论文部分内容阅读
The impact of SEARCH COSTS on the online-offline competition UNDER SHOWROOMING
Abstract More and more consumers choose multiple purchase channels to compare product quality， price and other characters when shopping. In particular， the behavior of consumers experiencing products in offline stores and buying online channels is called as showrooming. In this context， different shopping channels will generate different search costs. When the channel price gap is small， search costs will become one important factor to determine the consumer channel selection. The results show that the offline retailer gets more benefits when the online search cost is greater than the offline one. Moreover， when showrooming exists， the online retailer should try to reduce the consumers’ return cost to ensure more profits regardless of the search costs. When showrooming does not exist， the offline retailer can increase its profit by reducing its search cost and the online retailer does not have to reduce the search cost when the return cost borne by itself is relatively large.
Keywords： Retailing， Showrooming， Search costs， Consumer returns
The impact of SEARCH COSTS on the online-offline competition UNDER SHOWROOMING
1. Introduction
With the continuous expansion of shopping scale， the marketing mode combining traditional retail channels and electronic channels becomes the main stream of all industries. According to the survey of Rodrguez-Torrico， Cabezudo， & San-Martn （2017）， more and more consumers are willing to place orders online and experience offline instead of choosing one shopping channel to buy directly. We refer to the behavior of experiencing products offline and buying online as“showrooming”.
In real life， showrooming phenomena can be seen everywhere. Many retailers， for example， Uniqlo， not only have their own online retail stores， but also promote scanning code purchase in offline retail stores. When consumers experience products offline， they can scan the products’ code on the product to find the purchase links online. Apple Inc opened offline experience store to attract consumers while many authorized retailers such as JD.com and Suning attract customers through lower price rather than the experience. Many consumers may experience products offline and then switch to online platforms to buy them. At this point， offline stores managed by Uniqlo and Apple Inc act as showroom.
The difference between online and offline search costs is also a major consideration for consumers. According to Bing （2018）， lower search costs for consumer will aggravate showrooming and intermediate search costs may not benefit to traditional retailers’ profits. Nuria Viejo （2018） found that most people think online shopping costs less time and they are more willing to shopping online. Wang （2021） concluded that when the amount of information obtained by searching is so large that consumers need to pay more search costs， consumers will choose to buy at random. The previous studies showed that different search costs may have an impact on showrooming and retailers’ profits. Therefore， it is important to consider the relationship between search costs and consumers’ channel selection. We also study the impact of search costs gaps on retailers’ profits and find that online retailer needs to reduce the search costs gaps in order to make profits. Return， as part of the process of online shopping， affects consumer experience and subsequent purchase behavior. Feng and Pei （2015） concluded that retailers can increase their sales and profits by setting a higher price when they allow returns. Zhang （2018） found that a loose return policy is conducive to encourage follow-up purchase behavior for consumers who have experienced returns. This article studies consumer returns and shows that the cost of returns can influence retailers’ profits.
This paper focuses on the impact of different search costs on retailers' profits with the addition of consumer returns. The innovation of this article is to consider both consumer returns and the impact of search costs on retailers. Firstly， this paper focuses on the relationship between different search costs and the choice of shopping channels under showrooming. Secondly， this paper considers the case where consumers are allowed to return products and the impact of the return cost on retailers' profits. The difference between this paper and other papers on showrooming is that this paper takes into account the situation of returning products and the cost that consumers and retailers have to bear in the case of returning.
The contribution of this paper lies in the combination of consumer returns and showrooming， which has rarely been combined in previous literature. And this paper takes search costs as an important factor in research while other literatures pay less attention to search costs. We then attempt to solve the following problems：
1）How does showrooming affect retailers pricing？
2）How does the gap in online and offline search costs influence consumers’ behavior？
3）How does the gap in online and offline search costs affect retailers’ prices and profits？
We find that the offline retailer gets more benefits when the online search costs is greater than the offline one. Therefore， the online retailer needs to find ways to reduce the search costs of consumers if he wants to make more profits. Moreover， when showrooming exists， the online retailer should try to reduce the return cost borne by consumers to make more profits. When showrooming does not exist， the profit of the offline retailer will decrease with the increase of the search costs gap. And the profits of the online retailer will increase with the increase of the search costs gap when the return cost borne by the online retailers is relatively small. Therefore， the offline retailer can increase its profit by reducing its search cost and the online retailer does not have to reduce the search cost when the return cost borne by itself is relatively large. The rest of the paper is organized as follows. Section 2 reviews the related literature and clarifies our contributions. Section 3 presents the model and Section 4 is analysis and findings. Section 5 concludes and discusses potential future research directions.
2. Literature review
Our research is related to three aspects： the major factors that influence competition between online and offline retailers， the impact of search costs on consumers’ channel selection and the effects of showrooming on consumers and retailers.
Many scholars have studied the factors that influence competition between online and offline retailers. Choi et al. （2019） found that both discount pricing and bundling have positive effects on the daily sales of the applications. Deufel et al. （2019） showed that the payment choice has a strong influence on consumers’ channel selection. Herhausen et al. （2015） proved that the advantages of online and offline channel integration are more than the advantages of channel separation. Jing and Bing （2018） believed showrooming has increased competition among retailers and reduced profits for online and offline retailers. Loginova （2009） believed that customers with high valuation are more willing to shop offline， while customers with low valuation tend to the channel of showrooming. Mehra and Kumar （2013） found that increasing online search matching difficulty and charging consumers for showrooming could help offline retailers improve profits. Ofek et al. （2011） examined the circumstances under which two offline retailers can operate an online store. He found that if the differentiation among competing offline retailers is small， the operation of online stores could assist their offline stores and provide a platform for displaying offline goods. However， when the gap is big， only one can run an online store and make little profit. Thomas et al. （2019） found that the reviews information obtained from online search can influence consumers' purchase intentions. This paper considers several factors when studying the competition of retailers， including showrooming and consumer returns.
Much attention has been paid to search costs. Arora and Singha （2017） found that the offline experience and the guidance of sales staff would drive customers to the offline store. Online search costs and online shopping services will have a certain impact on online sales. Fishman and Levy （2015） analyzed that lower search cost decreases the market share of lower-quality companies and increases the market share of higher-quality companies. Gilgenbach et al. （2015） found that retailers lowering search costs does not always increase prices. Different search costs will cause consumers to choose different consumption channels. Hong and Shum （2006） used search costs models to assess the distribution of search costs and prices. Strebel et al. （2004） believed that different channels have substitution effect in the search stage， and it is necessary to integrate various channels to investigate the channel selection. Zhao et al. （2018） explored the influence of price discounts on consumer search behaviors and the moderate effect of retailers' online reputations. In this paper， we study the relationship between the search costs gap and the retailers’ profits when consumer returns are allowed under showrooming. Many studies have explored the showrooming behavior. Adam et al. （2015） believed that the behaviors and strategies of professional sales staff in offline stores will offset the negative influence brought by showrooming. Basu and Basak （2017） considered the sales effort and found that with the increased impact of the showrooming， the profits of online and offline retailers will decrease. Chiu and Hsieh （2010） regarded the showrooming as free-riding behavior and studied the driving factors， pulling factors and impeding factors of consumers’ online information search and offline purchase. Gensler and Neslin （2017） found that the quality of products and the waiting time of services positively affects the showrooming behavior. Mehra et al. （2017） first tried to counter showrooming with a price-matching strategy but this would lead to lower profits for offline retailers. Mehra ea al. （2013） studied three strategies to help offline retailers resist showrooms： price matching， making it harder to find products， and charging for offline experiences. The results showed that price matching does not increase profits for offline retailers， only the latter two can. Rapp et al. （2015） showed that the behavior of good salespeople and store level can offset the negative impact of showrooming. Sharma et al. （2000） believed that the emergence of showrooming behavior affects both online and offline retailers. Soh and Markus （2006） found that consumers would first experience the product offline and then search online for the product through mobile clients and complete the transaction. This paper considers showrooming when the consumer return is allowed. The return cost borne by consumers can have an impact on retailers’ profits.
Through reviewing relevant literatures， we find that there is almost no research on how search costs affect consumers’ channel selection. This paper focuses on the impact of different search costs when consumer returns are allowed. And this paper takes the cost that consumers and retailers have to bear in the case of returning into consideration. We find that the offline retailer gets more benefits when the online search cost is greater than the offline search costs. Moreover， when showrooming exists， online retailers should try to reduce the consumers’ return cost to ensure more profits. We also find that retailers' profits are inversely related to the gap in online and offline search costs.
3. Problem statement
We consider of an online retailer and an offline retailer that sell an identical product at different price and . The retailers set prices simultaneously and consumers can easily get the price information with no fee from the two retailers. The offline retailer sets up a physical store to sell products by providing consumers with experience services. The online retailer displays the products’ information on the website and sells products online. Consumers can choose to go to the physical store to experience the products and buy them offline or they can browse the products’ information online and buy them directly online. There are also quite a few consumers choosing to experience products offline to resolve their match uncertainty and then buy online. Under the circumstance， the physical store plays the role of “showrooming”. The offline retailer provides additional services to consumers and need to pay service cost， so we assume that . The consumer perceives a positive valuation for the product before experiencing the product. After experiencing the product offline or browsing the products’ information online， the probability of the product meeting consumers' expectations is （）. The consumer who is not satisfied with the product after browsing can choose not to buy it. For simplicity， consumers can choose to buy directly online and may return the goods due to the match uncertainty， denoted as channel E. Besides， consumers can purchase directly from offline retailers， denoted as channel T. In addition， consumers go to the offline store to experience the product before ordering online， denoted as S. We assume that consumers who have experienced the product in the offline store will be satisfied with the product， and if they buy it， they will not return it. Consumers who choose S and T channels can know exactly whether they are satisfied with the product. Consumers who buy the product directly online without experiencing it may return the product to the online retailer. Consumers returning goods need to bear the freight cost. We use to indicate the ratio of return cost to the product price and to represent the return cost. Similarly， online retailers also need to pay for consumer returns. We use to indicate the ratio of return cost to the product price and to represent the return cost that the online retailer needs to pay.
Following Zhang （2019），the different search costs can also affect channels selection of consumers. We assume that represents the cost of offline search， which includes the cost of travelling and the time spent on learning product information in the store. We divide online search costs into two parts： represents the cost of searching the product information online and represents the cost of waiting for the product to arrive after purchasing online. is uniformly distributed in . According to the actual situation， when the price gap between retailers is not obvious， the waiting time has an impact on consumer’ channel selection. The greater the waiting time ， the more consumers are willing to buy offline.
As can be seen， when the return price ratio is low， that is， consumers bear low return cost， both online and offline prices are inversely proportional to the gap in search costs. When consumers need to bear lower return cost， the retailers can only increase the price by reducing the gap in search costs. When consumers bear less return cost， retailers need to ensure that the search costs is similar to that of the other platform if they want to set a higher price At this point， offline retailers and online retailers restrict each other.
Proposition 7. When showrooming exists， we can draw the following conclusions：
From this inference we can conclude that when the search costs gap is large （small） and the return cost borne by the online retailer is large （small）， the profit of the online retailer is inversely proportional to the search costs gap. When the search costs gap is large， the profit of offline retailer increases with the increase of the search costs gap. When the search costs gap is small， the offline retailer’s profit decreases with the increase of the search costs gap. When the online search costs and the return cost borne by the online retailer is small， at this point， the online profit increases with the decrease of the search costs gap. It means that only when the offline search cost is decreased can the online retailer’s profit increase. As can be seen， showrooming is not necessarily beneficial to online retailers， but also has a restrictive effect. If offline retailers want to increase profits， they can increase the gap in search costs. When the offline search cost is large enough， consumers' choice will be the same as case 1 and consumers will not consider buying online directly. Proposition 8. In case 3， we can draw the following conclusions：
In this case， the online price is directly proportional to the search costs gap while the offline price is inversely proportional to the search costs gap. When the return cost borne by online retailers is small， the online profit is directly proportional to the gap of search costs. When the return cost is large， the online profit is inversely proportional to the gap of search costs， while the offline profit is always inversely proportional to the gap of search costs. When showrooming does not exist， the profit of the offline retailer will decrease with the increase of the search costs gap. When the profit of the online retailer will increase with the increase of the search costs gap when the return cost borne by the online retailers is relatively small. Therefore， offline retailers can increase its profit by reducing its search cost. When the return costs borne by online retailers are relatively small， the online retailer also need to reduce its search cost in order to ensure more profits.
5. Numerical studies
The online and the offline retailer often make a strategy choice based on the profit. In order to delineate the relationship between parameters clearly， we conduct numerical studies in this section.
Based on the above analysis， the consumers have different choices under different conditions. In case2 and case3， both retail channels have consumers choosing to buy， which will affect the profit of the two retail channels. Therefore， we will carry out numerical studies in these two cases.
We then set the reasonable base parameter values for the numerical simulation. We set the consumer valuation . In the above analysis， we mainly analyze the relationship between the search costs gap of the two channels and profits， so we take the cost gap as a parameter in the numerical analysis. We set the consumer matching probability .
We first study the impact of the search costs gap on profits. In this case， we set ， . We can get the following observations：
Observation 1. From figure 4， we can find that in case 2， the profit of offline retailer is always greater than that of online retailer. And retailers' profits decrease as the cost gap increases. In case3， when ， the profit of offline retailer is greater than that of online retailer. We can conclude that when the cost of offline search is less than that of online search， the profit of the offline retailer has a great advantage. when ， at a certain threshold， the profit of the online retailer will be larger than that of the offline retailer. In case 3， the profit of the offline retailer is inversely proportional to the cost gap， while the profit of the online retailer is directly proportional to the cost gap. Thus it can be seen that search costs is one of the important factors that determine the profit of retailers. We then study the impact of the consumer return cost on profits. In this case， we compare the profit at different values of .we set ， . We can get the following observations：
Observation 2. From figure 5， we can find that in case 2 and case 3， the profit of the offline retailer is greater than that of online retailer. In case2， retailers' profits increase as consumer return costs rise. Consumer return mainly occur through online channel， when consumer returns cost is high， the offline retailer' profit increase. When the cost of return is high， the number of returned goods will be reduced， and the profits of online retailers will also increase.
In reality， ， and can be controlled by the retailers. And retailers often sells different categories of products. The different products have different parameter values. At the same time， for different categories of products， customers also have different valuation of different channel. So if retailers want to make more profits， they need to take measures to help consumers reduce search costs. In real life， retailers reduce search costs by optimizing search interface， WeChat push， etc.
6. Conclusion
In this paper， we studied the price competition between the offline retailer and the online retailer in the presence of showrooming. We took returns and search costs into account and studied the relationship between the prices and profits of the retailer and the search costs gap.
Firstly， we built the utility models to determine the optimal prices and profits. Next， we analyzed the resulting prices and profits. Our analysis of the results are mainly to compare the prices and profits of the offline retailer and the online retailer. We also found out how consumers choose shopping channels under different search costs and how different search costs affect the retailers’ profits.
We found that when the search costs gap is small， consumers will give up the channel of online purchase directly. They will choose to experience the products offline and then decide which channel to purchase. When the search costs gap is in the moderate， consumers will consider three purchase channels at the same time. When the gap in search costs is too large， consumers will not choose the channel of offline experience before online purchase， but will directly buy online or directly buy offline.
We also found that when there is no online buying directly， the price is a constant value and the profit is only related to consumer satisfaction. The offline profit is always greater than the online profit. When showrooming exists and consumers bear large return cost， the prices of the online and offline retailer are proportional to the search costs gap. In this case， the offline retailer’s profit is proportional to the search costs gap and the online retailer’s profit increases with the decrease of the search costs gap. The online retailer needs to reduce the return costs borne by themselves if they want to make more profits. When there is no showrooming， the online price increases with the increase of the search costs gap while the offline price decreases with the increase of the search costs gap. And the profit of offline retailer decreases with the increase of the gap. The profit of the online retailer increases with the increase of the search costs gap when the return cost borne by the online retailer is relatively small. Therefore， offline retailers can increase its profit by reducing its search cost. When the return cost borne by the online retailer is relatively large， the online retailer does not have to reduce the search cost.
This research still needs to be expanded in the future. Firstly， only online channels are allowed to return products in this paper， while offline channels are also allowed to return products in future studies. Secondly， this paper considers the pricing problem of retailers from the perspective of consumers. We can consider adding manufacturers to the supply chain model and consider showrooming from the perspective of manufacturers and retailers.
REFERENCES
Adam Rapp， T. L. B.， （2015） “Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance，” Journal of Retailing， 91（2）， 358-369.
Arora， S.， Singha， K.， & Sahney. S.， （2017） “Understanding consumer’s showrooming behaviour，” Asia Pacific Journal of Marketing & Logistics， 29（2）， 409-431.
Basu， P.， Basak， S.， Avittathur， B.， & Sikdar， S.， （2017）. “A game theoretic analysis of multichannel retail in the context of “showrooming，” Decision Support Systems， 103（2）， 117-131.
Chiu， H. C.， Hsieh， Y.-C.， Roan， J.， Tseng， K.-J.， & Hsieh， J.-K.， （2010）. “The challenge for multichannel services： Cross-channel free-riding behavior，” Electronic Commerce Research & Applications， 10（2）， 268-277.
Feng， Y.， Pei， H.， Zhao， F.， & Hu， C.， （2015）. “Customer returns model in a dual-channel supply chain，” Journal of Modelling in Management， 10（3）， 360-379.
Fishman， A.， & Levy， N.， （2015）. “Search costs and investment in quality，” Journal of Industrial Economics， 63（4）， 625-641.
Gensler， S.， Neslin， S. A.， & Verhoef， P. C.， （2017）. “The showrooming phenomenon： It’s more than just about price，”
Journal of Interactive Marketing， 38（4）， 29-43.
Gilgenbach， & G.， R.， （2015）. “Can a decline in search costs increase prices？” Canadian Journal of Economics/Revue Canadienne d‘Economique， 48（4）， 1381-1402. Herhausen， D.， Binder， J.， Schoegel， M.， & Herrmann， A.， （2015）. “Integrating bricks with clicks： Retailer-level and channel-level outcomes of online–offline channel integration，” Journal of Retailing， 91（2）， 309-325.
Hong， H.， & Shum， M.， （2006）. “Using price distributions to estimate search costs，” Rand Journal of Economics， 37（2）， 257-275.
Jing， & Bing. （2018）. “Showrooming and webrooming： Information externalities between online and offline sellers，”
Marketing Science， 37（3）， 469-483.
Loginova， O.， （2009）. “Real and virtual competition，” Journal of Industrial Economics， 57（2）， 319-342.
Mehra， A.， Kumar， S.， & Raju， J. S.， （2013）. “showrooming and the competition between store and online retailers，”
SSRN Electronic Journal， 190（11）， 22-60.
Mehra， A.， Kumar，S.，& Raju， S.， （2017）. “Competitive strategies for brick-and-mortar stores to counter ‘showrooming’，” Social Science Electronic Publishing， 64（7）. 3076-3090.
Ofek E， S. M.， Katona Z.， （2011）. “Bricks and clicks”： The impact of product returns on the strategies of multichannel retailers，” Marketing Science， 30（1）， 42-60.
Rapp， A.， Baker， T. L.， Bachrach， D. G.， Ogilvie， J.， & Beitelspacher， L. S.， （2015）. “Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance，” Journal of Retailing， 91（2）， 358-369.
Rodrguez-Torrico， P.， Cabezudo， R. S. J.， & San-Martn， S.， （2017）. “Tell me what they are like and i will tell you where they buy. an analysis of omnichannel consumer behavior，” Computers in Human Behavior， 68（3）， 465-471.
Sharma， A.， Levy， M.， & Kumar， A.， （2000）. “Knowledge structures and retail sales performance： An empirical examination，” Journal of Retailing， 76（1）， 53-69.
Soh， C.， Markus， M. L.， & Goh， K. H.， （2006）. “Electronic marketplaces and price transparency： Strategy， information technology， and success，” MIS Quarterly， 30（3）， 705-723.
Strebel， J.， Erdem， T.， & Swait， J.， （2004）. “Consumer search in high technology markets： Exploring the use of traditional information channels，” Journal of Consumer Psychology， 14（1-2）， 96-104.
Zhang， P.， He， Y.， & Zhao， X.， （2019）. “Preorder-online， pickup-in-store strategy for a dual-channel retailer”.
Transportation Research Part E： Logistics and Transportation Review， 122（3）， 27-47.
Wang， A， Li， W， & Zhang， Y.， （2021）. “Price discount and price dispersion in online market： Do more firms still lead to more competition？”. Journal of theoretical and applied electronic commerce research， 16（2）， 140-154.
School of Economics and Management Tongji University， Shanghai 200092， China
Abstract More and more consumers choose multiple purchase channels to compare product quality， price and other characters when shopping. In particular， the behavior of consumers experiencing products in offline stores and buying online channels is called as showrooming. In this context， different shopping channels will generate different search costs. When the channel price gap is small， search costs will become one important factor to determine the consumer channel selection. The results show that the offline retailer gets more benefits when the online search cost is greater than the offline one. Moreover， when showrooming exists， the online retailer should try to reduce the consumers’ return cost to ensure more profits regardless of the search costs. When showrooming does not exist， the offline retailer can increase its profit by reducing its search cost and the online retailer does not have to reduce the search cost when the return cost borne by itself is relatively large.
Keywords： Retailing， Showrooming， Search costs， Consumer returns
The impact of SEARCH COSTS on the online-offline competition UNDER SHOWROOMING
1. Introduction
With the continuous expansion of shopping scale， the marketing mode combining traditional retail channels and electronic channels becomes the main stream of all industries. According to the survey of Rodrguez-Torrico， Cabezudo， & San-Martn （2017）， more and more consumers are willing to place orders online and experience offline instead of choosing one shopping channel to buy directly. We refer to the behavior of experiencing products offline and buying online as“showrooming”.
In real life， showrooming phenomena can be seen everywhere. Many retailers， for example， Uniqlo， not only have their own online retail stores， but also promote scanning code purchase in offline retail stores. When consumers experience products offline， they can scan the products’ code on the product to find the purchase links online. Apple Inc opened offline experience store to attract consumers while many authorized retailers such as JD.com and Suning attract customers through lower price rather than the experience. Many consumers may experience products offline and then switch to online platforms to buy them. At this point， offline stores managed by Uniqlo and Apple Inc act as showroom.
The difference between online and offline search costs is also a major consideration for consumers. According to Bing （2018）， lower search costs for consumer will aggravate showrooming and intermediate search costs may not benefit to traditional retailers’ profits. Nuria Viejo （2018） found that most people think online shopping costs less time and they are more willing to shopping online. Wang （2021） concluded that when the amount of information obtained by searching is so large that consumers need to pay more search costs， consumers will choose to buy at random. The previous studies showed that different search costs may have an impact on showrooming and retailers’ profits. Therefore， it is important to consider the relationship between search costs and consumers’ channel selection. We also study the impact of search costs gaps on retailers’ profits and find that online retailer needs to reduce the search costs gaps in order to make profits. Return， as part of the process of online shopping， affects consumer experience and subsequent purchase behavior. Feng and Pei （2015） concluded that retailers can increase their sales and profits by setting a higher price when they allow returns. Zhang （2018） found that a loose return policy is conducive to encourage follow-up purchase behavior for consumers who have experienced returns. This article studies consumer returns and shows that the cost of returns can influence retailers’ profits.
This paper focuses on the impact of different search costs on retailers' profits with the addition of consumer returns. The innovation of this article is to consider both consumer returns and the impact of search costs on retailers. Firstly， this paper focuses on the relationship between different search costs and the choice of shopping channels under showrooming. Secondly， this paper considers the case where consumers are allowed to return products and the impact of the return cost on retailers' profits. The difference between this paper and other papers on showrooming is that this paper takes into account the situation of returning products and the cost that consumers and retailers have to bear in the case of returning.
The contribution of this paper lies in the combination of consumer returns and showrooming， which has rarely been combined in previous literature. And this paper takes search costs as an important factor in research while other literatures pay less attention to search costs. We then attempt to solve the following problems：
1）How does showrooming affect retailers pricing？
2）How does the gap in online and offline search costs influence consumers’ behavior？
3）How does the gap in online and offline search costs affect retailers’ prices and profits？
We find that the offline retailer gets more benefits when the online search costs is greater than the offline one. Therefore， the online retailer needs to find ways to reduce the search costs of consumers if he wants to make more profits. Moreover， when showrooming exists， the online retailer should try to reduce the return cost borne by consumers to make more profits. When showrooming does not exist， the profit of the offline retailer will decrease with the increase of the search costs gap. And the profits of the online retailer will increase with the increase of the search costs gap when the return cost borne by the online retailers is relatively small. Therefore， the offline retailer can increase its profit by reducing its search cost and the online retailer does not have to reduce the search cost when the return cost borne by itself is relatively large. The rest of the paper is organized as follows. Section 2 reviews the related literature and clarifies our contributions. Section 3 presents the model and Section 4 is analysis and findings. Section 5 concludes and discusses potential future research directions.
2. Literature review
Our research is related to three aspects： the major factors that influence competition between online and offline retailers， the impact of search costs on consumers’ channel selection and the effects of showrooming on consumers and retailers.
Many scholars have studied the factors that influence competition between online and offline retailers. Choi et al. （2019） found that both discount pricing and bundling have positive effects on the daily sales of the applications. Deufel et al. （2019） showed that the payment choice has a strong influence on consumers’ channel selection. Herhausen et al. （2015） proved that the advantages of online and offline channel integration are more than the advantages of channel separation. Jing and Bing （2018） believed showrooming has increased competition among retailers and reduced profits for online and offline retailers. Loginova （2009） believed that customers with high valuation are more willing to shop offline， while customers with low valuation tend to the channel of showrooming. Mehra and Kumar （2013） found that increasing online search matching difficulty and charging consumers for showrooming could help offline retailers improve profits. Ofek et al. （2011） examined the circumstances under which two offline retailers can operate an online store. He found that if the differentiation among competing offline retailers is small， the operation of online stores could assist their offline stores and provide a platform for displaying offline goods. However， when the gap is big， only one can run an online store and make little profit. Thomas et al. （2019） found that the reviews information obtained from online search can influence consumers' purchase intentions. This paper considers several factors when studying the competition of retailers， including showrooming and consumer returns.
Much attention has been paid to search costs. Arora and Singha （2017） found that the offline experience and the guidance of sales staff would drive customers to the offline store. Online search costs and online shopping services will have a certain impact on online sales. Fishman and Levy （2015） analyzed that lower search cost decreases the market share of lower-quality companies and increases the market share of higher-quality companies. Gilgenbach et al. （2015） found that retailers lowering search costs does not always increase prices. Different search costs will cause consumers to choose different consumption channels. Hong and Shum （2006） used search costs models to assess the distribution of search costs and prices. Strebel et al. （2004） believed that different channels have substitution effect in the search stage， and it is necessary to integrate various channels to investigate the channel selection. Zhao et al. （2018） explored the influence of price discounts on consumer search behaviors and the moderate effect of retailers' online reputations. In this paper， we study the relationship between the search costs gap and the retailers’ profits when consumer returns are allowed under showrooming. Many studies have explored the showrooming behavior. Adam et al. （2015） believed that the behaviors and strategies of professional sales staff in offline stores will offset the negative influence brought by showrooming. Basu and Basak （2017） considered the sales effort and found that with the increased impact of the showrooming， the profits of online and offline retailers will decrease. Chiu and Hsieh （2010） regarded the showrooming as free-riding behavior and studied the driving factors， pulling factors and impeding factors of consumers’ online information search and offline purchase. Gensler and Neslin （2017） found that the quality of products and the waiting time of services positively affects the showrooming behavior. Mehra et al. （2017） first tried to counter showrooming with a price-matching strategy but this would lead to lower profits for offline retailers. Mehra ea al. （2013） studied three strategies to help offline retailers resist showrooms： price matching， making it harder to find products， and charging for offline experiences. The results showed that price matching does not increase profits for offline retailers， only the latter two can. Rapp et al. （2015） showed that the behavior of good salespeople and store level can offset the negative impact of showrooming. Sharma et al. （2000） believed that the emergence of showrooming behavior affects both online and offline retailers. Soh and Markus （2006） found that consumers would first experience the product offline and then search online for the product through mobile clients and complete the transaction. This paper considers showrooming when the consumer return is allowed. The return cost borne by consumers can have an impact on retailers’ profits.
Through reviewing relevant literatures， we find that there is almost no research on how search costs affect consumers’ channel selection. This paper focuses on the impact of different search costs when consumer returns are allowed. And this paper takes the cost that consumers and retailers have to bear in the case of returning into consideration. We find that the offline retailer gets more benefits when the online search cost is greater than the offline search costs. Moreover， when showrooming exists， online retailers should try to reduce the consumers’ return cost to ensure more profits. We also find that retailers' profits are inversely related to the gap in online and offline search costs.
3. Problem statement
We consider of an online retailer and an offline retailer that sell an identical product at different price and . The retailers set prices simultaneously and consumers can easily get the price information with no fee from the two retailers. The offline retailer sets up a physical store to sell products by providing consumers with experience services. The online retailer displays the products’ information on the website and sells products online. Consumers can choose to go to the physical store to experience the products and buy them offline or they can browse the products’ information online and buy them directly online. There are also quite a few consumers choosing to experience products offline to resolve their match uncertainty and then buy online. Under the circumstance， the physical store plays the role of “showrooming”. The offline retailer provides additional services to consumers and need to pay service cost， so we assume that . The consumer perceives a positive valuation for the product before experiencing the product. After experiencing the product offline or browsing the products’ information online， the probability of the product meeting consumers' expectations is （）. The consumer who is not satisfied with the product after browsing can choose not to buy it. For simplicity， consumers can choose to buy directly online and may return the goods due to the match uncertainty， denoted as channel E. Besides， consumers can purchase directly from offline retailers， denoted as channel T. In addition， consumers go to the offline store to experience the product before ordering online， denoted as S. We assume that consumers who have experienced the product in the offline store will be satisfied with the product， and if they buy it， they will not return it. Consumers who choose S and T channels can know exactly whether they are satisfied with the product. Consumers who buy the product directly online without experiencing it may return the product to the online retailer. Consumers returning goods need to bear the freight cost. We use to indicate the ratio of return cost to the product price and to represent the return cost. Similarly， online retailers also need to pay for consumer returns. We use to indicate the ratio of return cost to the product price and to represent the return cost that the online retailer needs to pay.
Following Zhang （2019），the different search costs can also affect channels selection of consumers. We assume that represents the cost of offline search， which includes the cost of travelling and the time spent on learning product information in the store. We divide online search costs into two parts： represents the cost of searching the product information online and represents the cost of waiting for the product to arrive after purchasing online. is uniformly distributed in . According to the actual situation， when the price gap between retailers is not obvious， the waiting time has an impact on consumer’ channel selection. The greater the waiting time ， the more consumers are willing to buy offline.
As can be seen， when the return price ratio is low， that is， consumers bear low return cost， both online and offline prices are inversely proportional to the gap in search costs. When consumers need to bear lower return cost， the retailers can only increase the price by reducing the gap in search costs. When consumers bear less return cost， retailers need to ensure that the search costs is similar to that of the other platform if they want to set a higher price At this point， offline retailers and online retailers restrict each other.
Proposition 7. When showrooming exists， we can draw the following conclusions：
From this inference we can conclude that when the search costs gap is large （small） and the return cost borne by the online retailer is large （small）， the profit of the online retailer is inversely proportional to the search costs gap. When the search costs gap is large， the profit of offline retailer increases with the increase of the search costs gap. When the search costs gap is small， the offline retailer’s profit decreases with the increase of the search costs gap. When the online search costs and the return cost borne by the online retailer is small， at this point， the online profit increases with the decrease of the search costs gap. It means that only when the offline search cost is decreased can the online retailer’s profit increase. As can be seen， showrooming is not necessarily beneficial to online retailers， but also has a restrictive effect. If offline retailers want to increase profits， they can increase the gap in search costs. When the offline search cost is large enough， consumers' choice will be the same as case 1 and consumers will not consider buying online directly. Proposition 8. In case 3， we can draw the following conclusions：
In this case， the online price is directly proportional to the search costs gap while the offline price is inversely proportional to the search costs gap. When the return cost borne by online retailers is small， the online profit is directly proportional to the gap of search costs. When the return cost is large， the online profit is inversely proportional to the gap of search costs， while the offline profit is always inversely proportional to the gap of search costs. When showrooming does not exist， the profit of the offline retailer will decrease with the increase of the search costs gap. When the profit of the online retailer will increase with the increase of the search costs gap when the return cost borne by the online retailers is relatively small. Therefore， offline retailers can increase its profit by reducing its search cost. When the return costs borne by online retailers are relatively small， the online retailer also need to reduce its search cost in order to ensure more profits.
5. Numerical studies
The online and the offline retailer often make a strategy choice based on the profit. In order to delineate the relationship between parameters clearly， we conduct numerical studies in this section.
Based on the above analysis， the consumers have different choices under different conditions. In case2 and case3， both retail channels have consumers choosing to buy， which will affect the profit of the two retail channels. Therefore， we will carry out numerical studies in these two cases.
We then set the reasonable base parameter values for the numerical simulation. We set the consumer valuation . In the above analysis， we mainly analyze the relationship between the search costs gap of the two channels and profits， so we take the cost gap as a parameter in the numerical analysis. We set the consumer matching probability .
We first study the impact of the search costs gap on profits. In this case， we set ， . We can get the following observations：
Observation 1. From figure 4， we can find that in case 2， the profit of offline retailer is always greater than that of online retailer. And retailers' profits decrease as the cost gap increases. In case3， when ， the profit of offline retailer is greater than that of online retailer. We can conclude that when the cost of offline search is less than that of online search， the profit of the offline retailer has a great advantage. when ， at a certain threshold， the profit of the online retailer will be larger than that of the offline retailer. In case 3， the profit of the offline retailer is inversely proportional to the cost gap， while the profit of the online retailer is directly proportional to the cost gap. Thus it can be seen that search costs is one of the important factors that determine the profit of retailers. We then study the impact of the consumer return cost on profits. In this case， we compare the profit at different values of .we set ， . We can get the following observations：
Observation 2. From figure 5， we can find that in case 2 and case 3， the profit of the offline retailer is greater than that of online retailer. In case2， retailers' profits increase as consumer return costs rise. Consumer return mainly occur through online channel， when consumer returns cost is high， the offline retailer' profit increase. When the cost of return is high， the number of returned goods will be reduced， and the profits of online retailers will also increase.
In reality， ， and can be controlled by the retailers. And retailers often sells different categories of products. The different products have different parameter values. At the same time， for different categories of products， customers also have different valuation of different channel. So if retailers want to make more profits， they need to take measures to help consumers reduce search costs. In real life， retailers reduce search costs by optimizing search interface， WeChat push， etc.
6. Conclusion
In this paper， we studied the price competition between the offline retailer and the online retailer in the presence of showrooming. We took returns and search costs into account and studied the relationship between the prices and profits of the retailer and the search costs gap.
Firstly， we built the utility models to determine the optimal prices and profits. Next， we analyzed the resulting prices and profits. Our analysis of the results are mainly to compare the prices and profits of the offline retailer and the online retailer. We also found out how consumers choose shopping channels under different search costs and how different search costs affect the retailers’ profits.
We found that when the search costs gap is small， consumers will give up the channel of online purchase directly. They will choose to experience the products offline and then decide which channel to purchase. When the search costs gap is in the moderate， consumers will consider three purchase channels at the same time. When the gap in search costs is too large， consumers will not choose the channel of offline experience before online purchase， but will directly buy online or directly buy offline.
We also found that when there is no online buying directly， the price is a constant value and the profit is only related to consumer satisfaction. The offline profit is always greater than the online profit. When showrooming exists and consumers bear large return cost， the prices of the online and offline retailer are proportional to the search costs gap. In this case， the offline retailer’s profit is proportional to the search costs gap and the online retailer’s profit increases with the decrease of the search costs gap. The online retailer needs to reduce the return costs borne by themselves if they want to make more profits. When there is no showrooming， the online price increases with the increase of the search costs gap while the offline price decreases with the increase of the search costs gap. And the profit of offline retailer decreases with the increase of the gap. The profit of the online retailer increases with the increase of the search costs gap when the return cost borne by the online retailer is relatively small. Therefore， offline retailers can increase its profit by reducing its search cost. When the return cost borne by the online retailer is relatively large， the online retailer does not have to reduce the search cost.
This research still needs to be expanded in the future. Firstly， only online channels are allowed to return products in this paper， while offline channels are also allowed to return products in future studies. Secondly， this paper considers the pricing problem of retailers from the perspective of consumers. We can consider adding manufacturers to the supply chain model and consider showrooming from the perspective of manufacturers and retailers.
REFERENCES
Adam Rapp， T. L. B.， （2015） “Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance，” Journal of Retailing， 91（2）， 358-369.
Arora， S.， Singha， K.， & Sahney. S.， （2017） “Understanding consumer’s showrooming behaviour，” Asia Pacific Journal of Marketing & Logistics， 29（2）， 409-431.
Basu， P.， Basak， S.， Avittathur， B.， & Sikdar， S.， （2017）. “A game theoretic analysis of multichannel retail in the context of “showrooming，” Decision Support Systems， 103（2）， 117-131.
Chiu， H. C.， Hsieh， Y.-C.， Roan， J.， Tseng， K.-J.， & Hsieh， J.-K.， （2010）. “The challenge for multichannel services： Cross-channel free-riding behavior，” Electronic Commerce Research & Applications， 10（2）， 268-277.
Feng， Y.， Pei， H.， Zhao， F.， & Hu， C.， （2015）. “Customer returns model in a dual-channel supply chain，” Journal of Modelling in Management， 10（3）， 360-379.
Fishman， A.， & Levy， N.， （2015）. “Search costs and investment in quality，” Journal of Industrial Economics， 63（4）， 625-641.
Gensler， S.， Neslin， S. A.， & Verhoef， P. C.， （2017）. “The showrooming phenomenon： It’s more than just about price，”
Journal of Interactive Marketing， 38（4）， 29-43.
Gilgenbach， & G.， R.， （2015）. “Can a decline in search costs increase prices？” Canadian Journal of Economics/Revue Canadienne d‘Economique， 48（4）， 1381-1402. Herhausen， D.， Binder， J.， Schoegel， M.， & Herrmann， A.， （2015）. “Integrating bricks with clicks： Retailer-level and channel-level outcomes of online–offline channel integration，” Journal of Retailing， 91（2）， 309-325.
Hong， H.， & Shum， M.， （2006）. “Using price distributions to estimate search costs，” Rand Journal of Economics， 37（2）， 257-275.
Jing， & Bing. （2018）. “Showrooming and webrooming： Information externalities between online and offline sellers，”
Marketing Science， 37（3）， 469-483.
Loginova， O.， （2009）. “Real and virtual competition，” Journal of Industrial Economics， 57（2）， 319-342.
Mehra， A.， Kumar， S.， & Raju， J. S.， （2013）. “showrooming and the competition between store and online retailers，”
SSRN Electronic Journal， 190（11）， 22-60.
Mehra， A.， Kumar，S.，& Raju， S.， （2017）. “Competitive strategies for brick-and-mortar stores to counter ‘showrooming’，” Social Science Electronic Publishing， 64（7）. 3076-3090.
Ofek E， S. M.， Katona Z.， （2011）. “Bricks and clicks”： The impact of product returns on the strategies of multichannel retailers，” Marketing Science， 30（1）， 42-60.
Rapp， A.， Baker， T. L.， Bachrach， D. G.， Ogilvie， J.， & Beitelspacher， L. S.， （2015）. “Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance，” Journal of Retailing， 91（2）， 358-369.
Rodrguez-Torrico， P.， Cabezudo， R. S. J.， & San-Martn， S.， （2017）. “Tell me what they are like and i will tell you where they buy. an analysis of omnichannel consumer behavior，” Computers in Human Behavior， 68（3）， 465-471.
Sharma， A.， Levy， M.， & Kumar， A.， （2000）. “Knowledge structures and retail sales performance： An empirical examination，” Journal of Retailing， 76（1）， 53-69.
Soh， C.， Markus， M. L.， & Goh， K. H.， （2006）. “Electronic marketplaces and price transparency： Strategy， information technology， and success，” MIS Quarterly， 30（3）， 705-723.
Strebel， J.， Erdem， T.， & Swait， J.， （2004）. “Consumer search in high technology markets： Exploring the use of traditional information channels，” Journal of Consumer Psychology， 14（1-2）， 96-104.
Zhang， P.， He， Y.， & Zhao， X.， （2019）. “Preorder-online， pickup-in-store strategy for a dual-channel retailer”.
Transportation Research Part E： Logistics and Transportation Review， 122（3）， 27-47.
Wang， A， Li， W， & Zhang， Y.， （2021）. “Price discount and price dispersion in online market： Do more firms still lead to more competition？”. Journal of theoretical and applied electronic commerce research， 16（2）， 140-154.
School of Economics and Management Tongji University， Shanghai 200092， China