Influence of Customer Acceptance of Online Sales

Influence of Customer Acceptance of Online Sales Channel on Firm Profits under Channel Competition Rofin T M, Biswajit Mahanty Industrial and Systems Engineering IIT Kharagpur Kharagpur, India [email protected] [email protected] Abstract - This paper focuses on the influence of customer acceptance of online sales channel on the profit of firms when the online and the traditional ‘Brick and Mortar’ channels compete. Profit is calculated when the channels are engaged in Bertrand competition and when the channels are integrated. Influence of customer acceptance of online channel on the firm profit is analyzed for different categories of products. Firms enjoy better profits when the channels are integrated for those products for which customer acceptance of online channel is high. Keywords: Dual channel, Bertrand competition, customer acceptance of online channel.

The rest of the paper is organized as follows. A brief review of literature is given in Section 2. Section 3 describes the Bertrand competition and channel integration models developed for the purpose. Section 4 presents a numerical example and Section 5 gives the conclusions and managerial implications. II. LITERATURE REVIEW A. Dual Channel Competition Dual channel competition has been studied from different perspectives. Dual channel competition from the service perspective was studied by considering product availability as the service dimension of retail channel and delivery time as the service dimension of online channel [2]. Pricing policies in a centralized and decentralized dual channel supply chain have been studied by considering level of retail service and degree of customer loyalty [3]. Cai et al. [4] looked at the influence of price discount contracts in dual channel competition using Stackelberg game model. Pricing in dual channel scenario was studied using fuzzy demand and game theory [5]. Effect of demand uncertainty on firm’s profit and its retail service level was addressed by Hu and Li [6]. Demand uncertainty is also addressed by using a stochastic demand function by Fernando and Federgruen [7]. Some studies warn organizations about operating through a direct channel. For instance Lu and Chen [8] examined the problems of selling through online channel and how it leads to channel conflict. But on the flip side, some studies give insights about the supporting role of direct internet channels. The strategic role of internet channel in complementing the retail channel was studied by Ruan and Kumar [9]. Li et al. [10] studied pricing decisions in dual channel supply chain by considering heterogeneous customer expectations.

ȱ. INTRODUCTION Rapid internet penetration and change in customer shopping behavior has augmented e-commerce in the recent years. More and more organizations are selling through online channels because of huge online market potential. Though there is a demand amplification due to the introduction of direct online channel, demand in the direct online channel is taking up a portion of demand in the traditional ‘brick and mortar’ channel [1]. Due to this bifurcation of sales, there is competition between online sales channels and traditional channels. This competition is termed as ‘Dual Channel Competition’. In the coming years dual channel competition is predicted to be intense, as more and more organizations will employ multi-channel strategy. Considering the market perspective, competition level will be determined by channel preference of customers. As channel acceptance precedes channel preference, customer acceptance of the online channel is an important area of study. In our research we focus on the influence of customer acceptance of online channel on the profit of firms under two different scenarios: 1) When the online and the traditional channels are independent 2) When the online and the traditional channels are integrated. We also study whether different product categories influence such decisions of channel independence or integration.

c 978-1-4799-8792-4/15/$31.00 2015 IEEE

Zhang and Yao examined returns in dual channel scenario. They assumed risk averse manufacturer and risk averse retailer [11]. Li and Ma used a Bertrand model and analyzed

875

the complexity of the game in a dual channel scenario [12]. Dual channel competition has been studied by applying game theoretic models and other analytical tools. B. Customer Acceptance of Online channel Customer channel choice is the driver of dual channel competition and it has been found that channel choice depends on several factors like price, product availability, product category, perceived risk, convenience, information security, easiness to return etc. [13]. So while a customer chooses a channel, she mentally accounts for these factors and takes a decision. Customers’ convenience orientation also plays a significant role in channel preference [14]. Chiang [15] studied the dual channel scenario using Stackelberg game and concluded that introduction of direct channel is a means for controlling the traditional channel. In his study he used the variable Customer acceptance of online channel denoted by ‘ θ ’. Higher the θ value, higher will be the online sales. So we can use this construct as an antecedent of online channel preference. Customer acceptance of online channel was later renamed as product web fit [16]. There have been studies to estimate customer acceptance of online channel. Liang and Huang [17] estimated the values of θ for different product categories (Table I). Table I. Customer Acceptance of online channel ( θ ) Product

Book

Shoes

Toothpaste

DVD player

Flowers

Food Items

θ

0.904

0.769

0.886

0.787

0.792

0.784

Here all the products considered have a θ value of more than 0.75. This study was done on 1998 when the e-commerce was not so popular. So Kacen et al. [18] re- estimated θ value for the same product categories and found that traditional channels are preferred over online channels. In other words value of θ lies between 0 and 1. Literature is scant on dual channel competition from a customer utility perspective. Yan [19] studied how profit is influenced by θ to understand pricing strategies. In our study we check the influence of θ on total profit on the basis of product categories. There is also a need to study the relationship between product category and channel strategy to optimize total profit under dual channel competition. ȱȱȱ. MODEL DEVELOPMENT We consider a firm selling its product through an exclusive traditional retailer as well as through a direct channel. The

876

direct channel is either the firm’s website or an online marketplace with which the firm has a contract. The following notations are used to develop the mathematical model: Qt = Quantity demanded in traditional channel

Qo = Quantity demanded in Online Channel pt = Price in traditional channel po = Price in online channel

θ = Customer acceptance of online channel v = Customer valuation m = Money utility π t = Profit of traditional channel π o = Profit of online channel π CB = Total Channel Profit under Bertrand competition π c = Total profit when the channels are integrated ct = Marginal cost incurred by traditional channel co = Marginal cost incurred by online channel k = Proportionality constant Demand functions First we derive demand functions for online channel and traditional channel (See Appendix) using utility theory assuming a continuous scale for customer valuation. We consider customer valuation and money utility in addition to customer acceptance of online channel. We have demand through the traditional channel as

ª m( pt − po ) º Qt = k « v − (1 − θ ) »¼ ¬ And demand through the online channel as

ª m( pt − po ) mpo º Qo = k « − θ »¼ ¬ (1 − θ ) A similar approach for deriving the demand functions is used by Moon et al. [20].Using these demand functions we analyze the following two scenarios: 1) Competition when channels are independent, and 2) Competition when channels are integrated. Case 1: Optimum profit of the firm when online channel and traditional channel is engaged in Bertrand competition Channels can compete on the basis of aspects such as price or service competitions [21]. Among the different forms of competition, price competition is the most common form [22]. In our work, we consider Bertrand competition model for price competition between online and the traditional channels.

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

In this model, each channel makes decisions simultaneously and independently. The objective of each channel is to choose a profit maximizing price. Profit for traditional channel is

Case 2: Optimum profit of the firm when online and traditional channels are integrated

Similarly profit for online channel is

When online and offline channel are integrated, the decision making is centralized. Here price for online and offline channels will be selected so as to maximize the profit for integrated firm. The profit function for integrated channel contains both online channel profit and offline channel profit. This profit function is optimized to find out the price in online channel and price in offline channel which will optimize the total profit.

π o = ( po − co )Qo

Total Profit when the channels are integrated

ª m( pt − po ) º π t = ( pt − ct )Qt Where Qt = k « v − (1 − θ ) »¼ ¬ ª m( pt − po ) º (1) Thus π t = k ( pt − ct ) « v − (1 − θ ) »¼ ¬

ª m( pt − po ) mpo º Where Qo = k « − θ ¼» ¬ (1 − θ ) ª ( p − po ) po º − » (2) Thus π o = km( po − co ) « t θ ¼ ¬ (1 − θ ) Differentiating π t on pt and then π o on po and applying ∂π t ∂π first order conditions i.e. = 0 and o = 0 ; we will get ∂pt ∂po 2v(1 − θ ) 2ct + co * Bertrand Nash Equilibrium as p tB = + m(4 − θ ) (4 − θ ) θ v(1 − θ ) 2co + θ ct * + and p oB = m(4 − θ ) (4 − θ ) Total channel profit under Bertrand competition is given by π CB = π t + π o (3) Substituting

p*tB and p*oB in (3) to find the maximum channel

π c = ( pt − ct )Qt + ( po − co )Qo

(5)

For maximization of profit with respect to traditional channel

∂π c =0 ∂pt 2kmpt km = kv − [ 2 po + ct + co ] (1 − θ ) (1 − θ )

price

On simplification we will get the traditional channel price corresponding to maximum profit

pt =

v (1 − θ ) 1 − [ 2 po + ct + co ] 2m 2

(6)

For maximization of profit with respect to online channel

∂π c =0 ∂po 2p c o = 2 pt − ct + o 2 θ

price

profit under Bertrand competition Proposition 1: The optimum profit for firm under Bertrand competition between online channel and traditional channel, is * π CB

1 = [ A + k * B] m(4 − θ )

(4)

ª [−2v(θ − 1) + mco + mct (θ − 2)]2 º Where A = k « » (θ − 4)(θ − 1) ¬ ¼ [ m(θ − 2)co + θ (v − vθ + mct )] θ (θ − 1)(θ − 4)

2

B=

On simplification we will get the online channel price corresponding to maximum profit

po* = θ pt −

θ ct 2

+

co 2

(7)

Substituting the value of po* in to (6)

pt* =

c c (1 − θ ) v(1 − θ ) − o + t 2m(1 + θ ) (1 + θ ) 2(1 + θ )

Similarly substituting the value of pt* into (7)

po* =

θ co cθ2 c θ v(1 − θ ) − − t + o 2m(1 + θ ) (1 + θ ) (1 + θ ) 2

Substituting the values of pt* and po* into (5) equation, we will get optimum value of profit.


877

Proposition 2: The optimum profit for the firm w when online channel and traditional channel are integrated is:

k[ P + Q + R + S ] 4m(1 − θ )(1 + θ )2 Where P = [ m 2 (1 − 2θ + 5θ 2 ) co2 ]

π c* =

(8)

Q = ª¬ 2mθ co (4v (θ − 1)θ + m(3θ 2 − 6θ − 1)ct º¼ R = θ [v 2 (θ − 1) 2 (1 + 3θ ) + 2 mvct (5θ 3 − 3θ 2 − θ − 1)] S = m 2 ct2θ [1 + 2θ − 3θ 2 + 4θ 3 ] ȱV. NUMERICAL EXAMPLE E We make the following assumptions for tthe numerical example: 1) Marginal cost of online channel is more than marginal cost of traditional channel consideringg the additional cost incurred by the online retailer in she hass to handle the product returns. 2) Customer valuation is uniformly distributed as in Chiang et. al. [15] With these assumptions we take the followiing values for numerical substitution for comparing the profiits of Bertrand Game scenario and integrated channel scenario

We only consider Class A and Class C B products since these product categories are preferred by b firms to sell through online sales channels. Products falling under Class A are low involvement goods like books, e--books, computer accessories (e.g. pen drives), and mobile acccessories (e.g. memory cards) etc. Apparels are considered as an a experience good under the SEC (Search, Experience, and Credence) C classification. But the enormous sales growth of o apparels through online channels compels us to treat it as a Class B product. Apart hones, software and antivirus from apparels, we treat mobile ph packages, Footwear etc. as Classs B products. Class C and Class D products (e.g. furniture or refrigerators) are usually not sold online due to their inherently technological or dimensional complexity. Also hig gh involvement products like luxury goods (e.g. ornaments) are a usually not sold through online channels. In Fig. 1 profit of the firm selling class A products is plotted against θ when the chan nnels are engaged in Bertrand competition. Fig. 2 shows the saame for class B products. In Fig. 3, integrated channel scenario is considered and firm profit is plotted against θ for Claass A products. Fig. 4 shows the profit of the firm for Class B products in the integrated channel scenario.

k = 1, m = 1, co = 2, ct = 0.5, v = 3 ms of θ , we get Substituting the values to find out profit in term the following Profit function under Bertrand ccompetition in terms of θ :

π CB =

1 + ( 4θ − θ 2 − 2 ) (1 − θ ) (θ − 4 )

2

2

θ 6.06θ + 2.25θ + 0.6875 − 0.68θ − 2.06θ πc = θ (1 − θ )(1 + θ ) 2

ms of Profit function under integrated channels in term 3

4

2

Fig. 1. Profit of the firm for Class A produ ucts under Bertrand competition

To analyze profit for different product categoriies we classify products based on θ (Table II).This classification of products is based on the features of the product andd sales growth through online channel. Table II. Classification Class A Class B Class C Class D

Range of 0.75-1 0.5-0.75 0.25-0.5 0-0.25

θ

Fig. 2. Profit of the firm for Class B produ ucts under Bertrand competition

878


Fig. 3. Profit of the firm for class A products when channelss are integrated

Fig. 4. Profit of the firm for Class B products when channelss are integrated

Profit is positive and exponentially increasingg for Class A products when the channels are engaged in Bertrand competition. Within the Class A product cateegory itself we can see variation in profit with respect to θ . Proofit increases as θ increases. Under Bertrand competition, Claass B products show a U shaped profit curve. When the channels are integrated, firm is enjoying a higher profit. Forr both Class A and Class B products, profit is exponentiaally increasing confirming that integration leads to better system m profit. V. CONCLUSIONS AND MANAG GERIAL IMPLICATIONS This study relates total channel profits w with customer acceptance of online channel. We first modeled the competition between online channels and tradiitional channel using the price competition model in gam me theory i.e. Bertrand competition. After that we considereed the scenario where both channels are integrated. Expressiions for profit were derived for both models.

c based on customer We classified products into four categories acceptance of online channel. For products with higher acceptance of online channel, Firrms enjoy better profits when the channels are integrated than n when they are allowed for independent price competition. More specifically, for firms selling products like books, e-bo ooks, mobile accessories and computer accessories, it is better to integrate the channels than allowing Bertrand competition. The T same conclusion applies for firms selling products likee Apparels, mobile phones, software and antivirus package, fo ootwear etc. Under Bertrand competition, within each category y of product there is variation in profit. For the products having h very high customer acceptance of online channel proffit is exponentially increasing whereas for products having high customer acceptance of online channel profit is showing U- shaped behavior. But for both class A and class B products profits are increasing exponentially when channels are integrated. Due to the exponential growth h rate of e-commerce, it is evident that firms will go forr online selling. There are different options for firms to choose an online mode of sales r out to customers, 2) 1) fully owned website directly reaching collaboration with online marrketplaces like Alibaba or Snapdeal, and 3) An e-tailer who buys the stock from the firm and sells to the customers. In the first two options the firm has direct control over the price and promotion p strategies whereas in the third option the e-tailerr decides on the price and promotion strategies. We have no ot considered sales through an e-tailer in this work. In the first option, i.e. when the firm fi is selling through its own website, the traditional retailer is i in direct competition with the firm’s online selling website. When the firm decides to go for an online marketplace, tradittional retailer competes with online marketplace. In both thesse forms of competition, the price is decided by the firm. Wh hen the firm decides the price independently for the exclusive retailer r and online channel, it leads to Bertrand competition. In n the integrated scenario, the firm decides the price with an objective o of maximizing total system profit considering exclusive retailer as well as online platform. The results show that,, irrespective of whether the firm is selling through a fully owned o website or an online marketplace, it is better for the firm to choose price b the channels. considering the demand through both The study can be extended by considering sequential competition models like Stackellberg model. The study can also be extended by considering an e-tailer to which the firm sells the product. We also feel th hat determination of customer acceptance of online channel values for more product categories is essential. Studiess on channel structure for products having low acceptance of online channel are also to be carried out.


879

direct online channel,” Quant. Mark. Econ., vol. 4, no. 3, pp. 289–323, 2006. ACKNOWLEDGMENT

[10]

F. Li, J. Liu, and Y. Wei, “Pricing Decision in a DualChannel System with Heterogeneous Consumers,” in Proceedings of 3rd International Conference on Logistics, Informatics and Service Science, 2015, pp. 307–314.

[11]

L. Zhang and Z. Yao, “Decision Support Systems III Impact of Decision Support Systems for Global Environments,” vol. 184, pp. 118–130, 2014.

[12]

T. Li and J. Ma, “Complexity analysis of dual-channel game model with different managers’ business objectives,” Commun. Nonlinear Sci. Numer. Simul., vol. 20, no. 1, pp. 199–208, Jan. 2015.

[13]

D. D. Schoenbachler and G. L. Gordon, “MultiǦchannel shopping: understanding what drives channel choice,” J. Consum. Mark., vol. 19, no. 1, pp. 42–53, Feb. 2002.

[14]

T. Kollmann, A. Kuckertz, and I. Kayser, “Cannibalization or synergy? Consumers’ channel selection in online–offline multichannel systems,” J. Retail. Consum. Serv., vol. 19, no. 2, pp. 186–194, Mar. 2012.

[15]

W. K. Chiang, D. Chhajed, and J. D. Hess, “Direct Marketing , Indirect Profits : A Strategic Analysis of Dual-Channel Supply-Chain Design,” Manage. Sci., vol. 49, no. August 2014, pp. 1–20, 2003.

[16]

R. Yan and R. Yeh, “Consumer’s online purchase cost and firm profits in a dualǦchannel competitive market,” Mark. Intell. Plan., vol. 27, no. 5, pp. 698– 713, Jul. 2009.

We would like to acknowledge UGC India for supporting the study under UGC JRF scheme under the grant number 1528(NET-DEC 2012). We also thank the anonymous reviewers for their valuable comments. REFERENCES [1]

[2]

E. Biyalogorsky and P. Naik, “Clicks and mortar: The effect of on-line activities on off-line sales,” Mark. Lett., vol. 14, pp. 21–32, 2003. K.-Y. Chen, M. Kaya, and Ö. Özer, “Dual Sales Channel Management with Service Competition,” Manuf. Serv. Oper. Manag., vol. 10, no. 4, pp. 654– 675, Oct. 2008.

[3]

B. Dan, G. Xu, and C. Liu, “Pricing policies in a dualchannel supply chain with retail services,” Int. J. Prod. Econ., vol. 139, no. 1, pp. 312–320, 2012.

[4]

G. (George) Cai, Z. G. Zhang, and M. Zhang, “Game theoretical perspectives on dual-channel supply chain competition with price discounts and pricing schemes,” Int. J. Prod. Econ., vol. 117, no. 1, pp. 80– 96, Jan. 2009.

[5]

F. Soleimani, “Optimal pricing decisions in a fuzzy dual-channel supply chain,” Soft Comput., no. Zimmermann 2000, 2014.

[6]

W. Hu and Y. Li, “Retail service for mixed retail and E-tail channels,” Ann. Oper. Res., vol. 192, no. 1, pp. 151–171, 2012.

[7]

F. Bernstein and A. Federgruen, “Decentralized Supply Chains with Competing Retailers Under Demand Uncertainty,” Manage. Sci., vol. 51, no. 1, pp. 18–29, 2005.

[17]

L. Hsiao and Y. J. Chen, “The perils of selling online: Manufacturer competition, channel conflict, and consumer preferences,” Mark. Lett., vol. 24, no. 3, pp. 277–292, 2013.

T.-P. Liang and J.-S. Huang, “An empirical study on consumer acceptance of products in electronic markets: a transaction cost model,” Decis. Support Syst., vol. 24, pp. 29–43, 2000.

[18]

J. J. Kacen, J. D. Hess, and W.-Y. Kevin Chiang, “Bricks or Clicks? Consumer Attitudes toward Traditional Stores and Online Stores,” Glob. Econ. Manag. Rev., vol. 18, no. 1, pp. 12–21, 2013.

[19]

R. Yan, “Pricing strategy for companies with mixed online and traditional retailing distribution markets,”

[8]

[9]

880

N. Kumar and R. Ruan, “On manufacturers complementing the traditional retail channel with a


J. Prod. Brand Manag., vol. 17, no. 1, pp. 48–56, 2008. [20]

[21]

[22]

Y. Moon, T. Yao, and T. Friesz L, “Dyynamic Pricing and Inventory Policies: A Strategic Annalysis of Dual Channel Supply Chain Design,” Seerv. Sci., no. February 2015, 2010. A. . Ha, L. Li, and S.-M. Ng, “Pricee and Delivery Logistics Competition in a Supply Chhain,” Manage. Sci., no. March 2015, 2003. S. Yang, C. Victor, Y. Zhang, and JJ. Zhu, “Price competition for retailers with profit and revenue targets,” Int. J. Prod. Econ., vol. 154,, pp. 233–242, 2014.

vti

vo voi v ii

nd = k * Utility Demand ∝ Utility Deman

Where k = constant of proportion nality Demand in traditional channel

ª

Qt = k (v − vιι ) = k «v −

¬

m( pt − po ) º (1 − θ ) »¼

Demand in online channel

ª m( pt − po ) mpo º − θ »¼ ¬ (1 − θ )

Qo = k ( vιι − voι ) = k « Case 2: vti < voi

vti < voi v ii < vti < voi

Appendix

v vt

n is more than v ιι buys from Those customers whose valuation traditional stores. To find out how much demand is there in each channel we have assumed demand to be proportional to the utility derived.

Customer valuation Customer valuation of product soold in traditional channel Minimum customer valuation to ppurchase through traditional channel Customer valuation of product soold in online channel Minimum customer valuation to ppurchase through online channel Inflexion point in valuation

Two cases are considered for deriving the demand function. In first case customer valuation of traditional chhannel is more than customer valuation of online channel. In thhe second case customer valuation of online channel is moree than that of traditional channel. Case 1: vtι ≥ vιo

vtι ≥ vιo vιι > vtι > vιo

Customer valuation is illustratted as a continuous scale and is segmented into three zones. A,, B and C. Zone A customers doesn’t buy anything since theiir valuation has not reached either vti or voi . Zone B custo omers buy from traditional channel as their valuation has crossed the inflexion point v ιι . Zone C customers also buy fro om traditional channel since their valuation has not reached voi . Thus demand in the traditional ch hannel

Qt = k (v − vtι )

= k (v − mpt ) Demand in the online channel is zero in case 2. So there is no competition in case 2. So onlly consider case 1 for our analysis.

Customer valuation is illustrated as a continuuous scale and is segmented into three zones. A, B and C. For those customers belonging to zone A, their valuatioon is less than and that segment of customers neithher buy from v ιo traditional channel nor from online channel. Thhose who are in zone B derives utility from online channel aand buy from online channel. Those who are in zone C also buuy from online channel since their valuation is less than inflexion point v ιι .


881