Online Relationship Marketing

Transcription

1 Marketing Science Institute Working Paper Series 2015 Report No Online Relationship Marketing Irina V. Kozlenkova, Eric (Er) Fang, Bangming Xiao, and Robert W. Palmatier Online Relationship Marketing 2015 Irina V. Kozlenkova, Eric (Er) Fang, Bangming Xiao, and Robert W. Palmatier; Report Summary 2015 Marketing Science Institute MSI working papers are distributed for the benefit of MSI corporate and academic members and the general public. Reports are not to be reproduced or published in any form or by any means, electronic or mechanical, without written permission.

2 Report Summary In online marketplaces, shopping communities have emerged to enhance the shopping experience and reinsert the personal interaction into the retail purchasing process. In response, sellers are implementing online relationship marketing (RM) programs to facilitate interactions and relationship formation in these communities. How effective are such efforts in improving sales performance? To address that question, Irina Kozlenkova, Eric Fang, Bangming Xiao, and Robert Palmatier evaluate data from an online shopping community in Taobao.com, the largest e-commerce platform in China, managed by Alibaba. They examine six months of longitudinal daily data for new buyers and sellers as they form unilateral (buyer-to-seller and seller-to-buyer) and bilateral (reciprocated) relationships in the online community. Findings Consistent with a buyer s need to reduce uncertainty, (1) communication, (2) seller s reputation, and (3) relationship clustering have positive effects on relationship formation, although the effects of communication and clustering diminish as the buyer gains experience. Consistent with a seller s need to reduce the cost of finding customers, (1) communication, (2) behavioral similarity, and (3) relational similarity improve sellers likelihood to form a relationship, although these effects are differentially moderated by time spent in the online community. Elasticity and marginal returns analyses provide additional managerial insights. Of the buyer-toseller online RM strategies, relationship clustering is the most effective, generating $210 in additional sales per week for a 1% increase in clustering, which is 9% higher than the marginal return from seller s reputation ($193) and 117% higher than that from communication ($97). However, when dealing with experienced buyers, seller s reputation becomes the most effective strategy, increasing weekly sales by $293 with a 1% increase in reputation a 76% higher payoff than relationship clustering ($166) and 240% higher than the payoffs of communication ($86). Buyer-to-seller strategies generate about 10 times more dollars in sales than seller-to-buyer strategies. The payoffs of the different types of seller-to-buyer strategies are relatively similar to each other. Relational similarity generates $16 in marginal weekly sales for a 1% increase, behavioral similarity generates $15, and communication generates $14. This effect is more pronounced for new buyers. Of the three types of relationships, the number of buyer-to-seller relationships possessed by a seller generates the highest payoff. On average, a 1% increase in the number of buyer-to-seller relationships increases weekly sales by $58, almost four times more than the number of seller-tobuyer relationships ($14) and three times more the number of reciprocated relationships ($19). Over time, however, the payoffs of building unilateral buyer and seller relationships become less important, and bilateral (reciprocated) buyer seller relationships become more important for generating sales. Marketing Science Institute Working Paper Series 1

3 Managerial implications For sellers, the biggest bang for the buck comes from the number of buyer-initiated relationships and RM strategies associated with their growth. Online sellers can proactively provide the information that buyers seek to help them feel more comfortable with their purchasing decisions. The largest marginal returns from experienced buyers come from seller s reputation, so sellers also need to take the time to formulate strategies that will improve their reputations (e.g., develop mechanisms to encourage reviews from past customers, address customer problems to prevent negative reviews). It is also important to invest in reciprocated relationships. They generate superior returns from enhanced loyalty, higher share of wallet, and positive word of mouth, all of which take time to emerge. Irina V. Kozlenkova is Assistant Professor of Marketing, Broad College of Business, Michigan State University. Eric (Er) Fang is Associate Professor of Marketing and James Tower Faculty Fellow, University of Illinois at Urbana-Champaign. Bangming Xiao is a Doctoral Candidate, Wuhan University, China, and Visiting Scholar, University of Illinois at Urbana-Champaign. Robert W. Palmatier is Professor of Marketing and John C. Narver Chair in Business Administration, Michael G. Foster School of Business, University of Washington. Marketing Science Institute Working Paper Series 2

4 Introduction Shopping on online marketplaces such as Amazon and Alibaba is growing dramatically, and Websites like ebay and Amazon have transformed the way people buy and sell products. With a current market cap of $68B and $151B respectively, it s clear that efficient and highly engaged marketplaces between buyers and sellers can provide real value to both parties (Forbes 2013). In the United States, more than 60% of buyers make their e-commerce purchases through an online marketplace, and online retail sales are expected to exceed $330 billion in 2015 (Forrester Report 2012, 2015). In China, online marketplaces account for more than 90% of all e- commerce (Nowlin 2014). Within these marketplaces, shopping communities, or subgroups that facilitate interactions among buyers and sellers, are emerging as a means to reinsert the shopping experience and personal interaction into the retail purchasing process elements that, when missing, often represent customers greatest concern with online versus traditional retailing (Dholakia and Vianello 2009; Forbes 2013; Yin 2010). In response, sellers implement online relationship marketing (RM) programs to facilitate interactions and relationship formation (e.g., following or friending another party) in shopping communities, in the belief that doing so can increase sales performance (Forbes 2013; Ha 2004). However, there is little research that provides insight into the effectiveness of such efforts (Verma, Sharma, and Sheth 2015). This article therefore seeks to understand the most effective relationship marketing strategies for forming online relationships and the effect of these relationships on sales performance in online shopping communities. Online and offline relationships differ, so offline RM strategies cannot simply be extended to online contexts. In particular, online relationship formation initially is unilateral, such that another party must proactively reciprocate a relationship request (e.g., accept a friend or follower ), which is different from face-to-face interactions. Accordingly, online relationships have a clear formation direction and can remain unilateral for their duration. Evaluations of online RM effectiveness thus must distinguish among strategies designed to generate buyer-to-seller (buyer following a specific seller) versus seller-to-buyer (seller following a specific buyer) unilateral relationships, as well as bilateral, reciprocated relationships (both buyer and seller follow each other), to determine their unique effects on sales performance. Online RM effectiveness further depends on each party s underlying motivation. Since buyers generally lack perfect information about sellers or their products before purchasing, they Marketing Science Institute Working Paper Series 3

5 are motivated to learn about the seller, its offerings, and its services to reduce their uncertainty (Mitchell 1999). Thus, the most effective buyer-to-seller RM strategies provide cues that buyers can use to reduce their uncertainty about a seller and enhance their desire to form a relationship. Conversely, sellers form relationships with buyers to reduce the search cost of finding high-quality buyers and persuade them to purchase their products (Stephen and Toubia 2010). Thus, the most effective seller-to-buyer RM strategies provide cues that sellers can use to reduce the search costs associated with finding high-quality buyers and enhance their desire to form a relationship. We test this conceptual model in two studies. In Study 1, we examine the RM drivers of buyer-to-seller (buyer) and seller-to-buyer (seller) relationship formation in online shopping communities in two separate models, to identify the most effective RM strategies for promoting each party s unilateral relationship formation. In Study 2, we examine the payoffs of building unilateral buyer and seller relationships, as well as bilateral (reciprocated) buyer seller relationships, on sales, such that we investigate the returns from all three types of online relationships. Studies 1 and 2 use a sample from an online shopping community in Taobao.com, the largest e-commerce platform in China, which is managed by Alibaba (similar to ebay and Amazon). Specifically, we evaluate six months of longitudinal daily data for new buyers and sellers as they form relationships in an online shopping community. This article contributes to the literature in three main ways. First, we evaluate the dynamic effects of multiple online RM strategies for increasing the likelihood of buyer and seller relationship formation (Study 1). Consistent with buyers need to reduce uncertainty, we find that communication (direct messages between a buyer and a seller), seller s reputation (number of seller s followers or friends), and relationship clustering (belonging to a tightly connected group within an online shopping community) have positive effects on the buyers likelihood of forming a relationship with a seller, but the effects of communication and relationship clustering diminish as the buyer spends more time in the shopping community. In addition, results show that relationship clustering acts as an enhancer to these other two cues, such that seller s reputation and communication appear more credible when they come from a source that is closely linked to the buyer s friends (i.e., relationship clustering). Consistent with sellers need to reduce search costs, we also find that communication and behavioral (i.e., events in which buyer and seller jointly participate) and relational (i.e., seller s and buyer s mutual friends) similarity Marketing Science Institute Working Paper Series 4

6 have positive effects on sellers likelihood of forming a relationship with a buyer but these effects are differentially moderated as the seller spends more time in the shopping community. Overall, by identifying unique buyer- and seller-side RM strategies, we find strong support for our theoretical arguments for why buyers and sellers form unilateral online relationships. With our post hoc analysis, we show that on average, buyer-to-seller strategies generate about 10 times more dollars in sales than seller-to-buyer strategies. Second, this paper is the first to reveal the direct effects of buyer, seller, and buyer seller reciprocated relationships on sales in online shopping communities, which allows us to investigate differential payoffs across all three types of online relationships (Study 2). We also evaluate how the effectiveness of the three types of relationships varies dynamically. Unilateral relationships become less important over time, as the seller builds a larger portfolio of unilateral relationships and each additional relationship is less valuable since it represents a worse fit than initial relationships. Reciprocated bilateral relationships instead become more important over time, because more reciprocated relationships typically imply a healthier customer portfolio, in which buyers are more loyal (i.e., reciprocated relational bonds) and likely to return and expand their purchases. A more loyal customer portfolio is especially critical as opportunities to find new, high-quality buyers diminish in maturing marketplaces. By investigating the dynamic effects of online RM strategies, we also extend previous research on online social networks. For example, Katona, Zubcsek, and Sarvary (2011) report that individual s network duration has no effect on their network growth rate, and though our results confirm a lack of a direct effect, they also show that duration has a significant and differential effect on relationship formation. Third, by integrating the results from Studies 1 and 2 and applying elasticity and marginal return analyses, we provide managerial insights into the sales returns on various online RM strategies at different points of buyers and sellers duration on the shopping platform. Results show that relational dynamics have critical influences on online RM; relationship clustering is the most effective RM strategy for generating sales from new buyers (twice as effective as seller s reputation at -1SD in buyer duration) while seller s reputation becomes more effective after the buyer gains more experience (76% higher payoff than relationship clustering at +1SD in buyer duration). Additionally, the returns from reciprocated relationships exceed the returns from buyer-to-seller relationships after sellers have been in the shopping community for about 110 days. Marketing Science Institute Working Paper Series 5

7 Relationship Marketing in Online Shopping Communities Online shopping communities Shopping in online marketplaces is undergoing explosive growth in both developed and emerging markets. Amazon, Alibaba, and Taobao are three of the largest online marketplaces, offering hundreds of thousands of products to millions of customers (Smith 2015). These marketplaces are growing faster than e-commerce as a whole (InternetRetailer 2014). Shopping communities are very popular in these online marketplaces as they provide more interpersonal interactions and shopping experiences that online shoppers are demanding (Dholakia and Vianello 2009). A central purpose of online shopping communities thus seems to be to build relationships among participants (Silverman 2012). Most shopping communities focus on a specific product category or group of related categories (e.g., rural style furniture, comics, children s clothing), to allow people with similar interests to interact, learn from one another, and enjoy the shopping process. Shopping communities on ebay are described as a great place to connect with other community members who share similar interests [g]ive support, share information, and connect with fellow members (ebay.com 2015). Therefore, we define online shopping communities as subgroups of an online marketplace that facilitate interactions among buyers and sellers as part of the shopping experience (Yin 2010). Generally, shopping communities exhibit the following characteristics (1) they reside within a larger online marketplace; (2) focus on a specific product category, topic, or purpose; and (3) promote interactions among buyers and sellers. This interaction characteristic is crucial, because online buyers and sellers are geographically dispersed, so online interactions and the related communication among members is the primary means to build a sense of community (Marsden and Chaney 2012). Marketplaces provide various interaction tools to promote online shopping communities. For example, Etsy maintains various forums and discussions where buyers and sellers can connect, such as online labs and workshops. One member emphasizes the importance of interaction and describes her online shopping community as a big international family with communication happening Help, advice, counsel, encouragement, appreciation, acceptance, affirmation are all a click away (etsy.com 2015). As Table 1 reveals (Tables and figures follow References), extant marketing research on online shopping communities is relatively scarce; most research focuses on social commerce as a Marketing Science Institute Working Paper Series 6

8 whole (Stephen and Toubia 2010) or social networks and product adoption (Katona, Zubcsek, and Sarvary 2011). Stephen and Toubia (2010) investigate the financial implications when sellers form relationships with other sellers and find increases in seller performance, because allowing sellers to connect with one another makes them more accessible to customers. Katona, Zubcsek, and Sarvary (2011) examine how the characteristics of networks and their members influence an individual s decision to adopt a product. They find that a person connected to many other product adopters is more likely to adopt, and the degree of clustering increases this effect. However, no research investigates online shopping communities from both buyer and seller perspectives, the reasons they form relationships, or the dynamic effects on objective seller performance, which is the focus of this research. Online relationship marketing Not only is there a lack of research on online shopping communities in general, little is known about the effectiveness of relationship marketing (RM) in these emerging but financially important online communities of interacting buyers and sellers. Online relationships tend to be less intense than offline relationships (Berry 1993), yet [p]eople who come to an online community are not just seeking information they treat it as a place to meet other people, to seek help, support, friendship (Zhang and Hiltz 2003, p. 411). Still, considering the inherent differences between online and offline relationships, we cannot simply extend offline RM concepts to an online context and expect the same results. Specifically, online and offline RM differs in four key ways. First, online relationship formation is initially unilateral: One party makes a request (e.g., to friend, to follow), and the other party must proactively reciprocate this request (e.g., accept a friend or follower). Relationships can remain one-sided for relatively long periods of time, and each relationship has a clear formation direction, depending on which party initiated the relationship (e.g., sent a friend request). Such factors are uncommon in offline relationships; a person does not walk around with her or his hand extended for a handshake for weeks, waiting for another person to decide whether to reciprocate this relational offer, nor do people generally keep track of who initiated the first handshake. Second, a relationship in an online setting typically is defined by the mere existence of a link between two parties, which is a weaker relationship indicator than the indicators typically used in an offline setting (Colgate, Buchanan- Marketing Science Institute Working Paper Series 7

9 Oliver, and Elmsly 2005). Third, prospective online relational partners evaluate distinct cues, in both type and degree, to determine if they want to form a relationship (Verma, Sharma, and Sheth 2015). For example, similarity can speed trust building, but partners evaluations of similarity differ in an online setting compared with an offline setting. Fourth, relational dynamics and lifecycle effects vary, because shopping and relationship formation seem less certain online than offline (Van Noort, Kerkhof, and Fennis 2007). Thus, effective strategies for building online relationships in early stages may need to focus on reducing relational uncertainty. These four differences suggest that sellers should not assume that the most effective RM strategies in online shopping communities mirror offline strategies, nor that they remain consistent over time. As Stephen and Toubia (2010, p. 217) argue, though similar to offline shopping centers at a basic level, social commerce marketplaces are not merely online equivalents of shopping centers, thus making social commerce a theoretically and substantively interesting context to study. Extant research has provided some insight into online RM. A recent meta-analysis shows that in online commerce settings, similarity and seller expertise have the strongest effects on trust and commitment, and word of mouth is the most important outcome of RM (Verma, Sharma, and Sheth 2015). Similar to offline relationships, Wang, Beatty, and Foxx (2004) find that trust is very important online. However, most extant research does not focus explicitly on shopping communities that organically emerge in online marketplaces. For example, Manchanda, Packard, and Pattabhiramaiah (2015) examine firm-sponsored online communities and find that customers who join them spend more than customers who do not. Overall, because building and retaining the customer relationship is a basic ingredient for long-term success in the competitive e- commerce environment (Ha 2004, p. 189), online RM is an important area of inquiry. Drivers of Relationship Formation (Study 1) In Study 1, we investigate the most effective RM strategies for building online relationships, before considering the sales payoffs of forming these relationships in online shopping communities in Study 2. Due to the unilateral nature of online relationships (i.e., sellers can form a relationship with buyers that is not reciprocated, and vice versa), we study the drivers and payoffs of buyer-to-seller (buyer) and seller-to-buyer (seller) relationship formation separately. In addition, relationships evolve over time, so we evaluate the dynamic effects of RM Marketing Science Institute Working Paper Series 8

10 on buyer and seller relationship formation and the ultimate returns as these relationships develop in the online community. Drivers of buyer relationship formation Buyers generally lack perfect information about sellers or their products. Before forming a relationship with a seller or making a purchase, be it a brick-and-mortar store or an online retailer, buyers need to learn about the seller and its offerings. Buyers do so in order to make themselves less vulnerable to the risk that arises from information asymmetry between the buyer and the seller since perceived risk is more powerful at explaining consumers behavior since consumers are more often motivated to avoid mistakes than to maximize utility in purchasing (Mitchell 1999, p. 163) To lower the various risks associated with making purchasing decisions, such as economic, performance, and time-loss risks (Ha 2004), buyers look for information. According to industry experts, successful online shopping communities thus provide cues that give buyers reliable information about sellers and their products (Chaney 2013). We evaluate three cues buyers might use to learn more about a seller and thus grow more desirous of a relationship with this seller during the initial steps in the purchase process: communication with the seller, seller s reputation, and friends relationships with the seller (or relationship clustering). We evaluate these three risk-reducing cues as online relationship marketing strategies that motivate buyers to form a unidirectional relationship with a seller in an online shopping community (Figure 1, Panel A). Communication. We define communication as the direct exchange of information between a buyer and a seller. In online shopping communities, communication can be initiated by either party and may include a reply or not. Research shows that customer communication is a powerful RM strategy that builds customer commitment and trust (Palmatier et al. 2006). In general, interpersonal, interactive communication encourages long-term relationships between buyers and sellers (Reinartz, Thomas, and Kumar 2005). In online communities, communication may become even more critical; by communicating with a potential buyer, a seller can provide tailored information to reassure the buyer of the seller s ability to meet its specific needs, which lowers both perceived risk and information asymmetry. The seller also signals transparency and trustworthiness to the potential buyer, which should increase the buyer s likelihood of wanting to initiate a relationship (Porter and Donthu 2008; Verma, Sharma, and Sheth 2015). Marketing Science Institute Working Paper Series 9

11 However, in relational exchanges, communication often yields diminishing returns over time (Palmatier et al. 2013). The longer buyers are present in an online shopping community, the more knowledgeable, experienced, and comfortable they become, and the less risk they feel (Zhu et al. 2012). As buyers gain experience, they likely have fewer informational needs, so communication becomes less valuable and less likely to trigger relationship formation. Thus, communication may yield diminishing benefits for buyers as they gain online experience. H 1 : (a) Communication increases the likelihood that a buyer will form a relationship with a seller, and (b) these effects diminish as the buyer s duration in the online shopping community increases. Seller s reputation. Reputation is an indication of the seller's quality, as perceived by the buyer (Baker, Faulkner, and Fisher 1998). A seller s reputation can serve as another source of information for a buyer (Welles and Contractor 2014), because a strong reputation can alleviate consumers perceived risk and concerns about the seller (Pavlou, Liang, and Xue 2007). In online shopping communities, indications of the seller s reputation provide a viable mechanism for fostering cooperation among strangers... by ensuring that the behavior of a trader toward any other trader becomes publicly known and may, therefore, affect the behavior of the entire community toward that trader in the future (Dellarocas 2003, p. 1407). We expect that buyers thus seek out and form relationships with sellers with strong reputations. However, the longer buyers are active in an online shopping community, the more experience, knowledge, and familiarity they gain, which allows them to overcome problems with sellers (Yoon 2002). For example, after a few successful product returns to Amazon or one of its affiliated sellers, a buyer will not be as hesitant to deal with other Amazon sellers, even if they do not have established reputations. Thus, as the buyer becomes more confident and knowledgeable, the value of the seller s reputation as a risk-reducing indicator should diminish. H 2 : (a) Seller s reputation increases the likelihood that a buyer will form a relationship with a seller, and (b) these effects diminish as the buyer s duration in the online shopping community increases. Relationship clustering. In shopping communities, relationship clustering occurs when dense, tightly connected subgroups of buyers and sellers emerge (Van den Bulte and Wuyts 2007). Relationship clusters help buyers assess the credibility of sellers by providing another relevant source of information. That is, to find trustworthy sellers and products that fit their Marketing Science Institute Working Paper Series 10

12 needs, buyers look to see where their friends and friends-of-friends shop. In the absence of other information, people often take cues from the actions of those around them (Chen, Wang, and Xie 2011). For example, on Polyvore, an online marketplace for fashion products, buyers can friend or follow other buyers whose tastes they like, thereby forming relationship clusters of interconnected, similar buyers. When considering whether to purchase a new product, instead of searching the entire online marketplace for credible sellers, a buyer can simply look to see which sellers the other buyers in his or her relationship cluster use. Recent research shows that customers in the same cluster are perceived as more credible and often serve as third parties for information (Hong and Pavlou 2014). Thus, when a buyer observes that friends or friends-offriends have a relationship with a seller, he or she also is more likely to form a relationship with that seller. Over time though, the influence of these friends may diminish. Previous research shows that in online shopping communities, the actions of other buyers influence sales, but this effect diminishes over the product s lifetime (Chen, Wang, and Xie 2011). As buyers gain more experience, they rely less on the behavior of their friends to determine their own actions, because they can depend more on their own knowledge. Iyengar, Van den Bulte, and Valente (2011) provide indirect support for this hypothesis by demonstrating that, compared with less knowledgeable consumers, opinion leaders are less affected by friends actions. As buyers gain experience in the community, they may pay more attention to their own opinions than to relationship clustering inputs. Moreover, we expect that relationship clustering will work synergistically with both communication and seller s reputation to increase the likelihood that a buyer will form a relationship with a seller. Clustering will act as a multiplier to these two other cues by adding credence or weight to the information (reputation and communication) when friends are also linked to the source. For example, seller s reputation or communication likely appears more credible to the buyer when it comes from a source close to the buyer, such as a friend in a relationship cluster. H 3 : (a) Relationship clustering increases the likelihood that a buyer will form a relationship with a seller, and (b) these effects diminish as the buyer s duration in the online shopping community increases. H 4 : Relationship clustering enhances the positive effect of (a) communication and (b) seller s reputation on the likelihood that a buyer will form a relationship with a seller. Marketing Science Institute Working Paper Series 11

13 Drivers of seller relationship formation Online shopping communities generate relationship benefits for both sellers and buyers. As an Etsy seller puts it, shopping communities are the glue that holds us in Etsy. It is our home within the wonderful great big world of Etsy. Without [them] it would be incredibly difficult to get found and establish a reputation (etsy.com 2015). In contrast with buyers, who form relationships with sellers to reduce information asymmetry and perceived risk of buying a product, sellers form relationships to reduce their search costs, incurred through their efforts to find high-quality buyers and persuade them to buy products (Stephen and Toubia 2010). For online sellers, what matters is how many eyeballs get there and how much it costs you to get the eyeballs there, and whether they re good, qualified traffic (Tozzi 2008). Thus, sellers initiate relationships with buyers in online shopping communities to reduce search costs and find highquality buyers (i.e., those more likely to purchase). Sellers use three main approaches to identify high-quality buyers and initiate relationships with them: (1) communication, (2) behavioral similarity, and (3) structural similarity with the buyer. We evaluate these three signals of highquality buyers as strategies that stimulate sellers to form a unidirectional relationship with a buyer in an online shopping community (Figure 1, Panel B). Communication. As we have noted already, communication is the foundation for exchange relationships (Mohr and Nevin 1990), and in online communities, communication activities are undoubtedly factors that attract and retain members (Zhang and Hiltz 2003, p. 411). Through communication, a seller can interact directly with potential buyers to interest them in its products. Communication can be initiated by either party and signals interest and better than average leads, which motivates the seller to form a relationship with the buyer to encourage purchase. As the seller s duration in an online shopping community increases, seller gains more experience with online selling overall and in the community specifically, so seller s targeting and messaging should become more effective. The seller learns which information typically satisfies potential buyers. Over time, sellers become not only more effective communicators but also more capable at identifying high-quality buyers that are worth their effort. For example, an experienced seller may learn that buyers that ask about a specific product feature represent its target market. Instead of spending time communicating with all potential buyers, this seller will focus on communicating with these high-quality buyers. Marketing Science Institute Working Paper Series 12

14 H 5 : (a) Communication increases the likelihood that a seller will form a relationship with a buyer, and (b) these effects strengthen as the seller s duration in an online shopping community increases. Behavioral similarity. Behavioral similarity refers to alignment between a seller and a buyer, in terms of their behaviors (Van den Bulte and Wuyts 2007). In today s e-commerce, it is no longer sufficient for online sellers to list their products for sale; they must engage with potential customers. Some e-commerce experts suggest those serious about selling online write blogs and comment on others, join social networks, and participate in online communities (Tozzi 2008). When buyers and sellers participate in such activities jointly, they exhibit behavioral similarity. In an online shopping context, behavioral similarity often means that both sellers and buyers participate in the same forums, pertaining to specific topics of interest. For a seller, the forums identify high-quality buyers with an interest in the seller s offerings. For example, several forums on Etsy are dedicated to hand-made gold jewelry. A seller who offers this type of product and participates in this forum can identify potential buyers, with whom this seller is more likely to form relationships, because they represent high-quality leads by virtue of appearing in the forum. Research shows that participation in common activities can lead to sellers forming new connections because focused activity puts people into contact with one another to foster the formation of personal relationships (McPherson, Smith-Lovin, and Cook 2001, p. 431). Time and experience alter the effects of behavioral similarity though (McPherson, Smith- Lovin, and Cook 2001). As sellers become more experienced in online shopping communities, they discover particularly appropriate events, forums, and discussions for their target market. With experience, the seller is more able to identify and effectively use relationship formation to generate more business. H 6 : (a) Behavioral similarity increases the likelihood that a seller will form a relationship with a buyer, and (b) these effects strengthen as the seller s duration in an online shopping community increases. Relational similarity. Another type of similarity between a buyer and a seller is based on the commonality of their contacts (Van den Bulte and Wuyts 2007). Relational similarity is a relatively low cost avenue for sellers in online communities to find high-quality buyers, because the sellers can review the friends of buyers with whom they already have relationships to identify Marketing Science Institute Working Paper Series 13

15 leads. Because people tend to form connections with others who are similar to them in demographic characteristics, attitudes, beliefs, or behaviors (McPherson, Smith-Lovin, and Cook 2001), customers in online shopping communities often follow or friend other customers whose style, favorite items, product recommendations, or past purchases they like. Goel and Goldstein (2014) show that people exhibit higher probabilities of clicking on an advertisement (up to 10 times greater) when their social contacts have clicked on that ad. Thus, in searching for highquality buyers, sellers likely form new relationships with buyers who share common friends (Kossinets and Watts 2006). However, as a seller gains experience in the online shopping community, the strategy of finding potential buyers by simply looking at friends of existing buyers might lose effectiveness. As the seller s time and experience in the online shopping community grows, the number of these friends with whom the seller already has formed a relationship grows. When just starting out in an online shopping community, a seller might have relationships with 10 buyers and have rather limited options for finding new leads. As a seller forms relationships with more buyers, the expanded set of friends-of-friends becomes more diverse and lower in quality (worse fit), which undermines the effectiveness of this strategy. Thus, as the seller s time in the community grows, the effectiveness of using friends to find leads diminishes. Sellers then should be less motivated to form relationships with these lower quality potential buyers. Similar to our previous argument that relationship clustering leverages the effects of communication and reputation on buyer relationship formation, we propose that relational similarity interacts synergistically with communication and behavioral similarity to increase the likelihood that a seller will form a relationship with a buyer. That is, relational similarity adds credence to communication and behavioral similarity cues, because when more mutual friends are also buyers, it provides an indication of the seller s relevance. As previous research notes, sharing focal activities and peers greatly increases the likelihood of individuals becoming connected, especially when these conditions apply simultaneously (Kossinets and Watts 2006, p. 90). Therefore, the more commonalities there are between a buyer and a seller, the stronger the signal that this potential buyer might turn into an actual customer and is worth the seller s effort (Silverman 2012). H 7 : (a) Relational similarity increases the likelihood that a seller will form a relationship with a buyer, and (b) these effects diminish as the seller s duration in an online shopping community increases. Marketing Science Institute Working Paper Series 14

16 H 8 : Relational similarity enhances the positive effect of (a) communication and (b) behavioral similarity on the likelihood that a seller will form a relationship with a buyer. Payoffs from Online Relationship Formation (Study 2) In Study 1 we examined the drivers of relationship formation between buyers and sellers in online shopping communities. In this study we examine the payoffs of building these online relationships. Extant research focuses mostly on the indirect effects of social networks, such as Facebook and Twitter, on seller performance (Curty and Zhang 2011); we instead examine the direct payoffs that sellers experience from relationship formation in online shopping communities. Specifically, we evaluate the unique effects of buyer, seller, and buyer seller reciprocated relationships on the seller s sales. Similar to Study 1, we evaluate how their effectiveness varies dynamically too. Whereas in Study 1 we test drivers of individual buyers and sellers behaviors over time (i.e., unit of analysis is at the individual buyer or seller relationship level), in Study 2 we evaluate seller performance at the relationship portfolio level over time (i.e., unit of analysis is at the seller s relationship portfolio level). Accordingly, the dependent variable is seller s performance, defined as seller s weekly sales revenue from all buyers in the online shopping community. Effects of buyer and seller online relationships on seller performance A recent study of a retailer-sponsored online community indicates that joining an online community and forming relationships with other customers both increase customers spending (Manchanda, Packard, and Pattabhiramaiah 2015). We advance this research stream by investigating how relationships between buyers and sellers influence sales in a non firmsponsored online shopping community. Consistent with extant research, we argue that forming a relationship, whether initiated by buyers or sellers, indicates some interest, involvement, or engagement. Because buyers primarily form relationships with sellers to reduce risk and enhance trust, they should be more likely to buy from the set of sellers with whom they already have a relationship (Palmatier 2008). Extensive RM research also shows that buyer relationships increase seller s performance (e.g., Palmatier et al. 2006). Thus, in an online shopping community, we expect that sellers with more buyer relationships outperform sellers with fewer buyer relationships, all else being equal. Similarly, when sellers form a relationship with a buyer, Marketing Science Institute Working Paper Series 15

17 it signals the seller s belief in the buyer s quality or purchase likelihood. Thus, sellers that have built a larger portfolio of high-quality seller relationships should outperform sellers that have built fewer seller relationships, all else being equal. However, the positive effects of the number of unidirectional relationships on seller performance might diminish over time. First, a larger portfolio of buyer and seller relationships implies greater diversity, such that each additional relationship formed should be less valuable and represents worse fit than the initial relationships. For example, a seller initially selects events to find buyers that best overlap with its offering. However, the number of these events that fit well with the seller s target market cannot be infinite; as time progresses, the seller adds buyers from new events, each of which represents a slightly worse fit with its offering, so any buyer attracted through these events likely generates less sales. Second, sellers initially generate most of their sales from recently acquired relationships, but as time progresses, more sales come from the seller s existing portfolio (i.e., repeat business), and new relationships represent a smaller fraction of its sales (Hibbard et al. 2001). H 9 : The number of (a) seller-to-buyer and (b) buyer-to-seller relationships will increase seller s performance, but the seller s duration in the community diminishes these effects of (c) seller-to-buyer and (d) buyer-to-seller relationships on seller performance. Effects of reciprocated relationships on seller performance Finally, in addition to the effect of one-sided relationships, initiated by either a buyer or a seller, we examine the effect of reciprocated relationships on seller s performance. A relationship between a buyer and a seller is reciprocated if it is bidirectional (Van den Bulte and Wuyts 2007). Reciprocity is an important element of relationships and one of the core mechanisms by which firms RM efforts improve their financial outcomes (Palmatier et al. 2006). Relationships in online communities can exhibit reciprocity, and extant research suggests reciprocity is a key structural characteristic of social networks (Ansari, Koenigsberg, and Stahl 2011). A reciprocated relationship between a buyer and seller indicates interest from both sides and should be more likely to generate sales than a unilateral relationship, because both parties are interested in the exchange. The more reciprocated relationships a seller has, the more its sales increase, because it possesses a more bilaterally committed customer portfolio. In contrast with the predicted effects of one-sided relationships that generate diminishing returns for the seller over time, we anticipate that the positive effects of reciprocated Marketing Science Institute Working Paper Series 16

18 relationships increase with time. Research shows that over time, reciprocity leads to relationship expansion, deep loyalty, and a desire to reward a partner directly through more sales and indirectly through positive word of mouth (Palmatier et al 2009). Reciprocated relationships capture the strength of the seller s portfolio of bilateral relationships and these more loyal customers likely have a higher propensity to return for future purchases (retention), expand into other product categories (expansion), and advocate for new customers (acquisition through word of mouth), making these relationship more valuable (Reinartz and Kumar 2003). Over time, sellers that generate more reciprocated relationships develop a healthier customer portfolio, with more buyers who are loyal and offer above-average sales growth, which is especially critical as the seller s ability to find new high-quality buyers diminishes in the maturing market. H 10 : (a) Reciprocated relationships increase seller performance, and (b) these effects strengthen as the seller gains online selling experience. Methodology We test our conceptual model using two separate studies. Study 1 investigates individual buyers and sellers in online shopping communities and the factors that drive their relationship formation (unit of analysis is the individual buyer or seller relationship). Study 2 looks at the dynamic payoffs of the formed relationships, so we evaluate seller performance at the relationship portfolio level over time (unit of analysis is the seller s relationship portfolio). Both studies use the same sample, with data obtained from an online shopping community within Taobao.com, the largest e-commerce platform in China. In 2010, Taobao instituted a new section that organizes all of the platform s shopping communities according to product categories. For this research, we used the clothing online shopping community, which was one of the most active in terms of community members. It had existed for approximately two years prior to our data collection. Data collection procedure and sample To minimize the potential for preexisting relationships among members, we sought data about members who joined the community only after the start of our data collection, on April 1, The data collection lasted approximately six months. To gather longitudinal network data, we programmed a web crawler to search and store data from the online shopping community daily. We obtained data about 482 community members who joined the community during the Marketing Science Institute Working Paper Series 17

19 data collection period: 146 buyers who formed 1074 relationships with sellers, and 336 sellers who formed 1741 relationships with buyers. Measurement When possible, we used existing measures. Table 2 contains a detailed summary of all construct definitions, descriptions, and operationalizations used to test our conceptual model. We start by reviewing the Study 1 measures. The first dependent variable, buyer-to-seller relationship formation, was a binary variable, equal to 1 if a buyer followed a seller, thereby forming a relationship with this seller at time t, and 0 otherwise. Communication was the number of times communication occurred between a buyer and a seller before time t, when predicting relationship formation at time t. Specifically, buyers and sellers generally interact in community topic forums by replying to each other s postings. To measure seller s reputation, we used its number of followers. Relationship clustering was the number of seller s followers that also were followed by a buyer prior to time t. Literature on networks and clustering also suggests including seller s indirect followers who are connected to a seller through a very short path (Van den Bulte and Wuyts 2007). Thus, we included one intermediary, which is consistent with prior research (Van den Bulte and Wuyts 2007). Buyer duration was the elapsed time, in days, since a buyer joined the online shopping community but before forming a relationship with a seller. The control variables included relational similarity, behavioral similarity, number of reviews of each seller before relationship formation, and credit ratings of both buyers and sellers before relationship formation. The seller s (buyer s) credit ratings came from previous buyers (sellers), who evaluated their overall level of satisfaction with previous transactions in which they interacted with the seller (buyer). The second dependent variable, seller-to-buyer relationship formation, was measured as a binary variable that took a value of 1 if a seller followed or formed a relationship with a buyer at time t, and 0 otherwise. Drivers of seller-to-buyer relationship formation included communication (measured as we described previously), behavioral similarity, relational similarity, and seller duration. Consistent with extant research (Kossinets and Watts 2006), we measured behavioral similarity between a seller and a buyer as the number of community events in which they jointly participated before time t. The community we investigated for this research maintains forums on many different topics, such as fashion trends and accessories, in which Marketing Science Institute Working Paper Series 18

20 sellers and buyers can post comments. Behavioral similarity occurred if a seller and a buyer participated in a discussion on the same forum. The more forums in which a buyer and a seller participated jointly, the more behaviorally similar they were. Relational similarity referred to the number of common friends between the buyer and seller before time t (Kossinets and Watts 2006). More mutual friends implied that the seller and buyer were more relationally similar. Seller duration was measured similarly to buyer s duration, as the number of days since the seller joined the community before time t. The control variables included seller s reputation, relationship clustering, the number of reviews of each seller before relationship formation, and the credit ratings of both buyers and sellers, which is the average of all reviews received by the buyer and by the seller, respectively. For Study 2, the dependent variable was seller performance, or the sales of a focal seller in a subsequent time period (i.e., the next week). To gather this information, we downloaded information from Taobao about each seller s daily revenue, which also included product names, product prices, and time stamps for each transaction. We aggregated the transactions of all sellers for each week in the data collection period, to explore the dynamic effects of unilateral relationships (number of seller relationships and number of buyer relationships), as well as of bilateral relationships (number of reciprocated relationships), over time. The number of seller-tobuyer relationships is a count variable of all seller-initiated relationships with buyers in the community, and the number of buyer-to-seller relationships is the same count on the other side. The measure of the number of reciprocated relationships referred to relationships in the community in which both the buyer followed the seller and the seller followed the buyer. We operationalized the seller s duration the same way as in Study 1. Table 3 contains the descriptive statistics and correlations for all constructs. Estimation and Results: Study 1 Network research frequently uses hazard analysis to investigate events affected by a series of covariates (Kossinets and Watts 2006). To estimate the conceptual model of Study 1, we used a Cox proportional hazard regression model (Cox 1972). Relationship formation is a timebased binary event, and the probability of this formation over time is a function of time-varying independent variables. Time-based phenomena can be modeled best by a hazard function, which can identify cross-sectional and longitudinal effects, as well as handle sample selection biases Marketing Science Institute Working Paper Series 19