Strategic dual-channel pricing games with e-retailer finance

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Strategic Dual-channel Pricing Games with E-retailer Finance Nina Yan , Yang Liu , Xun Xu , Xiuli He PII: DOI: Reference:

S0377-2217(19)30898-7 https://doi.org/10.1016/j.ejor.2019.10.046 EOR 16132

To appear in:

European Journal of Operational Research

Received date: Accepted date:

23 April 2019 31 October 2019

Please cite this article as: Nina Yan , Yang Liu , Xun Xu , Xiuli He , Strategic Dual-channel Pricing Games with E-retailer Finance, European Journal of Operational Research (2019), doi: https://doi.org/10.1016/j.ejor.2019.10.046

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Highlights



We examine E-commerce platform role in online finance and distribution.



We analyze the dual-channel structure and pricing in e-retailer finance.

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We compare pricing competition in horizontal and vertical games.

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Offering e-retailer finance to capital-constrained supplier adds value.

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Participating in vertical competition has the first-mover advantage.

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Strategic Dual-channel Pricing Games with E-retailer Finance Nina Yan1,Yang Liu1, Xun Xu2, Xiuli He3* 1 Business School, Central University of Finance and Economics, China 2 College of Business Administration, California State University, Stanislaus, United States 3 Belk College of Business, University of North Carolina at Charlotte, United States * Corresponding Author ABSTRACT

Small and medium-sized enterprises (SMEs) often face obstacles in reaching consumers and obtaining sufficient capital for their production and operations processes. Owning channel advantages and rich transaction data regarding suppliers’ sales, inventory, and credits, e-commerce platforms (henceforth, eretailers) can offer online distribution channels and online financing service for SMEs to facilitate their distribution and alleviate their capital constraints. This study analyzes the pricing competition in a dualchannel supply chain consisting of one capital-constrained supplier and one e-retailer providing finance. The supplier can sell her products either through the e-retailer using the online channel or through her direct offline channel. The e-retailer offers finance to the supplier if she is capital-constrained. We examine the equilibrium price and the associated optimal quantity and profits in dual channels when supplier may face capital constraint and compete with e-retailer horizontally or vertically. We find that eretailer finance is a value-added service for e-retailer and that the increased profits generated from financing offerings can offset the lowered revenue in the online distribution channel. E-retailer finance can increase market share, which also benefits the supplier. Participating in the vertical competition through announcing pricing decisions earlier than does the supplier can help the e-retailer seize the firstmover advantage. Further, we present the value of e-retailer finance and examine the impact of various financing, operational, and consumer-related factors on pricing and channel structure. We also provide guidelines for e-retailers and financing-constrained suppliers to utilize e-retailer finance to optimize their dual-channel structure and to make optimal pricing decisions. Keywords: supply chain finance, e-retailer finance, dual channel, pricing, game theory

1. INTRODUCTION In today’s business world, the number of small and medium-sized enterprises (SMEs) is growing rapidly, and SMEs are critical for economic growth in both developed and developing countries. The two biggest challenges most of SMEs currently face worldwide are the difficulties in accessing capital and distribution channels. As for financing challenges, traditional financing methods for SMEs are through commercial banks. However, due to SMEs lacking certain tangible internal resources, having high transaction costs, and being exposed to large operational risk and serious information asymmetry, bank loans are very costly or even unavailable to SME suppliers (Tang et al., 2018). The shortage of funds for

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suppliers can disrupt the entire supply chain due to suppliers’ failure in procuring raw materials and funding their production to fulfill the buyers’ orders (Huang et al., 2018). Channel challenges develop for SME suppliers because of their limited network connections, shortage of investments, their smaller size, information asymmetry, the general difficulty of reaching their customers, and high channel cost (Tunca & Zhu, 2018). Seeking intermediaries and utilizing platforms to attract prospective customers, reduce channel costs, and leverage the firms’ assets can be one of the solutions to overcoming the channel obstacles that follow the rapid development of platform economy, in which the economic and social activities are facilitated by platforms such as online matchmakers or technology frameworks (Otero et al., 2014). Given the fact that the working capital cost of a supplier can be up to about 20% of the total cost in a supply chain, buyers and suppliers are seeking collaboration to reduce the working capital cost for suppliers and to enhance the return on cash for buyers through large buyers’ offerings of financing services (Loughlin, 2012). Large buyers such as e-commerce platforms have recently started to implement their own financing schemes (Tunca & Zhu, 2018). The e-retailer giants such as Amazon in North America; and Suning.com and JD.com in China have launched ―Internet Commerce Banks‖. They viewed their financing services as the primary services, which have rapid growth during the past couple of years (JD.com, 2017). E-retailers can utilize their data resources about suppliers’ sales, inventory, and credits to control suppliers’ default risk. Currently, one of the biggest e-retailers in China, JD.com, provides online finance services (JD Finance) such as ―Jingbaobei,‖ which offers its suppliers instantapproval loans (within three minutes) (Tsai & Kuan-Jung, 2017). JD Finance has offered loans to many suppliers in various industries. Among them, typical industries include the 3C (Computer, Communication, and Consumer Electronics) industry and fashion industry. For example, the Langtongruilian Science and Technology Ltd. is one of those firms benefited by JD Finance, which helps its normal operations (Fenghuang News, 2017). In this way, JD.com has been serving the functions of both an e-commerce marketplace and an online financial service provider. For another example, suppliers in the fashion industry have commonly adopted e-retailers’ financing services to smooth their production. The fashion supplier named ―Guangyu‖ received 500 thousand RMB (around 70 thousand U.S. dollars) from JD finance in 2018 to facilitate its cash flow to have smooth production. This is a snapshot of thousands of suppliers benefited or will be benefited from e-retailer finance (Xuehua News, 2018). All of these financing services to its suppliers are delivered via its own e-commerce platform, which motivates suppliers, especially SME suppliers, to use their e-commerce platforms to distribute their products (Dou & Wong, 2015). For SME suppliers, the additional distribution channel offered by e-retailers may also bring competition. Armed with access to capital from e-retailers and to reach more customers, many SMEs

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want to adopt an integrated dual-channel strategy to distribute products both online and offline (Otero et al., 2014). For the suppliers, how to manage dual channels is a strategic issue rather than an operational one (Hsiao & Chen, 2014; Xia et al., 2017). Depending on SMEs’ operating strategies, SMEs have two options by which they can extend their online channels, differentiated by whether they want to develop their online channel with similar or even greater emphasis compared with their existing offline channel (Hsiao & Chen, 2014). For the first option, suppliers can place greater emphasis on developing an online distribution channel than their own offline direct channel. These suppliers have limited locations of physical stores, and mainly utilize online channels to reach consumers and sell their products. In this way, suppliers emphasize the sale of their products mainly through independent e-retailers, and thus, the supplier and e-retailer compete vertically (Chen & Sheu, 2017; Cho et al., 2009). Many suppliers in the fashion industry, such as the aforementioned ―Guangyu‖ firm, are those examples (Xuehua News, 2018). These vendors mainly use Walmart.com and other e-retailers to reach their consumers and generate sales. For the second option, suppliers can develop online channel through e-retailers’ distribution platform and their own offline direct channel simultaneously with equal focus. Various vendors in the 3C industry such as the aforementioned Langtongruilian Science and Technology Ltd. are these examples (Fenghuang News, 2017). These vendors have their brick and mortar stores as the convenience stores in various locations for consumers nearby to purchase, and their products are also listed in e-retailer’s online channel such as Amazon for sale. In this way, a supplier’s role is more like that a supplier using its direct offline channel to compete with an e-retailer’s online channel sales; thus, the supplier and e-retailer horizontally compete with each other (Li, 2002). The choice of options depends on how suppliers want to promote the depth and breadth of their products in a broader market through developing their distribution channel(s), particularly reaching for the ―not-so-obvious‖ customers (Stone et al., 2002). Product pricing, especially in dual channels, is an effective approach to capture the majority of supply chain profits and market share that suppliers may utilize to optimize their profits (Cai et al., 2009). Previous studies have discussed implementing optimal pricing decisions in each of the dual channels to enhance the channel profits (e.g., Cai et al., 2009; Dan et al., 2012). However, more studies focus on how to make pricing decisions rather than when to make pricing decisions. The influence of the pricing leadership and the announcement of the pricing sequentially or simultaneously on the dual channels’ profits are rarely examined. Motivated by the above research gaps and practical cases, this study aims to examine pricing competition of the capital-constrained supplier and the e-retailer in dual channels when using e-retailer finance. The supplier has two options for her channel development strategy. The first option is to place greater emphasis on developing an online distribution channel through e-retailer’s distribution platform, namely, to have vertical competition with the e-retailer (Game V). The second option is to develop an

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online channel and her own offline direct channel simultaneously with equal focus, namely, to have horizontal competition with the e-retailer (Game H). Examining vertical and horizontal competition games reflects the two different pricing positioning strategies that compete between online and offline channels (Matsui, 2017; Tian et al., 2018). An examination of pricing positioning strategy can show the influence of pricing leadership on equilibria, which reflects the strategic role of pricing sequentially or simultaneously in affecting various channels’ demand and stakeholders’ profits (Chen et al., 2018). Compared with Game H, in Game V, the supplier relies more on the e-retailer to generate her sales and vertically compete with the e-retailer. Thus, the e-retailer is the Stackelberg leader to price online first, and then the supplier decides her offline price. In Game H, the supplier horizontally competes with the eretailer using her offline channel, and each of them has her/his own power to decide their own channel’s price. In this way, they price online and offline simultaneously. We examine the value of e-retailer finance through the comparison of two scenarios – the supplier without capital-constraint (Scenario N) and capital-constrained supplier with e-retailer finance (Scenario B). Further, we examine the impact of various influencing factors related to finance (i.e., interest rate and loan ratio), operations (i.e., cost difference and bargaining power), and consumers (channel preference and price substitution). This study has three research questions. First, through comparison of various scenarios, we examine the value of e-retailer finance on profits and quantities in the context of our first research question: For the e-retailer, can providing finance be profitable or a loss? Additionally, our second research question is what is the optimal pricing strategy when the supplier has different strategies of developing the dual channels? Via modeling different games, this study examines the dual-channel pricing strategy that exists when the e-retailer and supplier compete horizontally or vertically. The third inevitable research question is how does the e-retailer finance scheme affect channel structure? By comparing online and offline quantities, we analyze the dual-channel structure with various influencing factors. In this way, this study provides a roadmap for the e-retailer and financing-constrained supplier to utilize online finance to optimize their dual-channel structures and make optimal pricing decisions. The contributions of this study mainly lie in the following aspects. First, regarding the e-retailers, most of the previous studies (e.g., Agatz et al., 2008; Jiang et al., 2013) have focused on e-retailers’ distribution function, with very few studies (e.g., Tsai & Kuan-Jung, 2017) examining e-retailers’ buyer finance role in a strategic perspective. This study addresses this research gap by investigating e-retailer’s dual roles in more depth: online sales and online financing offerings; and the interaction between them. Second, regarding the role of online financing offerings, few studies (e.g., Tsai & Kuan-Jung, 2017) have discussed the impact of suppliers’ capital constraints on dual-channel pricing decisions. Most of the previous SCF studies have examined traditional offline SCF (e.g., Deng et al., 2018; Yang & Birge, 2017). This study is one of the few studies focusing on online buyer finance. Third, this study is one of

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the first studies examining the impact of e-retailer’s financing offerings on pricing under various channel structures. Moreover, most of SCF studies have focused on the capital constraints from SME retailers, with few discussing the suppliers’ capital constraints (e.g., Tang et al., 2018; Tunca & Zhu, 2018). The remainder of this study is organized as follows. Section two reviews the literature. Section three introduces the model framework. Section four sets up the vertical and horizontal competition games in two scenarios and analyzes the equilibria. Section five presents the value of e-retailer finance through comparative analysis. Section six discusses the channel structure development strategy. Section seven extends the study by examining the credit financing scheme and comparing it with the factoring financing scheme. Section eight concludes the study and proposes future extensions to research.

2. LITERATURE REVIEW Our study examines the dual-channel pricing issues in the context of e-retailer finance. Thus, two fields of previous studies are relevant to our current study: SCF and dual channels. 2.1 SCF SCF studies have garnered much attention by researchers focusing on cash flow management. SCF papers published from 2000 to 2014, in a total of 119 papers, are systemically reviewed in the study of Gelsomino et al. (2016). They claimed that SCF can be supply chain oriented—with a focus on working capital optimization of accounts payable, receivable, and inventory—and/or fixed asset financing. Our study follows this stream of the studies to optimize the working capital and profits through optimal pricing decisions using online and offline channels. Depending on the finance source, the studies on SCF can be divided into two categories (Chen et al., 2019), namely, internal SCF (i.e., funding obtained from supply chain participants) and external SCF (i.e., funding obtained from external financing institutions). The present study can be categorized as being of the first type, and e-retailer provides the capital resources. Various SCF schemes were studied in earlier literature to alleviate the supply chain participants’ capital constraints. Depending on who offers financing in the supply chain, the SCF studies can be categorized into two types: supplier finance and buyer finance. For supplier finance, trade credit is a common mechanism (Yang and Birge, 2017). For buyer finance, Tang et al. (2018) examined a supply chain consisting of a manufacturer, a financially constrained supplier who could improve the delivery reliability through inputting efforts, and a bank. They found that information symmetry/asymmetry between the manufacturer and supplier decides the relative efficiency of the SCF. Deng et al. (2018) compared the mechanism of financing multiple heterogeneous suppliers from a bank or buyer. They found that the suppliers’ initial capital amount, production costs, and their heterogeneities influence the

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selection of the optimal financing scheme. This study focuses on buyer finance and provides a solution to alleviating suppliers’ capital constraints through e-retailers’ factoring. Compared with the traditional SCF research, many fewer studies have focused on online SCF. Online SCF, or e-retailer finance, as a particular form of SCF with the feature of financing through an online platform, has been emerging and rapidly developing with the background of online peer-to-peer (P2P) lending development (Gao et al., 2018). Recently, online SCF has been applied in the financing schemes led by commercial banks, e-commerce (e.g., B2B, B2C) platforms, and/or a co-leading by banks and platforms. Zheng and Zhang (2017) analyzed the SCF coordination mechanism for B2C cross-border commerce. They found that online SCF can lower banks’ interest rates and increase their accounts receivables, thus lower the financing cost of non-core companies through the guarantees of core companies. Tunca and Zhu (2018) used online retailer data and, through structural regressions, demonstrated that the buyer intermediation lowered wholesale prices and interest rates, increased the fill rates of orders and supplier borrowings. Our study examines the e-retailer’s SCF offerings to the supplier through factoring in the dual-channel structure and proposes an extended model of credit financing scheme. 2.2 Dual Channels At present, many firms use more than one single channel to distribute their products and attract consumer demand. Previous studies mainly examined three critical issues in dual channels. The first issue is consumer shopping behaviors. Previous studies have examined the characteristics of consumers who prefer to shop online versus those who prefer to shop offline (Hsiao & Chen, 2014) and the phenomena of consumers’ browsing the products in brick-and-mortar retail stores and then later switching to online shopping to place their orders (Balakrishnan et al., 2014). The second issue is competition and coordination. These can happen among homogenous retailers (e.g., Giri & Sarker, 2016), between manufacturers and retailers (e.g., Yang et al., 2017), or among all supply chain participants (e.g., Dvaid & Adida, 2015) for prices and service levels competition. The third issue is the optimal pricing strategies in dual channels, which can often influence the channel structure. Xia et al. (2013) discussed the pricing power issues in dual channels and found both pricing power and products’ substitutability influence manufacturers’ dual-channel development motivation. Compared with the problem of how to make pricing decisions, only a few studies (e.g., Matsui, 2017; Wang et al., 2018) focused on when to make pricing decisions. Essentially, the pricing timing issue is about the pricing leadership: whether the supply chain participants want to set the pricing simultaneously or sequentially and whether the first mover (i.e., the leader) or the last mover (the follower) has the advantage (Chen et al., 2018). Wang et al. (2018) examined a manufacturer’s online channel issue, where the channel options include a direct-sales channel and a third-party consignment channel to complement the existing brick-and-mortar store. They found

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that if manufacturers announced pricing decisions before physical retailers did, the manufacturers could always obtain a higher profit. The manufacturers’ incentive to adopt an online channel depends on the unit operating cost in each channel and revenue allocation ratio in various channels. Matsui (2017) suggested that manufacturers post the direct price before or upon setting the wholesale price for retailers in order to maximize the manufacturers’ profits when implementing multichannel sales strategies. This study bridges the above categories of studies in dual channels. For pricing issues, we examine both when to and how to make pricing decisions. We examine both vertical and horizontal competition between suppliers and e-retailers in dual channels. In addition, this paper bridges e-retailer finance and dual-channel studies. Previous studies (Dan et al., 2012; Yang et al., 2017) mainly focused on the distribution/sales function of the dual channels. E-retailers can have dual functions for suppliers: selling products online (i.e., distribution function) and providing loans (financing function). This study fills the literature gap through discussion of the optimal pricing and its timing strategy in both of the online and offline channels to maximize the participants’ profits when e-retailer takes on both the roles of offering online sales and online finance services.

3. MODEL FRAMEWORK 3.1 Channel Structure and Financing Scheme In this study, the supply chain consists of a supplier (hereinafter referred to as she) and an e-retailer (hereinafter referred to as he). The supplier has dual-channel options to distribute the products: an offline sales channel through direct retailing stores and an indirect online sales channel through the e-retailer’s platform. The supplier has capital constraint on production. The e-retailer plays two roles: as a downstream buyer to distribute and sell products and as a financing provider to fill in supplier’s capital gap. In the role of a downstream buyer, in the indirect online sales channel, the e-retailer sells the product to the customer after purchasing the product from the supplier. This is a common practice in today’s ecommerce, as exemplified by JD.com and Amazon.com, two of the biggest e-commerce platforms in China and in the United States respectively. In the role of a financing provider, the e-retailer can provide financing based on factoring that serves a short-term financing scheme for suppliers. The supplier’s accounts receivable serves as the traded asset for the e-retailer to provide immediate cash or serve as loan collateral (Gelsomino et al., 2016). In practice, the factoring usually covers part of the value of receivables up to 70% (Kouvelis & Xu, 2018). As the buyer, the e-retailer usually uses credit sales when considering the potential defects and return issues (Fitzpatrick & Lien, 2013). Thus, the supplier often holds the last period of accounts receivables for the e-retailer. Without loss of generality, in this study, we assume in each period the e-retailer’s order quantities are the same, and thus the receivables credits are the same in each period. In practice, when the supplier has capital needs, she requests loans from the e8

retailer according to her receivables receipt held. However, in the online financing scheme, the e-retailer usually does not fully accept all the account receivables as the loan amount. Instead, depending on the supplier’s historical records regarding her transaction, credit score, and default risk, the e-retailer sets a credit line for her, namely, allocates  proportion of the loans to the account receivables, and then charges interests (Kouvelis & Xu, 2018). At the end of the selling season, the e-retailer pays the supplier

Figure 1: Model Framework

with the remaining accounts payables. The dual-channel structure and financing scheme can be illustrated in Figure 1. 3.2 Notation and Assumptions The study makes the following assumptions: (1) The supplier and e-retailer are risk neutral (e.g., Gao et al., 2018); (2) The risk-free interest rate is zero, and accordingly the opportunity cost of capital is zero (e.g., Kouvelis & Zhao, 2015); (3) No information asymmetry exists among supply chain participants (e.g., Gao et al., 2018); (4) Based on common practices, the products in dual channels are homogenous. However, the channel costs to distribute the products are different, namely, the unit cost in the online channel is lower than that in the offline channel, due to the extra marketing expenses (e.g., costs of store construction, shelf display and so on) required in the offline channel, namely, ce  cs ; (5) To avoid trivial cases, the marginal profit is positive, namely, cs  ps and ce  w  pe . Namely, the unit price floor of online (offline) channel can be denoted as pe ( ps ); where pe  ce (1   ) , ps  cs . In this study, we assume the wholesale price is a proportion of the sales price depending on  . This follows the previous studies (e.g. Abratt & Pitt, 1985; Shipley, 1986; Shipley & Jobber, 2001) that found cost-plus pricing is the (or one of the) most widely practiced pricing approaches in various countries. Cost-plus pricing is to add a markup to the cost of products to reach a sales price. The markup is usually a proportion of the cost. Today, many manufacturers use cost-plus pricing strategies in various industries all 9

over the world (O'Rourke, 2019; SKP, 2019). A typical price incremental proportion can range from 5% to 800% (Dholakia, 2018). In this study, the proportion depends on the bargaining power of the e-retailer, with the higher bargaining power leading to the higher sales price. The notation is summarized in Table 1. Table 1: Notation Variables Superscripts

Description Scenarios that supplier has no capital constraint (N); has capital constraint with e-retailer finance (B) Games of vertical (V) or horizontal (H) competition between supplier and e-retailer

Subscripts e s Decision Variables

E-retailer Supplier

peij

Unit price of online channel in scenario in game

psij

Unit price of offline direct channel in scenario in game

qeij

Online demand in scenario in game

qsij

Offline demand in scenario in game

L

Loan amount

Parameters

ce cs 

wij

Unit cost of online channel Unit cost of offline direct channel E-retailer’s bargaining power on pricing;   0,1 . Higher value of  indicates stronger bargaining power. Unit wholesale price offered by supplier to e-retailer in online channel in scenario in game . The wholesale price is a proportion of the online retail price depending on e-retailer’s price bargaining power. Namely, wij  1    peij .

pe

Unit price floor of online channel

ps 

Unit price floor of offline channel



K

r 

 eij  sij

Customers’ preference for online channel; 0    1 . Thus, 1   represents customers’ preference for offline channel. The price substitution coefficient, showing the price competition level between dual channels; 0    1 . Supplier’s initial capital Interest rate Loan ratio, 0    1 . E-retailer’s profit in scenario in game Supplier’s profit in scenario in game

3.3 Demand Functions Without loss of generality, we set the total demand of the dual channels to be one. According to previous studies (Mcguire & Staelin, 1983), the demand for each channel depends on the sales price in each

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channel, which is negatively related to the price in its own channel and positively related to the price in the other channel due to the substitution effect, as shown in Eq. (1). qe ( ps , pe )    pe   ( ps  pe ) .  qs ( ps , pe )  1    ps   ( pe  ps )

(1)

In Eq. (1), the variable  represents customers’ initial demand in the online channel, which indicates customers’ preference on the online channel (Cai et al., 2013). Thus, the variable 1   represents customers’ preference on the offline direct channel. Intuitively, the variable  reflects how favorable for shoppers is the online channel compared with the offline channel, where customers can be categorized as more like Internet shoppers or grocery shoppers depending on their channel preference (Hsiao & Chen, 2014). The parameter  shows the price substitution coefficient between online and offline channels, which indicates the price competition between the two channels. The value range   0,1 ensures that the impact of the other channel’s price on customer demand is no more than the impact of its own channel’s price. The smaller  indicates a smaller substitution effect due to the price difference between the dual channels. The value  with full substitution, while 

1

0

indicates that the dual channels completely compete with each other

indicates that the dual channels are demand independent.

4. PRICING STRATEGY For each game, we examine two scenarios depending on the supplier’s capital constraint and access to finance, that is, the scenario of without capital constraint and scenario of with capital constraint but having access to finance. In each scenario, the supplier aims to maximize her profits by optimizing the offline retail price. The e-retailer aims to maximize his profits by optimizing the online retail price. In this section, we first formulate the decision problems in the two scenarios, then describe the sequence in vertical and horizontal games, and finally present and analyze the equilibria. 4.1 Scenarios (1) Benchmark: Unconstrained Supplier (Scenario N) When the supplier has sufficient capital to operate online and offline channels, she does not need to finance from the e-retailer. The supplier’s decision problem is as follows: max  sNj   psNj  cs  qsNj   wNj  ce  qeNj . Nj

(2)

ps

s.t. cs qsNj  ce qeNj  K .

(3)

The e-retailer’s decision problem is as follows: max  eNj   peNj  wNj  qeNj . Nj

(4)

pe

(2) Capital-constrained Supplier with E-retailer Finance (Scenario B)

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When the supplier has capital constraint and has access to finance from the e-retailer, the supplier’s decision problem is: max  sBj   psBj  cs  qsBj   wBj  ce  qeBj   wBj qeBj r . Bj

(5)

ps

s.t. ce qeBj  cs qsBj  K  L .

(6) In Eq. (5), the supplier’s profits at the end of the sales season include the retail sales profits

p

Bj s

 cs  qsBj from the offline channel, plus the wholesale profits  wBj  ce  qeBj from the online channel,

Bj Bj and minus the financing interest cost Lr , where L   w qe and 0    1 . The loan provided by the e-

Bj Bj retailer is based on the supplier’s accounts receivables w qe . In this way, the loan amount can be

straightforwardly determined by the invoice value. Thus, the e-retailer does not need to collect any other information such as the supplier’s initial capital and offline price, which shortens the loan request and approval process, and makes the loans more attractive and efficient. This mechanism is practiced by the ―Jingbaobei‖, an online finance services offered by JD.com to its suppliers, with the loan approval process being as short as several minutes. The loan ratio 0    1 ensures the loan amount cannot exceed supplier’s accounts receivables. The supplier’s better credit history such as higher credit rating can motivate the e-retailer to offer a higher credit line through a higher loan ratio. It is intuitive that the supplier has no incentive to over-borrow the loan due to the extra interest rate. In addition, the e-retailer does not approve a loan with more than the required amount in practice. Thus, the loan size ( L ) should be Bj Bj no more than the supplier’s capital gap (ce qe  cs qs  K ) . Namely, the scenario of K  ce qeBj  cs qsBj  K  L

does not exist. Thus, the supplier faces Scenario B when ce qeBj  cs qsBj  K  L , which can be characterized in Eq. (6). Whether the loan amount can fully cover the supplier’s capital gap depends on her initial capital and the loan ratio she can get from the e-retailer. Accordingly, the e-retailer’s decision problem is: max  eBj   erBj   efBj   peBj  wBj  qeBj   wBj qeBj r . Bj

(7)

pe

In Eq. (7), the e-retailer’s profits include the online sales profits  erBj   peBj  wBj  qeBj and the interest earned from online finance  efBj   wBj qeBj r . The supplier decides offline channel retail price, while the eretailer decides the online channel retail price. The online wholesale price offered by the supplier to eretailer is a proportion of the online retail price depending on the bargaining power of the e-retailer (Dukes et al., 2006; Nair et al., 2011). 4.2 Sequences Referring to previous studies (e.g., Matsui, 2017; Wang et al., 2018), depending on the supplier’s dualchannel development strategies, we formulate two strategic games in this study to examine the equilibrium prices. (1) Vertical Competition Game (Game V) 12

In the vertical competition game, the online channel is her emphasis on the targeted market, although she also operates a direct offline channel. The supplier would like primarily to develop her online channel through using the e-retailer to distribute and sell her products with the expectation of obtaining profits from the wholesale price offered to the e-retailer (Hsiao & Chen, 2014). In this way, the supplier’s role is more like a vendor wholesaling products to the e-retailer, and the supplier and e-retailer compete vertically with each other (Chen & Sheu, 2017; Matsui, 2017). Hence, in Game V, the e-retailer first iV iV determines the online retail price pe , and then the supplier makes the offline pricing decision ps .

Technically, we formulate this vertical competition game as a Stackelberg game because the supplier and the e-retailer determine channel prices sequentially, in which the e-retailer acts as a leader and the supplier acts as a follower. (2) Horizontal Competition Game (Game H) In the horizontal competition game, the supplier would like to develop both online and offline channels simultaneously, namely, the supplier adopts both e-retailer and her direct channel to sell the products with the same emphasis. In this way, the supplier’s role is like a retailer selling products directly to customers, and thus, the online and offline channels compete with each other horizontally (Li, 2002). iH iH The supplier decides the price ps and the e-retailer decides the price pe simultaneously with no

communication (Wang et al., 2018). Hence, we use a Nash game to formulate this horizontal competition strategy in which the supplier and e-retailer decide the prices simultaneously. The sequence of events in the Stackelberg Game (i.e., Game V) or Nash Game (i.e., Game H) in this study is summarized in Figure 2. Note the main reason that the supplier cannot be considered the leader in our proposed Stackelberg game is because this study’s domain lies in the buyer finance. The supplier has the capital constraints of production and needs to seek finance from the e-retailer. This happens frequently when the supplier is small or medium-sized (i.e., an SME supplier) and the e-retailer is a retail giant (Tunca & Zhu, 2018). Therefore, due to the financial constraints of the supplier and her weaker power in comparison to the eretailer, the supplier is unlikely to act as a leader in the Stackelberg game. This assumption is supported by previous studies (e.g., Deng et al., 2018; Tang et al., 2018; Tunca & Zhu, 2018) in which the financially constrained supplier is not considered to be a leader in the game. 4.3 Equilibria We mainly focus on the dual-channel strategy. The single channel is a trivial case. The supplier choosing dual channels, instead of single channel, needs to satisfy two conditions: (a) The marginal profits for each channel is positive to ensure that capacity allocation for each channel is positive, and (b) the effective demand for each channel is positive which requires the price is not too high. These two conditions depend on customers’ online channel preference. 13

Figure 2: Sequence of Event

Table 2 presents the condition that the dual channels exist through finding the price floor and ceiling, and the condition of positive capacity allocation and effective demand. In Table 2, to simplify the notation  ， and generalize the comparison, we use the parameter  i    r 1     ，

iN iB

to characterize the

additional (adjusted) bargaining power of e-retailer for different scenarios, where  represents the pure bargaining power without financing in scenario N. In scenario B, in addition to the pure bargaining power, the adjusted bargaining power of the eretailer is from his financing offerings. In practice, the e-retailer has more flexibilities compared with traditional banks to determine the loan ratio and interest rate. The higher loan ratio offerings and interest B B rate lead to the e-retailer’s higher adjusted bargaining power (namely,  r  0 and    0 ). This

is because the higher loan ratio indicates that the e-retailer offers a higher amount of loan to the supplier. In this way, the supplier’s financial constraints can be more largely alleviated. The higher interest rate indicates the scarcity of loan offerings from other competitors, which means more difficulties of obtaining loans due to the supplier’s low credit score, short credit history, or higher default risk; or relatively higher channel power of the e-retailer. Thus, according to the resource dependence theory (Pfeffer and Salancik, 2003), the higher loan ratio and interest rate indicate the monetary resources the e-retailer owns are more important to the supplier, which makes the e-retailer have control over them relatively more concentrated, and thus the e-retailer has the higher adjusted bargaining power (Crook and Combs, 2007). Thus, the eB N retailer has higher adjusted bargaining power when offering finance , namely,    . The more

resources provided to the supplier makes the e-retailer has higher bargaining power and negotiation advantage in a supply chain, which can facilitate better channel management (Feng and Lu, 2013).

14

1

In detail, Table 2 shows the conditions of the existence of rational pricing range of participant m in Scenario i in Game j. In order to reflect the nonij ij negative profit margin and the non-negative demand for each channel to ensure the existence of the dual-channel structure, we let  sc and  ec be the ij ij threshold value of supplier allocating capacity for offline and online channel respectively, and let  sd and  ed be the threshold value of customers having

effective demand for offline and online channel respectively, as presented in Table 2 . For m  s, e , i   N , B , and j  V , H  , we first present the price floor of Game V and Game H. For offline channel, it is the unit cost of offline direct channel. For online channel, it is related to the unit cost of online direct ij ij * channel and the bargaining power. Then, we present the price ceiling pm , which refers to the upper bound of the equilibrium prices. If pm is higher than the

price ceiling, the effective demand will be zero. Only when the price is between the floor and ceiling will the supplier develop a dual-channel strategy. To ensure the demand of each of the dual channels, we need the capacity allocation and the effect demand to be positive for each channel. Finally, in Table 2, we compare the price ceiling, the threshold value of capacity allocation, and the effective demand for offline and online channel in Scenarios B and N. Further, ij ij ij we find the price ceiling of an online channel pe increases with  , i.e., pe   0 , and the price ceiling of an offline channel ps decreases with  , i.e.,

psij   0 . Lemma 1 describes how supplier’s channel structure depends on customers’ channel preference. Lemma 1: Channel structure with customer online preference. ij ij ij (a) If ˆe    ˆ s , then dual-channel structure exists, where ˆ sij  min  scij ,  sdij ,1 , ˆeij  max ecij ,0 , i   N , B , and j  V , H  . (b) If   ˆ s , then only ij pure online channel exists. (c) If   ˆ e , then only pure offline channel exists. BV BH Bj It is worth noting that because ed  ed  0 , then ˆe  0 . This shows that the effective demand for an online channel in scenario B is always positive.

This is because the loan amount that the capital-constrained supplier gets from e-retailer finance is based on the account receivables, and then the supplier should maintain the online channel in Scenario B. Hence, in the following analysis, we mainly focus on the dual-channel structure and ignore the trivial cases of pure online channel or pure offline channel. The equilibria of dual channels in each scenario and each game are summarized in Table 3. In Scenario B, we have two different cases in equilibria according to the supplier’s capital constraint. If the supplier is highly constrained (Case 1), namely, K  Kˆ , then the constraint in Eq. (6) is non-binding. In this case, the supplier can receive a loan, but with a limited amount that cannot fully cover her total production costs in dual channels. Otherwise, if the supplier is less constrained (Case 2), namely, K  Kˆ , then the constraint in Eq. (6) is binding. In this case, the loan the supplier received can exactly cover the total production costs in dual channels to eliminate her capital constraint. The equilibria are functions of her initial capital K . In order to facilitate the comparative analysis between Scenario N and B, in which both equilibria are independent of K , without loss of generality, we mainly focus on Case 1 in the following sections. From Table 3, we can find that online and offline pricing strategies are influenced by various factors. In this subsection, we focus on operational factors including channel costs and the retailer’s bargaining power, as described in Corollary 1, and on consumer-related factors (online channel preference), as presented in Proposition 1. Corollary 1: The operational factors affect pricing as follows: Bj * Bj * Bj * Bj * (a) supplier’s channel costs: pe ce  0 , ps ce  0 ; pe cs  0 , ps cs  0 .

Bj * Bj * (b) e-retailer’s bargaining power: pe   0 , ps   0 .

Corollary 1(a) shows that both the online and offline prices decrease with online cost and increase with the offline cost. Because cs  ce , the increased online (offline) cost makes the cost difference between online and offline decreased (increased). This makes the customers perceive less (more) heterogeneity of the products online and offline, which leads to the more (less) fierce pricing competition between supplier and e-retailer to attract customers through reducing (increasing) each channel’s price to earn more profits. Corollary 1(b) shows that both the optimal online and offline prices decrease with the e-retailer’s bargaining power. When the bargaining power increases, the e-retailer can obtain the same marginal profits through the reduction of the online price. The reduced online retail price enhances the competition between the dual channels, which pushes the supplier to reduce the offline channel price to attract customer demand. Proposition 1: Comparative analysis of customers’ online preference impact on the prices between scenarios and games, where i   N , B and j  V , H  is as ij * ij * ij * ij * Nj Bj Bj Nj iV iH ij ij follows: (a) if    ec , then pe   0 ; Otherwise pe  =0 . If    sc , then ps   0 ; otherwise ps   0 . (b) ec  ec ,  sc   sc ;  ec   ec ,

iH iV Nj * Bj * Nj * Bj * iV * iH * iV * iH * and  sc   sc if 1  cs  ce   0 . (c) ps   ps  , pe   pe  ; ps   ps  , pe   pe  .

1

Proposition 1(a) illustrates that when the customer’s online channel preference is higher than the lower (upper) bound, then the online (offline) channel price increases (decreases) with customers’ preference for the online channel. The enhanced customers’ preference for online channel serves as a premium the customer would like to pay for using the online channel. This can offset the negative influence of increased online channel price on customer demand and can thus encourage the e-retailer to raise the online price. The offline channel has to lower the retail price to attract more demand when customers’ preference for online channel increases. According to Proposition 1(b), we find the lower bound of the online channel preference in Scenario N is smaller than that in Scenario B, and the lower bound of the online channel preference is lower in Game V compared with that in Game H. However, for the upper bound of the online channel preference, in Scenario B it is smaller than in Scenario N, and in Game H is smaller than in Game V. Proposition 1(c) shows that the influence of the online channel preference on both online and offline prices in Scenario N is higher than in Scenario B, and in Game V are higher than in Game H. This is because when offering online finance, the e-retailer converts interest rates and financing ratio into his enhanced bargaining power, which is  B   N ; the

0

1

0

1

Figure 3: Channel Structures between Different Scenarios

0

1

0

1

Figure 4: Channel Structures between Different

price competition betweenGames online and offline becomes fiercer. Thus, with a higher preference for the online channel, the extent of the price change in Scenario B is smaller. Similarly, the price competition between online and offline is fiercer in Game H than in Game V, and the extent of the price change in Game H is smaller as the online channel preference increases. According to the above analysis, we can demonstrate the pricing range between different scenarios in Figure 3 and between different games in Figure 4 with the change of customers’ online channel preference. From Figure 3 and Figure 4, we find the e-retailer and the supplier have more room to adjust their price in Scenario N and in the vertical game. The capital constraint limits the pricing range of the supply chain participants. Compared with the horizontal competition, the vertical competition provides both supplier and e-retailer more options to use pricing strategy (e.g., promotion, pricing competition, and differentiation) to earn more profits through adjustment of their marginal profits. The online retail price in Game V is higher than in Game H, which indicates that the wholesale price in Game V is higher than in Game H under a given bargaining power.

5. VALUE OF E-RETAILER FINANCE This section continues the discussion of pricing strategies from the e-retailer finance perspective. We further examine the value of e-retailer finance in enhancing profit and market share. Proposition 2: Price comparison with and without e-retailer finance peBj*  peNj* and psBj*  psNj* , j  V , H  .

From Proposition 2, we find that both the online and offline price is lower in Scenario B compared with Scenario N, which is due to  B   N . In addition, interestingly, according to Table 3, we have peNV *  peBV *  peNH *  peBH * , and psBV *  psBH * . This shows for both of the supplier and e-retailer, their pricing in the vertical game is higher than in the horizontal game. Corollary 2: Influence of financing factors on pricing is as follows: (a) Interest rates: peBj* r  0 and psBj* r  0 , j  V , H  ; (b) Loan ratio: peBj*   0 and psBj*   0 , j  V , H  .

2

From Corollary 2, we can find that both the online and offline channel prices decrease with interest rates and factoring financing proportion. Higher interest rate and factoring proportion increase the e-retailer’s bargaining power, which then reduces the online and offline prices. Corollary 3 compares the loan sizes between vertical and horizontal competitions. Corollary 3: Comparisons of loan size between different games

 2a     1    8 1     2  4   2 B  2

V LV  LH , where L 

and LH 

2 1    2a     1   

 4  8    2     2

B

2

.

From Corollary 3, we find that the loan size in the vertical competition game is higher than in the horizontal competition game. This shows that the eretailer’s first-mover advantage in Game V and the price competition between online and offline is smaller compared with Game H; thus, the e-retailer is willing to offer more loans. 5.1 Effect of E-retailer Finance on Profit Increase E-retailer’s profits come from sales revenues  er and financing interests  ef . Proposition 3 examines the online finance’s effect on e-retailer’s profits increase from three perspectives. Proposition 3: Online finance’s effect on e-retailer’s profit increase is as follows: (a) between different scenarios:  eBj*   eNj*   erBj* , j  V , H  ; (b) between different games:  eiV *   eiH * , i   N , B ; (c) between different profit sources:

 erBV *   erBH * and  efBV *   efBH * . Proposition 3 shows that offering online finance is a value-added service for the e-retailer, one that can offset his reduced retail revenue. In detail, we found the following results. Proposition 3(a) illustrates that in both games the e-retailer’s profits when offering financing is higher than when he does not offer financing, which shows that providing financing is profitable. Compared with no financing offering, providing online finance can bring more interest revenue for the e-retailer, which can offset lowered online retail profit and thus, increase the e-retailer’s total profits. Proposition 3(b) shows for an e-retailer that his profit in Game V is higher than in Game H, regardless of whether he provides financing. This shows the first-mover advantage of announcing the pricing strategy first. Proposition 3(c) reflects that, when providing online finance, the e-retailer’s profits come from two sources: online sales profit and online finance interests. The higher profits in Game V compared with Game H come from both the online profit and the online financing offering. 5.2 Effect of E-retailer Finance on Market Share Increase Proposition 4 shows the effect of e-retailer finance on the supplier’s market share increase. Proposition 4: Comparative analysis of equilibrium quantity between different scenarios. (a) qeBV*  qeNV* , qsBV*  qsNV* ; (b) qeBH*  qeNH* , qsBH*  qsNH* ; (c) qeBj*  qsBj*  qeNj*  qsNj* , j  V , H . Proposition 4(a) shows in Game V that e-retailer finance cannot affect online quantity; however, it can increase the offline channel quantity. The reason is, in Game V, that the e-retailer has the first-mover advantage, and thus, the online quantity is not influenced by bargaining power and remains the same. The offline demand is increased due to the lowered price affected by the e-retailer’s enhanced bargaining power when offering online finance. Proposition 4(b) shows that, in Game H, adopting e-retailer finance reduces the online channel quantity and increases the offline quantity. This is because, in Game H, the eretailer does not have the first-mover advantage; thus, the impact of a reduced price on online quantity is less than Table 3: Summary of Equilibria (a) Scenario N Vertical Competition Game ( j V )

peNj*

Price

Profit

qeNj*

 2    4 1   

1    2   

qsNj *

4 1  2  2      N  2    4    

 2     2  4   2        N

Case 1:

Quantity

NV * e

2 1          2   N   N   2   

 2 1    

4  2+4 + 2 N 



N

2

N  2

 eNj*

2  2     N 8 1     2  4 + 2 N 

2 1    2     N  N   2 

 sNj*

p

p

NV * s

 cs  qsNV *   peNV * 1   N   ce  qeNV *

Vertical Competition Game ( j  V ) peBj * p

Bj * s

 2   

2  2  4   2 B 

2      2   

B

p

qeBj*

 2    4 1   

qsBj*

4 1  2  2     



 2    

̂

Profit

N

p

 2     N   2 

2      2   

(b) Scenario B Price

2  2  4   2 N 

p

Nj * s

Quantity

 2   

Horizontal Competition Game ( jH)

Bj * e

 erBj *

BV * e

B

 cs  qsNH *  1   N  peNH *  ce  qeNH *

Horizontal Competition Game ( j  H )

 2     B   2  2 1          2   B   B   2   

 2 1    

1    2     B   2  B

 2    4  

4  2+4 + 2 B 

2

NH * s

2



8 1    2    4  

 2    2  4   2        B B   2

B



2  2a     8 1     2    4   B  

2 1    2     B  B   2 

2 1    2a      B   2 

2

2

3

Price

 efBj *

2  2a    r 1    8 1     2    4   B  

2 1    2a    r 1     B   2 

 sBj *

p

p

peBj *

T cs  1  2 1   

psBj * qeBj*

̂

BV * s

 cs  qsBV *   peBV * 1   B   ce  qeBV *

T     1   

qsBj*

 Profit

Bj * er

 efBj *

    1   

 2 1   

 2 1   

 2T   1         cs  K    cs   M1T  M 2 

2 1     1         2  4   2  

1  r  T 2  2 2 1    T 2  2 2 1    rT 2  2 1   

1  r  2 4 2 2 1   1    2 4 2 2 1   1    r2 4 2 1   1   

T    cs  2 1   

 eBj *

 cs  qsBH *   peBH * 1   B   ce  qeBH *

 2 1   1   

2 cs 1  2   B  GpeBV *   peBV *  1     1      T  cs 1  2    1         cs  K    T 

Quantity

BH * s

2

 p  c  q   p 1     c  q ˆ  where K   2c 1     A 1    p    2  p  1     c   p 1       1     A  A  p  1    1   1  r    2 1    , A  c 1     c   p  1    ,   cs 1  2   cs 1     ce   , B  c 1     c   K , T     1         c  K     , G   1     c 1     c  , M  c 1  2        1    , M 2  cs 1  2   1         ce  K 1     , F  cs  2  4   2   ce  1    ,   F  4 1     1     c      K    F , and   1    c   c  c    sBj *

p

BV * s

 cs  qsBV *   peBV * 1   B   ce  qeBV *

2 2 1     1   

Bj

s

e

Bj e

s

e

e

BH * s

BH * s

s

Bj e

BH * e

B

e

BH * e

Bj

Bj e

s

e

e

2

e

s

s

1

e

2

s

,

s

s

e

j  N, B .

offline quantity, which causes an increase in offline demand, and a decrease in online demand. Proposition 4(c) illustrates that, in both of the two games, eretailer finance can increase the total market share (i.e., the online and offline demand in the aggregate), as showed by the total quantities in dual channels in Scenario B are greater than in Scenario N. This is because e-retailer finance can help the supplier develop the offline channel due to more capital resources availabilities. This shows the positive externality of the influence of e-retailer finance on offline demand. We use Figure 5 to show the value of e-retailer finance on market share. In Figure 5 and those that follow, without loss of generality, we set   0.8 ,   0.7,   0.25, r  0.15 , cs  0.14 , ce  0.10, and K  0.01 . From Figure 5, we find that when a supplier chooses to develop dual channels within the certain

range of customers’ online channel preference, the market share with e-retailer finance is greater in comparison with no financing. The positive effect of eretailer finance on market share gets more significant when the online channel preferences increase and when in Game V. When the supplier develops a single channel due to low online channel preferences, the market share when adopting e-retailer finance is larger than

Loan ratio

Optimal quantity

without financing, and the increasing extent due to financing access gets larger with higher online channel preferences. However, when the supplier develops

Interest rate

Figure 5: Value of E-retailer Finance on Market Share

Figure 6: Channel Structures with Financing Factors

a single channel due to a high online channel preference, the increasing extent of market share due to financing access gets lower with higher online channel preference. When the customer online channel preference is extremely large (i.e., greater than ˆ eBj and very close to 1), the market share when having financing access is even lower than without financing.

6. CHANNEL STRUCTURE ANALYSIS Previous sections examined and compared the effects of e-retailer finance on equilibria between Game V and Game H. We found that Game V under e-retailer finance benefits supply chain participants more than Game H regarding the profits and market share. Hence, in this section we mainly focus on Game V and examine the influential factors of channel structure (online versus offline quantity) including financing factors (i.e., interest rate and loan ratio), operational factors (i.e., cost difference and bargaining power), and consumer-related factors (i.e., channel preference and price substitution). As for numerical analysis, we use the same parameter settings as in previous sections. (1) Channel Structure with Financing Factors Figure 6 describes the impact of the financing factors of interest rate and loan ratio on the equilibria quantity between dual channels. It shows that when the loan ratio is higher, with an increasing interest rate, it is more likely that the online channel quantity is lower than the offline channel quantity. This is because the higher interest rate gives the supplier less room to utilize financing access to develop online channel. The higher interest rates increase the

4

financing costs of the supplier especially when the loan ratio is higher. The higher loan ratio indicates that supplier needs less online demand fulfilled to receive the cetain financing amount. Thus, the supplier will choose to reduce the online demand and increase offline demand to maximize her profit. (2) Channel Structure with Operational Factors Figure 7 compares the equilibrium quantity between the online and offline channel with the change in the operational factors of cost difference and bargaining power. From Figure 7, we find that only when the bargaining power and cost difference are below certain thresholds will the supplier want to develop dual channels. This is because the e-retailer’s extremely high bargaining power considerably reduces the supplier’s marginal profit in the online channel, which motivates the supplier to develop only the offline channel. When the cost difference is higher than a threshold, the supplier will only develop online channel due to the relatively high cost of the offline channel. In addition, Figure 7 shows that when the channel cost difference is relatively higher (lower), online channel quantity is greater (less) than the offline channel quantity. This is because the higher channel cost differences indicate lower online channel costs, which leads to the cheaper online price, and thus increases online demand. It also shows higher cost difference widens the bargaining power range, making online demand higher than offline demand. This is because the higher channel cost difference offers more room for e-retailer to utilize his higher bargaining power to motivate the supplier to implement dual channels and make online demand higher than offline’s. (3) Channel Structure with Consumer-related Factors Figure 8 describes the influence of the consumer-related factors of channel preference and price substitution on the equilibrium quantity between dual channels. Figure 8 shows that only in the event that customers’ preference difference between online and offline is not high can the supplier develop both of the dual channels, which supports previous findings. A higher online channel preference leads to higher online demand. Comparatively, the influence of channel preference on channel structure is greater than price substitution. The higher (lower) price substitution makes it more possible that online channel demand is higher (lower) than offline demand. This is because higher price substitution can increase the online channel price advantage and thus attract more online demand.

Bargaining power

Price substitution coefficient

7. AN EXTENSION: CREDIT FINANCING

Cost difference

Figure 7: Channel Structures with Operational Factors

Channel preference

Figure 8: Channel Structures with Consumer-related Factors

In the previous sections, we examined the online factoring financing scheme, in which the e-retailer serves as a downstream buyer to purchase products at a wholesale price and sells them for a retail profit. This is one of the e-retailer finance schemes widely adopted nowadays, in which the loan amount is mainly determined by the supplier’s accounts receivables. In practice, besides acting as an e-reseller, the e-retailer can also serve as an intermediary role to provide the e-platform for suppliers to sell products by themselves. For example, the e-retailers, such as Amazon.com and Orbitz.com in the United States and Tmall.com in China, pass orders from the customers to suppliers and charge an agreed referral rate (e.g., mostly 8% -15% on Amazon.com) based on each transaction (Akçura et al., 2015). This shows the platform economy, which uses online structures enabling a wide range of activities to develop a new digital economy (Kenney & Zysman, 2016). In these cases, when the supplier has the capital constraint, she can apply for immediate financing from an e-retailer and, upon approval of a credit loan, pay back the principal and interest at the end of the sales period. This credit financing scheme has also been commonly practiced by major e-retailers such as the ―Jingxiaodai‖ financing scheme offered by JD.com and ―the lending program‖ offered by Amazon.com (Chen, 2016).

In this way, the e-retailer can meanwhile provide the other form of a popularly used financing scheme, i.e., credit financing. Different from the

factoring finance, in this credit financing scheme, the retailer determines the loan amount based on the supplier’s capital gap, which is decided by the supplier’s initial capital and total production cost. This section examines pricing strategies with credit financing scheme and compares them with factoring financing. We use the superscript G to represent the credit financing scheme based on supplier’s capital gap. In credit financing, a capital-constrained supplier simultaneously decides the online price peBG on E-commerce platform and the offline price psBG in physical stores to maximize her total profits. The supplier applies for credit loan to fill in the capital gap between production costs and her initial capital, which is LG  ce qeBG  cs qsBG  K . We assume that the interest rate r in credit financing is as same as that in factoring financing. In addition, we introduce  to describe the referral rate, and assume the referral fee the e-retailer charges the supplier is based on the supplier’s online sales revenue through E-commerce platform. In this way, the supplier’s decision problem is how to maximize her profit through pricing optimization, as formulated in Eq. (8)-(9). max  sBG   psBG  cs  qsBG   peBG 1     ce  qeBG  LG r .

psBG , peBG

(8)

5

 sBG  psBG , peBG   0   s.t. ce qeBG  cs qsBG  K .  BG p  cs   s

(9)

Proposition 5 presents the equilibria of dual-channel pricing strategies in credit financing. Proposition 5: If   0,ˆ , the optimal prices of dual channels in credit financing are as follows:  peBG*   2    1      1     2 Ae (ce )  Be (ce , cs )  4 1  2 1      2 2     ,  BG* 2 2  ps   2 1    As (cs )    2      1     Bs (ce , cs )  4 1  2 1       

where ˆ satisfies 4(1  2  ˆ)  ˆ(8  ˆ)  0 , Am (cm )  cm 1  r 1  2  , Bm (cm , c m )   1  r   c m 1     cm   , and m  e, s . Based on Proposition 5, we can find the equilibrium quantity of online and offline are:  BG* As (cs )   2     2 Ae (ce ) 1     2 1  2      1    3   qe  4 1  2  1      2 2  .   BG* Ae (ce )   2      2 1  2  1           2 As (cs ) 1     1    q   s 4 1  2 1      2 2 

Corollary 4 describes the impact of interest rate on the equilibrium prices and quantities in online and offline channels. Corollary 4: The influence of interest rate on price and quantity. If   2    2 1   1     cs ce  1   2  3   1   and   4  4    , we have (a) peBG* r  0 , psBG* r  0 ；(b) qeBG* r  0 , qsBG* r  0 . Corollary 4a (4b) describes that if the cost ratio between offline and online is in a certain range and the referral rate is less than a threshold value, then both the online and offline price (quantity) are positively (negatively) influenced by the interest rate. The higher interest rate increases the supplier’s costs, which then push her to price higher in both of the online and offline channels, thus hurting demand. Corollary 5 describes the influence of online and offline cost on the equilibrium price and quantities in dual channels. Corollary 5: The influence of online and offline cost on price and quantity. (a) peBG* ce  0 , psBG* ce  0 ; peBG* cs  0 , psBG* cs  0 ； (b) qeBG* ce  0 , qsBG* ce  0 ; qeBG* cs  0 , qsBG* cs  0 . Corollary 5(a) shows that both the online and offline prices are positively influenced by the online channel cost. However, the online price is negatively influenced by the offline channel cost, while the offline price is positively influenced by its channel cost. The supplier increases online retail price to cover the increased online cost and raises the offline retail price to continue to attract online demand by limiting the number of customers that will switch to the offline channel. When the offline cost increases, supplier increases the offline retail price to cover the increased cost, and at the same time attracts more online demand through reduction of online price to maximize her total profits in dual channels. Corollary 5(b) shows that both the online and offline product quantities are negatively influenced by their own channel costs, while being positively influenced by the other channel cost. The higher own channel cost increases the retail price and thus reduces the demand of this channel, while the higher cost of the alternative channel motivates customers to switch to this channel. When comparing the credit financing and factoring financing schemes, interestingly, we find that the influences of channel cost and the interest rate on pricing are different, according to Corollary 1 and 2 from factoring financing and Corollary 4 and 5 from credit financing. In terms of the interest rate, we find that both online and offline prices decrease with interest rate in factoring financing, while they increase with interest rate in credit financing. In terms of channel cost, we find that both the online and offline prices in factoring financing decrease (increase) with the unit online (offline) cost. However, in credit financing, both the online and the offline prices increase with the unit online cost; and the offline price increases with the unit offline cost, however, the online price decreases with the unit offline cost. This shows the additional functions of the e-retailer in factoring financing as compared with only serving the intermediary role in credit financing, and it also reflects the supplier’s different pricing mechanisms under different financing schemes. The influence of channel cost and the interest rate on the optimal dual-channel prices is different in different financing schemes. The interest rate and online cost are indirect factors for the supplier’s online pricing in factoring financing because she only decides the offline price, while these are direct factors in credit financing where the supplier decides the price for both channels.

8. CONCLUDING REMARKS This study enhances the nascent literature on e-retailer finance by examining its impact on dual-channel pricing strategies when a supplier faces capital constraint. The novelty of this study mainly resides in the fact that we examine the e-retailer’s dual roles in online finance and in online distribution offering, as well as in its influence on channel structure. In addition, from the perspective of supplier’s different strategic roles in dual channels, we examine the interaction between pricing competition and online finance in horizontal and vertical competition games. Through our modeling analysis, we establish the following principal results. First, we find that the online financing offering has value-added effects evidenced by the increased e-retailer’s profit and higher total demand of online and offline channels. The e-retailer’s increased profits come from the additional interest revenue, which can offset the lowered online retail profit. Online finance provides a win-win situation for both the e-retailer and the supplier. Second, for the e-retailer, competing with supplier vertically can bring higher profit, which shows the first-mover advantage of announcing the pricing earlier than supplier. For the supplier, vertical competition can also bring her more total demand in online and offline, which shows the effect of supplier’s market share increase. Third, the channel structure and associated pricing strategies are influenced by various financing, operational, and consumer-

6

related factors. The influence mechanism is different in different financing schemes (factoring or credit financing). Financing schemes choice is determined by the different strategic role the e-retailer would like to act as, i.e., intermediate platform or distribution platform. This study provides managerial implications of the interface of dual-channel operations and online finance for both the e-retailer and supplier. The eretailer should understand the value-added process of offering financing services to the capital-constrained supplier and the profitability mainly comes from online finance initiatives, which necessitates the sacrifice of some sales profits. Hence, how to optimize the overall profits from a global perspective and then achieve an equitable balance between operations and finance is crucial for e-retailer’s implementation of online finance. Organizational coordination between various departments such as marketing, sales, and financing in e-retail companies is needed when facing the profits switch from online sales to online finance. In addition, the e-retailer needs to understand the different influence mechanisms of channel costs and interest rate on pricing and quantity when develops different financing schemes. For the supplier, adopting e-retailer finance can provide her with more capital to operate an online channel and to implement a dual-channel strategy. Comparatively, she can focus more on online channel development to utilize the e-retailer as a distribution platform to reach more online customers. Earnings from online wholesale revenue through participating in a vertical competition game with the e-retailer can bring more total demand from online and offline channels. Sequentially announcing the retail price first online by the e-retailer and second offline by the supplier can bring more benefits to both of them compared with announcing the retail price in different channels simultaneously. The development of dual-channel structures can be guided through adjusting operational factors such as channel cost and bargaining power, implementing promoting actions to influence customers’ channel preferences and substitution levels, and adjusting such financing factors as loan ratio and interest rate. This study provides guidelines for e-retailers to serve in two roles—as a reseller and as a financing service provider together—and finds a feasible solution for a supplier to utilize an e-retailer’s distribution platform and financing offerings to develop dual channels. This study shows the benefits of the platform economy can be further improved when online SCF is offered. This study has several limitations, which provide directions for future research. First, our study only considers indirect online and direct offline channels. Future research can examine the pricing issues under different channel structures such as omni-channels, mobile channels, or hybrid channels. In addition, our study mainly compares two e-retailer finance schemes. A comparative analysis with other financing schemes initiated by e-commerce platforms can also be worth studying. Further, dynamic pricing mechanisms and negotiation mechanisms between the offline and the online retailers can also be analyzed in the future studies.

ACKNOWLEDGMENTS This research is supported by the National Natural Science Foundation of China (Grant nos. 71872200 and 71372191), Beijing Natural Science Foundation (9192021), and the Fundamental Research Funds for the Central Universities.

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