2 4(4θt−b2 )

4θt−b2 8θt−b2

2(3θt−b2 ) 3(4θt−b2 )

2θt−b2 6θt−b2

3θt−b2 2(4θt−b2 )

2θt−b2 2(4θt−b2 )

πo

(6θt−b2 )2 16θ(4θt−b2 )

πs

(10θt−3b2 )2 32θ(4θt−b2 )

πe

(6θt−b2 )2 64θ(4θt−b2 )

π

5(6θt−b2 )2 64θ(4θt−b2 )

4θt2 8θt−b2

(6θt−b2 )2 12θ(4θt−b2 )

4θt2 (4θt−b2 ) (6θt−b2 )2

3(5θt−b2 )2 16θ(4θt−b2 )

(6θt−b2 )2 16θ(4θt−b2 )

CS

(68θ 2 t2 −36θtb2 +5b4 )t 16(4θt−b2 )2

(32θ 2 t2 −8θtb2 +b4 )t 2(8θt−b2 )2

(72θ 2 t2 −36θtb2 +5b4 )t 18(4θt−b2 )2

(20θ 2 t2 −4θtb2 +b4 )t 2(6θt−b2 )2

(17θ 2 t2 −8θtb2 +b4 )t 4(4θt−b2 )2

(20θ 2 t2 −8θtb2 +b4 )t 4(4θt−b2 )2

(6θt−b2 )t 3(4θt−b2 )

2bt 6θt−b2

6θt−b2 6θ

b(5θt−b2 ) 4θ(4θt−b2 )

b(6θt−b2 ) 4θ(4θt−b2 )

5θt−b2 4θ

(6θt−b2 )2 18θ(4θt−b2 ) (4θt−b2 )2 t (8θt−b2 )2

SC

(5θt−b2 )t 2(4θt−b2 )

b(6θt−b2 ) 6θ(4θt−b2 )

2bt 8θt−b2

SD

(5θt−b2 )2 8θ(4θt−b2 )

2(3θt−b2 )2 9θ(4θt−b2 )

(2θt−b2 )2 t (6θt−b2 )2

(6θt−b2 )2 36θ(4θt−b2 )

(3θt−b2 )2 4θ(4θt−b2 )

(2θt−b2 )2 8θ(4θt−b2 )

(5θt−b2 )2 16θ(4θt−b2 )

Note, we use “OD” to represent the decentralized online Stackelberg game, “ND” for the decentralized Nash game, and “SD” for the decentralized offline store Stackelberg game; “OC” for the centralized case in online Stackelberg game, “NC” for the centralized Nash game,“SC” for the centralized offline store Stackelberg game.

5.

Comparison and analysis

In this section, we analyse the optimal solutions derived in the previous models presented in Table 2. By comparing six optimal solutions under three power structures and two decision modes, we have the following results. 5.1.

Effect of offline inconvenient cost

When online retailers compete with traditional retailers, the consumer’s inconvenience cost to the brick-and-mortar store has great impacts. We explore how the offline inconvenience cost affects the equilibrium behaviours of game players. Proposition 1 In each scenario, 1. service level β decreases with inconvenience cost t. 2. service charge ω increases with inconvenience cost t. 3. the demand for online product Do decreases in consumers’ inconvenience cost t, and the demand for offline product Ds increases with inconvenience cost t.

18

4. the retail prices in both channels po , ps increase with inconvenience cost t. Figures 3-5 provide visual descriptions for this proposition. It is drawn with the parameters θ = 0.2 and b = 0.1. The horizontal axis starts from 0.025 because of the condition t > b2 /2θ. It is straightforward that a large inconvenience cost coefficient to brick-and-mortar stores raises consumers’ purchasing cost and therefore undermines the store’s competitiveness. Intuitively, this leads to lower demand for the offline store’s products. However, in contrast, we could observe from the results that the demand for offline products Ds increases with t, and the demand for online products Do decreases with t as shown in Proposition 1 (3). It could be explained by the following facts. After observing the increasing inconvenience cost, the competitive online channel could then take the advantage to relax the requirement for the delivery service level β and raise the service charge ω and product price po , as shown in Proposition 1 (4). Therefore, it follows that the online shopping cost for consumers rises and finally reduces the online demand D0 . It is an equilibrium outcome. The conclusions are consistent with real practice. In remote areas with inconvenient transportation such as in western China, consumers need to pay considerable time and effort to go to physical stores. In these areas, the delivery fee is usually relatively high, and the service level is quite low compared with eastern China. Rural consumers are more willing to buy from brick-and-mortar stores and more reluctant to buy online products than urban consumers. The following data from the industry provide evidence. The express industry survey in 2017 by State Post of Bureau of China (2018) shows that average delivery time needed for eastern areas is 56.60 hours, while the time for western areas increases to 62.35 hours. A report from Xingye Securities Transportation and Logistics Institute (2018) shows that in 2017, the average standard delivery service charge in western areas is 14.6 RMB, which is higher than the 13.3 RMB charged in eastern areas of China. For example, the average standard price in Xi’an is 12.18 RMB, and in Nanning, it is 14.8 RMB, while the price in Guangzhou is 9.75 RMB, and in Hangzhou, it is 10.79 RMB. Therefore, it is not surprising to see low demand for online products and related express service in rural areas. According to State Post of Bureau of China (2018), from January to July 2018, the express delivery business volumes of the eastern, middle and western areas of China are 80.3%, 12% and 7.7%, respectively, demonstrating lower demand in rural areas of China. 5.2.

Effect of online channel integration

We compare the results before and after integration to study its impact on the online channel, offline channel and consumers. The comparison results are summarized in the following propositions.

19

Β Ω 0.25 0.15 SC

0.20 NC

0.10 SD

ND

0.15

ND

OD

0.05

0.10

OD

0.05

0.00 0.025

SD

OC

0.05

0.10

0.15

0.20

0.25

t

0.025

0.05

0.10

0.15

0.20

0.25

t

(a) service charges increase with t (b) service levels decrease with t Figure 3 Service charge and level’s relationship with t

Ds 0.6

Do

OD

1.0 ND

0.5 OC 0.8

SC

0.4

SD

NC 0.6

SD

OC

OD

0.1

0.2 0.025

0.05

SC

0.2

ND 0.4

NC

0.3

0.10

0.15

0.20

0.25

t

0.0 0.025

0.05

0.10

0.15

0.20

0.25

t

(a) demands for online products decrease with t (b) demands for offline products increase with t Figure 4 Online and offline demands display opposite trends with t

ps 0.35

po 0.35

0.30

0.30

0.25

0.25

OD

SD

0.20 SD

OD

0.20

0.15

ND

0.15 ND

0.10

0.10

0.05

0.05 0.00 0.025

OC

0.05

0.10

0.15

0.20

0.25

t

0.00 0.025

SC

NC 0.05

0.10

0.15

0.20

0.25

t

(a) online product prices linearly increase with t (b) online product prices linearly increase with t Figure 5 Online and offline prices increase with t

Proposition 2 In each scenario, online channel integration always raises service level and demand for the online channel and reduces the total money paid by consumers for online products, i.e., GOD > GOC , GN D > GN C , GSD > GSC , β OD < β OC , β N D < β N C , β SD < β SC , DoOD < DoOC , DoN D < DoN C , DoSD < DoSC .

20

As shown in Figure 6, the money G in the decentralized case is always higher than that in centralized case. Figure 3b exhibits a similar result for the service level, which is better in the centralized case than in the decentralized case. The online channel exhibits the double marginalization phenomenon. Different objectives create conflicts between retailers and express companies. Retailers expect a high service level and low price, while the express companies are not willing to improve the service level and try to raise the price. Since service cost is usually quite heavy in maintaining daily delivery network operations, the express company is reluctant to improve service level because the retailer would benefit from high service level but is not willing to share the service cost. Even the delivery giant, FedEx, complains about the investment burden in expanding the service capacity. FedEx executives stated in a report from Wall Street Journal (Stevens, 2016) that retailers should be paying more for shipments to help offset the cost of expanding its network to meet the growing demands of e-commerce. After integration, retailers and express companies act as one decision maker to optimize the service level and the total price as a whole. At that time, they are willing to improve the service level and reduce the total money to attract consumers for more profit, producing the results in the above proposition. Proposition 3 In each scenario, online channel integration always hurts brick-and-mortar stores, OC < DsOD , πsOC < πsOD , < pOD resulting in decreased retail price, demands and profit, i.e., pOC s , Ds s SC SD D C < DsSD , πsSC < πsSD . , DsN C < DsN D , πsN C < πsN D , pSC < pN pN s < ps , Ds s s

Figure 7 demonstrates that the brick-and-mortar store always loses profit after online channel centralization. Figure 5b and Figure 4b also confirm the conclusions in the proposition about the offline store’s prices and demands. It is understandable that after online channel integration, the store’s price and profit decrease because its competitor online channel now makes decisions as a whole, which would surely put the offline store in a worse position. As stated in Proposition 2, consumers obtain the lower total price and higher service level from the online channel. These two effects from the price and service level together make consumers switch to buy online products and therefore lower the demand for offline products. To compete with the online channel, the offline store also has to lower his price and finally receives lower profit than before. Combining the above two propositions, we can see that the introduction of online channel centralization always leads to better service, lower total price, and lower profit for the offline store. Then, if we ask whether integration is always beneficial for the online channel and consumers, the answer is no. It mainly depends on the power structure. We leave this area to discussions in the following section.

21 G

0.4

0.3 ND SD=OD 0.2

OC SC

0.1

NC

0.0 0.025

0.05

Figure 6

5.3.

0.10

0.15

0.20

0.25

t

Effect of t on the total money G

Effect of power structure

In this section, we compare the results before and after integration to study the impact of integration on the online channel’s profit and consumers’ surplus. Note that the consumer surplus (denoted as CS hereafter) can be computed by substituting the optimal solutions into the integration based on the linear inverse demand functions in Equation (1) and Equation (2). Particularly, we find that integration does not always lead to better performance as our intuition tells us. In a single online channel of one online retailer and one express company, a centralized decision achieves better performance for the system. However, when strategic interaction with the other channel is added into the framework, the conclusion is uncertain. In most cases, retailers prefer using outside third-party logistics companies as intermediaries to avoid carrying heavy service operations costs. Only when the retailer has market dominance and could obtain sufficient profit is it optimal for him to integrate the logistics company, raise the service level and win more demands and profit. To identify the impact of integration on the online channel’s total profit, we introduce the ratio C

π πD

as the ratio of profits of the online channel under centralized and decentralized cases. Analysing

the upper bound of the ratio, we find the following facts. Proposition 4 In each scenario, the ratio of profits always decreases with t. In different power structures, it has different bounds, i.e., relationship

SC

π π SD

<

NC

π πN D

<

OC

π π OD

32 45

<

π OC π OD

<

16 , 48 15 81

<

πN C πN D

< 34 ,

12 25

<

π SC π SD

<

16 , 27

and the

holds.

Figure 8 demonstrates that the ratios in three power structures all decrease with t, indicating that the large offline inconvenience cost makes the centralization less beneficial. It is noticeable that the upper bounds in the Nash game and offline store Stackelberg game are both smaller than 1, indicating centralization brings lower profit and harms the whole online channel. In addition, the ratio is quite low. As shown in the proposition, the upper bounds are only 0.75 and approximately 0.69 in Nash game and offline Stackelberg game, respectively. In either

22 Πs

0.15 OD 0.10 SD ND SC

0.05 OC

NC

0.00 0.025

Figure 7

0.05

0.10

0.15

0.20

0.25

t

The store loses profit after online channel centralization

case, 25 percent and more than 30 percent of the channel’s profit will be lost after centralization. Although, traditionally, a centralized supply chain will improve profit for making decisions as one entity, centralization brings lower profit for the whole online channel. Only in the online Stackelberg game, where the online retailer dominates the market, is there a possibility that the ratio is larger than 1. We have the following result. Proposition 5 Only in the online Stackelberg game and when the consumer’s inconvenience cost t is moderate, i.e.,

b2 2θ

< t < t0 , is the online channel integration beneficial for the online retailer

and express company.

( π OC > π OD , π OC ≤ π OD ,

b2 2θ

< t < t0 , t ≥ t0 .

Here, t0 is defined as the solution that makes the ratio

π OC π OD

(18) equal to 1, t0 =

of T is very complicated, we describe it in the appendix. The range between

T b2 . 312θ 2

b 2θ

Since the form

< t < t0 represents

the possible opportunity for improving the online channel’s profit. This proposition explains why, in reality, we see very few cases of vertical integration between online retailers and express companies. Most of the online retailers choose one or multiple thirdparty logistics companies to deliver products to consumers rather than integrating with express companies. As shown in the proposition, the centralization is beneficial for both parties only under strict conditions. In reality, only giant online retailers such as JD.com, Suning, and Amazon have the power to carry out the strategic action of merging with express companies. Additionally, they also should carefully choose the region of the delivery business. JD.com carries out its own delivery business only in large urban cities, while in rural areas, it uses other third-party logistics companies to perform this function. If one considers the viewpoint of consumers, online channel centralization is also not always good for consumers. Recalling Proposition 2, lower total price and higher service level after centralization are favorable outcomes for consumers. However, this result does not hold when we look closely at

23 C

Π Π

D

16 15

1.0

0.9

0.8

Online

3 4

0.7 Nash 0.6

16 27

Offline

0.5 0.4 0.025

Figure 8

0.05

0.10

0.15

0.20

0.25

0.30

t

The ratios decrease with t with different upper bounds

consumers’ surplus. For the impact on consumer surplus, we compare the results with and without centralization and find the following results. Proposition 6 In the Nash and offline store Stackelberg game, consumers are always better off after integration, i.e., CS N D < CS N C , CS SD < CS SC , while in the online retailer Stackelberg game, it depends on t. When t is moderate, consumers are better off after integration. When t is large, consumers are worse off after integration, i.e., ( CS OD < CS OC , CS OD ≥ CS OC ,

b2 2θ

√ (9+ 33)b2 , 16θ √ (9+ 33)b2 . 16θ

≤t<

t≥

(19)

This proposition reveals that, in contrast to the offline store’s situation, in most cases, consumers benefit from integration. Only when the online retailer dominates the market and the inconvenience cost t is relatively large are consumers worse off. The centralized online channel can directly control output by adjusting the price and service level at the same time. It leads to a lowered total price and improved service level, which is beneficial for the consumers. However, in areas with a large inconvenience cost, the service level is low and service charge is high, and retail prices in online and offline channels both are high, according to Proposition 1. Particularly in the online Stackelberg game, the service level is the lowest in all six scenarios; see Figure 3b. Additionally, the total price G is the second largest in all six scenarios; see Figure 6. Therefore, consumers’ utility is actually quite low in purchasing the products. In summary, combining the two effects of centralization’s impacts and the large inconvenience cost t’s impact, consumers’ surplus exhibits the results from the above proposition. 5.4.

Discussion on channel integration conditions

Now, we explain why in our framework, decentralization can be beneficial to an online retailer in most cases, and integration only occurs under some strict conditions. It relies on strategic

24

interactions between channels because if there is no strategic interaction between channels, then centralization must be preferred by retailers in the vertical online channel. McGuire & Staelin (1983) think this strategic interaction comes from demand substitutability or competition intensity. A manufacturer benefits from downstream decentralization if the degree of product substitutability is high. Later, Moorthy (1988) demonstrates that what is important for decentralization to occur is not how substitutable the products are, but the coupling between demand dependence and strategic dependence. Generally, decentralization brings two effects: the direct negative effect and the indirect positive effect. The direct negative effect is the double-marginalization effect leading to a higher price and lower demand for the decentralized channel, and the indirect beneficial effect concerns inducing a rising equilibrium price of the competitive channel. If there is no indirect effect, when the online retailer decentralizes, the only effect is to raise his express delivery company’s marginal cost, inducing a high total cost and a lower service level and reducing channel profits as a consequence. With the indirect effect, however, decentralization can raise the competitive store’s equilibrium price, and that can have a beneficial effect of raising the online retailer’s demand. Therefore, it is identified as a necessary condition for a preferred decentralization. Although our framework is different than McGuire & Staelin (1983)’s model in a specific setting, it satisfies the necessary condition. As shown in Proposition 3, decentralization induces a rising price in the competitive offline store in each power structure scenario. The final result depends on whether the indirect positive effect dominates the direct negative effect. In our model, the direct effect relates to cost structure. The online channel has a quadratic service cost structure. A large θ indicates a large amount of operating cost rising very quickly with the service level. When decentralized, the service provider would raise the service price and lower the service quality to cover the cost, enhancing the direct negative effect. Therefore, a large θ prevents the retailer from integrating the channel. Using an independent express company to avoid the risk of carrying a network with a financial and management burden is a good choice for the retailer. The indirect effect relates to the power structure and inconvenience coefficient. The power structure has the most important influence. In the offline Stackelberg game and Nash game, the store can decide first or at the same time to control its price to the best outcome, expecting perfectly the reaction of the online retailer. The store’s equilibrium price rises most in the offline Stackelberg game as shown in Figure 5b. Therefore, the indirect preferable effect is increased greatly. This effect from market dominance is so strong that in these two games, the online retailer always considers decentralization, while in the online Stackelberg game, the retailer has the market power to make the price decision first and wins a large part of profit. Figure 7 shows the store’s profit losses most in the online Stackelberg game. The indirect effect is reduced. This provides a possibility to

25 0.25

0.20

J9+

t

t= 0.15

I

t = t0

t= 0.10

0.05

33 N b2 16 Θ

0.02

b2 2Θ

0.04

0.06

0.08

0.10

Θ

Figure 9

Preferred strategies of online channel in online Stackelberg game

allow for the direct effect to outperform the indirect effect. The final result depends on the mixed effect from inconvenience cost t for the indirect effect and from the cost coefficient θ for the direct effect. The inconvenience cost, as stated in Section 3.4, represents the effective price competition intensity. A large t softens price competition and reduces the necessity of decentralization. When θt satisfies the condition in Proposition 5, i.e,

b2 2

< θt <

T b2 , 312

the channel integration strategy will be

adopted. When t is fixed, improving delivery service management level and reducing θ encourages the retailer to integrate. When θ is fixed, moving the business area from rural areas to urban areas, changing to light products, or reducing coefficient t also encourages integration. To provide a more intuitive presentation, the previous equilibrium results of retailers and consumers in the online Stackelberg game are illustrated in Figure 9. As shown in the figure, the feasible region above the curve t =

b2 2θ

is divided into three regions (denoted by “I”, “II” and “III”

). Region I, the area above the curve t =

√ (9+ 33)b2 , 16θ

represents a lose-lose situation for all the mem-

bers, either retailers or consumers, in the online channel integration. In this area endowed with large θt, the online retailer prefers not to introduce integration. In Region II, the area between the curves t =

√ (9+ 33)b2 16θ

and t = t0 , consumers are better off,

while two retailers are both worse off after centralization. Therefore, region I together with region II represent a large area where there is no motivation for the online retailer to adopt the vertical integration strategy. Region III, the area between t = t0 and t =

b2 , 2θ

is the only area providing the opportunity for

implementing the vertical integration strategy. In this area, the online channel’s profit increases and consumers are better off after integration, while the offline store is worse off after integration. In this area, two parameters, t and θ, are relatively moderate at the middle, or one is high and

26

another is small at either end. Additionally, particularly, this area’s bound is limited by consumers’ service sensitivity b. When b rises, consumers are more sensitive to service; then, the bound of θt increases and the area for integration enlarges. This may reflect the current situation in ecommerce: customers are more demanding than before on the delivery speed and service quality, which provides more space for those giant online retailers to employ channel integration. In summary, vertical integration in the online channel is always beneficial for consumers but harmful for both channels in either the Nash game or offline Stackelberg game. In the online Stackelberg game, it depends on the mixed effect from the inconvenience cost t and service cost coefficient θ. In the high θt area, vertical integration is not preferable for each party in the game, producing a lose-lose situation. Only in the moderate θt area does vertical integration benefit the online channel but hurt the offline store and consumers. In that area, the online retailer has the motivation to introduce vertical integration.

6.

Extensions

In the basic model, we assume that the online retailer only operates the online channel and competes with the brick-and-mortar store. In this section, we extend the model setting to consider that an online retailer owning a mixed channel (i.e., both the online channel and the offline channel) competes with either a brick-and-mortar store or an online retailer, resulted in two cases (examined in Sections 6.1 and 6.2, respectively). We find that in such two cases, the retailer with the mixed channel has more incentive to integrate than with only the online channel. The preferred strategies for the basic and extended models are compared and summarized in Table 3. All the analyses are provided in the technical appendix.

Table 3

Summary of integration strategies in the base model and extensions

Scenarios

Online Stackelberg game / The first retailer Stackelberg game

Nash game

Offline Stackelberg game / The second retailer Stackelberg game

The base model

integration in Region III

decentralization

decentralization

Competition with a to ≥ ts brick-and-mortar store to < ts

integration in Region III

decentralization

decentralization

no difference between integration and decentralization

no difference between integration and decentralization

no difference between integration and decentralization

Competition with an online retailer

integration

integration

integration

27

6.1.

Competition with a brick-and-mortar store

We use the subscript oo and os to represent the online channel and offline channel of the online retailer. The online retailer charges the same price in both channels. The power structure and the game sequence are set the same as in the base model. We allow the online retailer’s offline channel to have different consumers’ inconvenience cost from the store, denoted as to . The consumer obtains the utility uos = v − po − to x when buying from the offline channel of the

online retailer, the utility uoo = v − po − ω + bβ from the online channel of the online retailer, and the utility us = v − ps − ts x from the brick-and-mortar store. To derive the demands, we need to compare

consumers’ utilities between uos and us . Thus, we discuss three cases: to > ts , to < ts , to = ts , which represent that the offline channel of the online retailer is less competitive than, more competitive than, or equal with the brick-and-mortar store, respectively. (1) to > ts or to = ts Under this circumstance, in the online Stackelberg game and Nash game, the online retailer quits the offline market and earns the profit solely from his online channel. With only one online channel and one offline store in the market, the equilibrium results and the integration strategy are the same as we have discussed in the base model. In the offline Stackelberg game, when ts ≤

b2 , θ

which indicates that the competitor has a large

advantage in the offline market, the online retailer quits the offline channel. When ts >

b2 θ

and

to > ts , the online retailer stays in both online and offline markets. But in either case, the retailer prefers not to integrate with the express service provider. This strategy is also the same as in the base model. In short, when the online retailer is in a weak or equal position in the offline channel, the integration strategy remains the same as in the base model. That is, in the offline Stackelberg game and Nash game the online retailer prefers decentralization over integration. The online retailer is willing to integrate only in Region III and only in the online Stackelberg game. (2) to < ts When the online retailer has an offline channel advantage over the competitive store, the online retailer stays in both online and offline markets and he makes no difference between integration and decentralization in three games. Recall that when the online retailer has one single online channel, he has very limited opportunities to integrate with the express service provider, only in Region III and only in the online Stackelberg game. Now the advantage in the offline channel brings the online retailer additional opportunities to consider integration as a choice in all three games. Our explanation is that the advantageous offline channel strengthens the online retailer’s market power besides his power in decision sequence and therefore encourages integration. As we discussed before in the base model, among three factors, market power is most important because it promises

28

a large market share to obtain sufficient profit and cover the logistics service cost. That’s why integration is not preferred in the offline Stackelberg game and Nash game, but adopted in the online Stackelberg game. Now a strong offline channel helps the online retailer to gain a portion of the offline market shares and capture more profit through two channels. Therefore, a strong offline channel relaxes the strict condition in the base model and allows the online retailer to integrate in all three games. This may explain why giant supermarkets such as Target, Walmart and Suning is willing to purchase express service providers for their online business. To conclude, when competing with a brick-and-mortar store, the online retailer either keeps the same integration strategy as in the base model (with a weak or equal offline channel) or can consider integration in three games (with a strong offline channel). In general, the introduction of the mixed channel improves the online retailer’s power in the game and has a beneficial effect on the channel profitability and integration choice. 6.2.

Competition with an online retailer

Here, we refer the retailer with a mixed channel as the first online retailer and the competitor as the second online retailer. To facilitate the analysis, we assume the second online retailer uses an express service provider with an exogenous service fee ω2 and service level β2 . The consumer has the utility uos = v − po − to x when buying from the offline channel of the first

online retailer, the utility uoo = v − po − ω + bβ from the online channel of the first online retailer, and the utility uo2 = v − po2 − σ from the second online retailer. Here we let σ = ω2 − bβ2 to simply the notation.

The competition between two online channels is in nature a price war, leading to an all-win or all-lose outcome. In this price war, the second retailer always loses because he has a fixed service level and service charge, while the first retailer together with the express company can change the price, service level and service charge. Since in the equilibrium the market is totally owned by the first retailer, we can assert that the first retailer is always willing to integrate due to the double marginalization effect. In three decentralized games, the results turn out to be all the same. That is, the first retailer gives up the offline channel and stays in the online market, while the second retailer loses and quits. In centralized games, the first retailer wins both offline and online channels in the Nash game and the second retailer Stackelberg game. In the first retailer Stackelberg game, the first retailer’s profit is guaranteed by the strategy of staying in both channels. By comparing the results in centralized and decentralized games, we demonstrate that the first online retailer is always willing to integrate with the express service provider. Notice that the first retailer obtains more profit from both channels in the centralized games than from the single online channel in the decentralized games where the offline channel generates zero profit, and we are safe to say that the mixed channel contributes to integration.

29

7.

Concluding remarks

This paper investigates the strategic vertical integration decision for the online retailer with the express company when competing with traditional brick-and-mortar stores. Usually, people focus on the cost benefits when outsourcing to a lower-cost outside service provider. We show that even without an efficiency advantage for third-party service providers, channel decentralization is still preferred in most situations. Integration needs strict conditions. Inappropriate vertical integration could lead to lose-lose undesirable results for all members in the online retailing industry. We first set up a consumer choice model between the online and offline channel based on the Hotelling model and then study the games between the online channel and offline channel. We consider three different power structures between online and offline retailers and two decision modes: decentralized and centralized modes. Comparing the equilibrium results between six scenarios, we analyse the impact of integration on channel members as well as consumers. Online channel integration will hurt offline stores at all times and benefit consumers in most cases. We identify three important factors: market power, inconvenience cost and service cost coefficient for the integration condition. Market power has the prevailing influence. When the online retailer does not have the first-move advantage in the market, he would do better not to integrate. Only when the online retailer has dominant market power, integration will probably improve the online channel’s profit. To make integration preferable, a moderate inconvenience cost and service cost coefficient is required. The consumers’ sensitivity to service determines the upper bound of the inconvenience cost and service cost coefficient. If customers are more demanding on the delivery speed and service quality, there will be more spaces for giant online retailers to carry out a channel integration strategy, which may explain why only recently have online retailers begun to consider integration. In the equilibrium results, the inconvenience cost and service cost coefficient are two important parameters. A high inconvenience cost indicates either bulky products or selling in a rural area market. We discover that the inconvenience cost’s impact could well explain some facts in reality. For instance, in the remote area with inconvenient transportation, consumers are more inclined to buy from offline stores than in urban areas, and the delivery fee is relatively high and service level is quite low compared with urban areas. Finally, we extend our basic model to consider an online retailer with a mixed channel competing with either a brick-and-mortar store or an online retailer. We find that in either case, the online retailer with the mixed channel has more incentive to integrate than with only the online channel because the mixed channel enhances the retailer’s market power and promises more market shares and profit to cover the logistics cost. This again proves that market power has the prevailing influence.

30

Acknowledgments The authors gratefully acknowledge the support from the Natural Science Foundation of China (NSFC 71532015, 71771179, 91746110), Shanghai Pujiang Program (17PJC099). Guo Li is the corresponding author.

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