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UNDERSTANDING THE FORMATION OF RECIPROCAL HYPERLINKS BETWEEN SELLERS IN AN E-MARKETPLACE
Zhaoran Xu1, Youwei Wang1, Yulin Fang2, Bernard Tan3, Hai Sun1
(1. Department of Information Management and Information Systems, Fudan University; 2. Department of Information Systems, City University of Hong Kong;
3. Department of Information Systems, National University of Singapore)Abstract
Online sellers in the e-marketplace cooperate with each other to increase resources and reduce
transaction costs, both of which are crucial to the success of small businesses. A commonly used IT-
enabled strategy is to ally with other online sellers by exchanging hyperlinks. This paper provides
theoretical guidance to sellers on how to choose partners to improve reciprocity rates in hyperlink
formation. Using the resource-based view and transaction-cost rationale, we examine the effects of
market conditions and seller reputation on reciprocity link formation, using real transaction data from
the largest online marketplace in China. The findings indicate that partners are less likely to exchange
hyperlinks if the two sellers sharing a link are in highly overlapping markets and are geographically
distant from one another, but the two factors weaken each other’s negative effects. The study also
explores the moderating effect of seller reputation, and finds that the negative effect of market
commonality is weakened by seller reputation. The results of this study can be extended to other types
of small business cooperation and are also useful to platform operators for designing mechanisms to
encourage cooperation among online sellers.
Keywords: online seller, hyperlink exchange, market commonality, geographical distance, seller
reputation
1 INTRODUCTION
Over the past decade, more and more sellers have conducted their business on e-marketplace
platforms, such as eBay, Amazon and Taobao (an e-marketplace owned by Chinese e-commerce giant
Alibaba). The large number of sellers leads to intensified competition in the e-marketplace. For
example, by the end of 2013, more than eight million active sellers were competing on Taobao. The
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majority of these are small business owners [1-3] with limited resources and market presence, and are
vulnerable to environmental forces [4]. According to a recent study [5], in 2010, 38.4% of the sellers
in apparel shut down their businesses within six months of establishing their stores on Taobao. To
survive the competition, online sellers cooperate with each other and undertake the same alliance
activities as traditional bricks-and-mortar firms, such as co-branding and co-marketing. In addition,
they deploy distinct strategies which rely on Internet infrastructures in an e-marketplaces [6]. The
strategies involve forming alliances with other online sellers by exchanging website hyperlinks.
Hyperlink is an important feature of World Wide Web (WWW) [7]. It enables visitors to jump
from one webpage to another by clicking on links embedded in the hypertexts. Because there are
numerous webpages with similar information contents or services, these webpages have to attract
visitors’ attention by unique service offerings and techniques. Exchanging hyperlinks is a technique
commonly utilized by Internet web services (more broadly those techniques fall into the category of
search engine optimization). It is well known that exchanging hyperlinks can lift the rankings of the
website in major search engines such as Google [8, 9]. This is because websites with large number of
hyperlinks will be given higher priority in search engines’ ranking algorithm. In e-marketplaces, each
seller manages an online store and the key motivation of exchanging hyperlinks is simply to attract
and exchange web browsing traffic because this can lead to more online purchases [10, 11]. Taking
Taobao as an example, a focal seller links to another seller (link seller) by putting a hyperlink on its
store front (typically on the left sidebar). This is an outgoing link for the focal seller. When customers
browse the focal seller’s shop, they may visit the link seller by clicking on the hyperlink. The link
seller can establish a hyperlink back the focal seller, which would be an incoming link or reciprocal
link for the focal seller. When this happens, the two sellers exchange hyperlinks successfully.
Prior studies have shown that exchanging hyperlinks can help online sellers to achieve success
by growing the customer base [12], enhancing customer trust [13, 14], and eventually improving
sellers’ competitive advantage [15]. Hyperlinks can improve the seller’s revenue and profit but the
effects only lies in the incoming links [16]. Incoming links make it easier for customers to discover
the seller whereas outgoing links can reduce customer traffic and undermine the performance of the
2
seller [17]. Thus, online sellers with more incoming hyperlinks and fewer outgoing links tend to
perform better [10]. If hyperlink exchange fails and the target seller does not reciprocate by placing a
hyperlink back to the focal seller on its online store, the focal seller may suffer the loss of customers
from the outgoing links initiated.
Although it is obvious that having more incoming links and less outgoing links are often good
for focal sellers [10, 17], it is not clear how such goals can be achieved. This is because each seller
can only control its outgoing links but not the incoming links. What usually happens is that sellers
initiate hyperlinks to other sellers in the hope that some of them will reciprocate by linking back.
However, no prior study has examined how sellers may get more reciprocal links in e-marketplaces.
To fill this important gap in knowledge, this paper investigates the following research question: What
types of partner sellers are more likely to exchange reciprocal hyperlinks in an online marketplace?
To address this question, we build on the literature of alliance formation in the field of strategy
by conceptualizing hyperlink exchanges as exchanges of customer resources. In the strategy literature,
there are two kinds of antecedents in alliance formation. One is the individual characteristics of the
partner firm, such as status or age [18]; and the other is the dyad characteristics between the alliance
partners, such as market commonality [19]. These factors can be better understood using theoretical
perspectives like resource-based view and transaction-cost rationale [20].
Utilizing a dataset collected from Taobao, this study proposes and empirically validates the main
effects and the interaction of market commonality and geographical distance (two market conditions
between the partner sellers), and the moderating effects of reputation (a key characteristic of online
sellers). Market commonality is the extent of overlap in the market segments of alliance partners.
Geographical distance is the physical distance between alliance partners. Specifically, this study finds
that sellers are less likely to exchange hyperlinks if they operate in highly overlapping markets and
are geographically distant from each other, but the two factors weaken each other’s negative effects.
In addition, the negative effects of market commonality are ameliorated by seller reputation. These
findings have important theoretical and empirical implications. Theoretically, these findings extend
the generalizability of related theories from traditional bricks-and-mortar industries to e-marketplaces.
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Practically, these findings help online sellers to make better decisions about how to cooperate with
other sellers and help platform operators make better decisions about how to enhance collaborations
in e-marketplaces.
2 THEORETICAL BACKGROUND
2.1 Resource-based view and transaction-cost rationale on alliances in e-marketplaces
The strategy literature offers theoretical guidance as to why firms enter into alliances. The
resource-based view argues that firms should possess resources that are rare, valuable, imperfectly
mobile, and non-substitunntable to achieve competitive advantage [21]. Thus, developing and
leveraging resources is a key driver for alliance formation [22]. The transaction-cost rationale
recommends that firms form alliances to minimize their fixed and continual transaction costs [23].
The literature suggests that firms tend to put resource concerns ahead of cost concerns when deciding
whether or not to engage in alliances [24] but considerations of economic costs can influence inter-
firm relationships [25]. Therefore, this study supplements the resource-based view with the
transaction-cost rationale in discuss alliance formation in e-marketplaces.
Alliance formation may differ depending on industry structure and competitive situation [26].
Given that this study is about e-marketplaces, the characteristics of the online environment have to be
considered when examining resource maximization and cost minimization in alliance formation.
Online sellers in e-marketplaces are small businesses which lack resources [27]. The resource-based
view suggests that, to survive, they need access to resources of alliance partners, especially customer
resources [6]. Indeed, it is critical for these small business to expand their customer base [28]. But it is
difficult for these small business to retain customers because online retailing allows customers to
transact with many different sellers [29]. Thus, in e-marketplaces, customer resources are vital but
easy to come and go. Sellers engage in alliances to cooperate and compete for customer resources at
the same time. The cooperation level (or competition level) depends on market conditions, such as
market commonality and geographical distance [30].
Another important resource for online sellers is their reputation. Customers attach considerable
importance to seller reputation in e-marketplaces [14] and seller reputation positively affects revenue 4
and total sales [31, 32]. Online sellers should consider the reputation of prospective partners when
forming alliances [33]. A good reputation improves the bbenefits of forming alliances [34]. Thus, this
study also examines the role of seller reputation in the exchange of hyperlinks.
Unlike production costs, transaction costs are incurred in organizing information, coordinating
behaviour, monitoring transactions, and safeguarding interests [35]. Online sellers use e-marketplaces
to select alliance partners and execute transactions, which lower search costs compared to traditional
approaches [36]. However, coordination costs are higher in e-marketplaces for two reasons. First, the
online environment is complex in the sense that partners can leverage on environmental uncertainty
and information asymmetry to be more opportunistic [37]. Second, competition tends to be fiercer in
e-marketplace and this creates conflicts in alliances, which increase coordination costs [38]. Thus, e-
marketplace alliances incur lower search costs and higher coordination costs compared to alliances in
bricks-and-mortar industries.
2.2 Market conditions and alliance formation in e-marketplaces
This study examines two market conditions: market commonality and geographical distance.
Market commonality is commonly known as “the degree of presence that a competitor manifests in
the markets it overlaps with the focal firm” [39]. When firms operate in overlapping markets, they
have higher market commonality and more collective strength compared to partner firms operating in
distinct industries [19].
Research has shown that firms with high market commonality are more likely to form alliances
because they share similar resources. This makes it easier for them to achieve economy of scale by
aggregating similar resources through alliances [19, 40]. Although there may be higher search costs
associated with screening prospective alliance partners [41], firms in the same markets are usually
quite familiar with each other and share common market knowledge [42]. This familiarity decreases
the costs of searching for prospective partners and makes the process less time-consuming [43]. Thus,
firms with high market commonality are likely to form alliances.
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But research has also suggested that firms with low market commonality tend to cooperate [18,
22, 43]. Firms that are in different markets can also form alliances to develop new and complementary
resources [44]. In this situation, firms tend to cooperate when their partners have strengths that can
make up for their weaknesses [18]. They also cooperate to exploit business opportunities from their
partners in different markets [40]. Besides, firms with low market commonality are less likely to find
themselves competing in the same market [45]. Overall, past studies have indicated that market
commonality is important to alliance formation. However, given that the business environment in e-
marketplaces is different, it is important to re-examine this issue of alliance formation in the context
of e-marketplaces.
Geographical distance is commonly known as the spatial or physical distance between economic
actors, such as alliance partners [42]. Geographical distance is well documented in the international
business [46, 47] and industry cluster literature [48, 49]. In alliance formation, remote partners can
help firms reach out to different, diverse, and non-redundant resources that co-located firms cannot
[47]. Because it can be difficult for firms to reach customers in distant markets, remote partners can
provide access to these new markets, thus making alliance formation more likely [6, 12].
But having co-located partners can lowers cost associated with distance (such as communication
costs [50]) and search costs for identifying useful competences [51]. In addition, geographically
distant partners are more likely to behave opportunistically, which can undermine trust in the alliance
[42]. Thus, geographically distant firms tend to prefer deeper inter-partner relationships (e.g ., joint
venture or merger and acquisition) over alliances [52].
Studies have also reported that geographic distance is not particularly relevant to alliances under
heterogeneous industry characteristics [49]. Furthermore, advances in communications technologies
may weaken the role of geographical distance [53]. Therefore, it is useful to re-examine the role of
geographical location in an online environment. The effects of the two market conditions may be
interdependent. For example, partners in different markets (i.e., low market commonality) can help
each other access new markets [40] but geographical distance can make it more difficult and costly to
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form alliances with partners [52]. Therefore, this study explores the interaction effects of market
commonality and geographical distance.
2.3 Firm reputation and alliance formation in e-marketplaces
Compared to bricks-and-mortar shops, customers tend to perceive more risks in transacting with
sellers in e-marketplaces. Thus, the reputation of online sellers plays a significant role in attracting
customers [54]. The concept of reputation is different from that of status but prior studies have often
confused the two [55]. Status represents the order or rank of the firm in a market whereas reputation
indicates the quality of the firm as determined by its previous actions and this is a market signal when
information is asymmetric [56].
The resource-based view suggests that reputation is a valuable resource to the firm [18] and can
affect the exchange of resources because high reputation enhances trust in inter-firm relationships
[57]. The transaction-cost rationale suggests that opportunism is a major concern in alliances and so a
good reputation helps to reduce cooperation costs by enhancing trust. For example, negotiation costs
are reduced when reputation improves trust between partner firms [58]. Thus, reputation can enhance
the competitive advantage of partners and has a positive effect on alliance formation [59]. This study
focuses on the moderating role of reputation on the effects of market conditions. For example, the
good reputation of the potential partner may reduce the opportunistic risk arising from geographical
distance.
3 HYPOTHESES DEVELOPMENT
3.1 Main effects of market conditions
Market commonality is commonly measured by sellers’ product categories [18, 22, 43]. A single
online seller can have multiple product categories. Overlapping categories between two partner sellers
increase market commonality.
Based on the resource-based view, a major benefit of forming alliance among online sellers is
that this increases the customer base for all sellers in the alliance [12]. As discussed above, research
has shown that both high and low market commonality between alliance partners can improve their
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access to customers [22, 40, 43]. Traditionally, firms in overlapping markets form alliances to share
similar resources and compete with outsiders [19, 60]. But in e-marketplaces, there can be numerous
sellers in each product category that alliances are often loose and informal [28]. Moreover, sellers
with high market commonality are competitors [61] because it is easy for customers to move from one
seller to another. Thus, plenty of online traffic flows through hyperlinks connecting sellers. These
links provide opportunities for customers to leave [10], undermining the long-term interests of sellers
[29]. In this situation, partner sellers with many overlapping products (i.e., high market commonality)
are at greater risk of losing customers to their partners. On the contrary, partner sellers with few
overlapping products (i.e., low market commonality) can help each other by facilitating customer
purchase from both sellers through their hyperlinks.
The transaction-cost rationale suggests that search costs associated with finding the right partner
are lower when partners have high market commonality in bricks-and-mortar industries [40].
However, this cost advantage is not as salient in e-marketplaces because search costs are low,
regardless of market commonality [37]. Thus, the lower search costs that come with high market
commonality are not applicable in e-marketplaces. On the contrary, competition arising from high
market commonality brings more costs, such as the need to offer extra services to retain customers
[4]. Therefore, market commonality has a negative effect on hyperlink exchange (i.e., alliance
formation) in e-marketplaces:
H1: Partner sellers are more likely to exchange hyperlinks if market commonality among the
two sellers (in terms of product categories) is lower.
Every online seller has a physical location (their inventory site). In this study, geographical
distance is measured by absolute distance between a pair of partner sellers based on their respective
physical location [62]. Taking the resource-based view, prior research suggests that long-distance
alliances can provide access to new customers in remote markets and such access would be difficult
without the alliances [47, 48]. However, in e-marketplaces, market access is not restricted by location
because sellers can easily overcome barriers of geographical distance to reach far-flung customers.
Prior research also suggests that having nearby partners can help sellers increase their customer base
8
because customers prefer the convenience of visiting retail clusters [63]. Having nearby partners bring
in other resources, such as access to common specialised suppliers and skilled labour pools [48]. In e-
marketplaces, closely-located partnerships can still increase customer base because customers may
prefer to make multiple purchases from nearby sellers for reasons such as lower shipping costs and
shorter delivery schedule for these purchases collectively [63, 64]. Thus, lower geographical distance
can still contribute to alliance formation.
Past research suggests that it can be more costly to monitor and coordinate geographically distant
partnerships because partners are more likely to behave opportunistically [42, 46]. This opportunism
associated with environmental uncertainty [65] tends to be more pronounced in e-marketplaces due to
the volatility [37]. For example, a seller may pursue an unfair competitive strategy (e.g., offering deep
customer discounts) that undercuts its partner seller. Geographical distance can also undermine trust
between partner sellers. This is because sellers tend to be less familiar with the service and product
quality of distant partners and risk associating with disreputable distant partners [41]. Therefore,
geographical distance has a negative effect on hyperlink exchange (i.e., alliance formation) in e-
marketplaces:
H2: Partner sellers are more likely to exchange hyperlinks if geographical distance among the
two sellers is shorter.
3.2 Interaction effects of market conditions
The negative effects of market commonality on alliance formation lie in the loss of customer
resources when partner sellers’ markets overlap [40, 61]. However, geographical distance can reduce
this loss of customer resources because, if the partner sellers are far apart, there is a lower chance that
customers will move from one seller to its partner seller through hyperlinks, given that customers still
prefer nearby sellers for reasons such as lower shipping costs and shorter delivery schedule [64]. In
this way, geographic distance between partner sellers can lessen their competition in overlapping
markets and mitigate the associated costs. Therefore, geographical distance can weaken the negative
effects of market commonality and so sellers far away from each other are more likely to form
alliances even if they are in highly overlapping markets.
9
Based on the transaction-cost rationale, sellers tend to avoid long-distance partnerships because
these partnerships can incur higher monitoring and coordination costs as well as increase the risk of
associating with low-quality partners [46]. To avoid engaging with an opportunistic partner, sellers
need more market information to assess a prospective partner’s likely future behaviour [66], and
sellers with high market commonality tend to be more familiar with each other . This can reduce the
coordination costs of alliances that arises from geographical distance [42]. Thus, market commonality
can weaken the negative effects of geographical distance and so sellers in highly overlapping markets
are more likely to form alliances even if they are further away from each other.
H3: Market commonality and geographical distance have interaction effects such that they
weaken each other’s negative effects on the likelihood of exchanging hyperlinks.
3.3 Moderating effects of reputation
Reputation plays a very important role in e-marketplaces because of the uncertainty of the online
environment [54]. Sellers can improve their reputation and thereby attract more customers by forming
alliances with partners with good reputation [34]. The benefits of seller reputation in e-marketplaces
are well-known [59]. Going beyond past studies, this study examines the moderating effects of seller
reputation on market conditions (market commonality and geographical distance).
Partners with high market commonality tend to have intensive competition that causes a loss of
customers [40, 61] so high market commonality has negative effects on alliance formation. This
situation is aggravated when a partner seller has a better reputation that enables it to attract more
customers [18, 22]. Seller reputation is easily accessible in e-marketplaces due to ready availability of
customer feedback. Therefore, high reputation of a partner seller strengthens the negative effects of
market commonality on alliance formation.
H4: Higher reputation of a partner seller strengthens the negative effects of market commonality
on the likelihood of exchanging hyperlinks.
Geographical distance increases the monitoring and coordination costs as well as the risk of
associating with opportunistic partners [46]. To avoid engaging in less productive alliances, sellers
can assess a prospective partner’s likely future behaviour using its track record of past behaviour [66]. 10
This practice may be particularly important for partners that are far away from each other given that
they tend to have less information about each other [41]. However, such costs and risk arising from
geographical distance is alleviated when a partner seller has a better reputation because such partner
sellers tend to collaborate with other sellers so as to improve the overall customer purchase experience
[59]. Therefore, high reputation of a partner seller weakens the negative effects of geographical
distance on alliance formation.
H5: Higher reputation of a partner seller weakens the negative effects of geographical distance
on the likelihood of exchanging hyperlinks.
4 METHODOLOGY
4.1 Dataset
Our dataset was obtained from Taobao, the largest e-marketplace in China. Taobao labels
hyperlink exchanges as “friendship links”. Apparel sellers were examined in this study because
apparel has been the best-selling product on Taobao. In 2011, there were 375,000 apparel sellers
accounting for about 20% of Taobao’s 68 seller categories. Competition was very intense in such a
market and therefore sellers would be motivated to collaborate in various ways, including exchanging
friendship links.
Our data was collected in January 2011. The data was analysed at the dyad level, with the link
itself as the unit of analysis. Each link involved two sellers: a focal seller and a link seller. There were
two scenarios: (1) a focal seller sent an outgoing link to a link seller and the link seller decided
whether to send a reciprocal link back to the focal seller; and (2) a focal seller received an incoming
link from a link seller and the focal seller decided whether to send a reciprocal link back to the link
seller. Information on both scenarios was included in our dataset.
A total of 1,000 apparel sellers were randomly chosen as our focal sellers. Information was
collected for friendship links of focal sellers (including outgoing hyperlinks and incoming hyperlinks)
which met the following requirements: (1) the hyperlinks should be voluntary; (2) the hyperlinks
should connect sellers with different identities (seller IDs); (3) two sellers connected by the hyperlinks
should not share the same mailing address; and (4) the reciprocal hyperlinks were created within 20
11
days of the original hyperlinks (because the reciprocal hyperlinks might not be triggered by the
original hyperlinks if the time interval was too long). Our dataset comprised 924 outgoing hyperlinks
and 701 incoming hyperlinks for 323 focal sellers in the apparel industry. Results of t-tests revealed
that the difference between stock levels of sellers in our dataset and sellers in the population was not
significant.
4.2 Measurement
4.2.1 Dependent variable
When sellers received incoming hyperlinks as a consequence of sending outgoing hyperlinks to
other sellers, these would be deemed reciprocal hyperlinks. Our analyses examined whether each
hyperlinks was reciprocated and measured this as a binary variable, consistent with other studies on
alliance partner selection [6, 22, 67]. Each outgoing hyperlink sent by a focal seller would be coded
“1” if the focal seller received a reciprocal hyperlink after and “0” otherwise. Each incoming
hyperlink received by a focal seller would be coded “1” if the focal seller sent a reciprocal link after
and “0” otherwise.
4.2.2 Independent variables
Market commonality. Product category was used to measure the market commonality of sellers
[18, 22, 43]. Taobao classifies the products on its platform into 68 categories and codes sellers into
these categories based on major products they sell (using an algorithm that considers recently sold
products). Each seller would have one or two major product categories. Market commonality was
computed as follows:
Market commonality=¿ Focalselle r ' scategories ∩ Link selle r ' scategories∨ ¿¿ Focal seller ' scategories∪ Link selle r ' scategories∨¿¿
¿
For example, if a focal seller traded apparel and bags and a link seller traded apparel and
cosmetics, then they would have one common category (apparel) out of three categories (apparel,
bags, and cosmetics) and their market commonality would be 0.33. The lowest possible market
commonality of two sellers would be 0 (if they had no common categories) and the highest possible
market commonality of two sellers would be 1 (if all their categories were identical).
12
Geographical distance. Geographical distance was computed based on absolute distance between
the registered cities of the focal seller and the link seller [62]. This was measured in units of 1,000
kilometres to enlarge the coefficient estimates.
Reputation. After completing a purchase, customers in Taobao would rate the product quality of
the online seller. Seller reputation was measured by the ratio of positive ratings.
4.2.3 Control variables
Factors that might influence the likelihood of obtaining reciprocity hyperlinks were included as
control variables. A control variable was the seller’s business tenure (measured by number of months
since the seller’s online store was established). Established sellers might have more legitimacy in the
e-marketplace [68-70]. Thus, it might be beneficial for a new seller to collaborate with an established
seller but not the other way round. Another control variable was the numbers of existing friendship
links. This reflected the inclination of sellers to exchange hyperlinks and might affect their decisions.
Another control variable was whether sellers participated in the consumer rights safeguarding plan
(CRSP) tend to attract more customers.1 Sellers might prefer to exchange hyperlinks with partners that
participated in CRSP. Table 1 shows the measurements of all the variables.
Table 1. Variable measurements
Variables Measurements1 Reciprocal link 1 if focal seller or link seller received a reciprocal link, 0 otherwise
2 Market commonality ¿ Focal seller ' s categories∩ Link seller ' scategories∨ ¿¿ Focal selle r ' s categories∪Link selle r ' scategories∨¿¿
¿
3 Geographical distance Absolute distance between the registered cities of the focal seller and the link seller (in 1,000 kilometres)
4 Seller reputation Online seller’s ratio of positive ratings5 Seller tenure Online seller’s store age (in months).6 Friendship links Online seller’s number of existing friendship links7 Assurance mechanism 1 if online seller participated in CRSP, 0 otherwise
4.3 Estimation method
The dependent variable was binary in nature. Correspondingly, a logit regression was used for
estimation. The outcome variable p was the probability of receiving reciprocal hyperlinks.
p=Pr ( y ij=1)
1 Sellers participating in CRSP placed a deposit into their platform accounts. This deposit would be used to reimburse customers in instances where sellers were found to be at fault in transaction disputes. Participating sellers would be marked with a conspicuous icon on their online storefronts so customers could easily identify such sellers.
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y ij={1, if reciprocal hyperlink recived0 , otherwise .
Because each seller could create multiple friendship links, the dataset was an unbalanced panel
data (with the seller as the panel variable and the hyperlink as the time variable). Random effects (RE)
model was used because RE parameter estimates could be applied to a random sample of the entire
population. Fixed effects (FE) model could not be used because FE parameter estimation required
variances in the dependent variable for the same seller. Therefore, if FE model was used, 225 sellers
out of the 327 sellers would have to be dropped and the sample size would then be too small. Thus,
we used RE model and the logit model specification as follows:
logit ( p )=ln ( p1−p
)=α i+x ij β+εij , α i IID ( α , σ2 ) ,
where x ij was the vector of independent variables and αi was a random variable distributed
independently of the regressors. The model was estimated through a maximum likelihood procedure
[71].
5 DATA ANALYSES
We analysed two situations in this study. In the first situation, focal sellers initiated hyperlink to
link sellers and it would be up to link sellers to decide whether to follow up with reciprocal hyperlinks
(focal seller → link seller). In the second situation, focal sellers received hyperlink invitations from
link sellers and it would be up to focal sellers to decide whether to follow up with reciprocal
hyperlinks (link seller → focal seller). Tables 2 and 3 summarize the descriptive statistics for the two
situations and show the Pearson correlation coefficients between the variables. Market commonality,
geographical distance, and seller reputation were significantly correlated to the dependent variable.
Multicollinearity was not an issue because the maximum variance inflation factor was 1.06 and 1.02
for apparel sellers initiating and receiving hyperlinks respectively, far below the threshold of 10 [72].
A hierarchical moderated regression was used to test the hypotheses. The variables were mean-
centered [73]. Models 1 to 5 in Table 4 present the regression results for the situation where focal
sellers first sent hyperlink invitations to link sellers. Market commonality and geographical distance
14
had negative main effects on the likelihood of obtaining reciprocal hyperlinks (see Model 1), thus
supporting H1 and H2. Market commonality and geographical distance had a positive interaction on
the likelihood of obtaining reciprocal hyperlinks (Model 2). The results suggested that market
commonality and geographical distance weakened the negative effects of each other, thus supporting
H3. Market commonality and seller reputation had a negative interaction on the likelihood of
obtaining reciprocal hyperlinks (Model 3). The results suggested that seller reputation strengthened
the negative effects of market commonality, thus supporting H4. Geographical distance and seller
reputation had no interaction on the likelihood of obtaining reciprocal hyperlinks (Model 4). The
results suggested that seller reputation did not change the negative effects of geographical distance,
thus rejecting H5. These results remained robust when all the interaction terms were examined
together (Model 5). Models 6 to 10 in Table 4 present the regression results for the situation where
focal sellers first received hyperlink invitations from link sellers. The results were consistent with
those in Models 1 to 5, thereby providing further evidence of robustness of these results.
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Table 2. Descriptive statistics and Pearson correlation matrix for apparel industry (focal seller → link seller)Variable Mean S.D. 1 2 3 4 5 6 7
1 Reciprocal link 0.26 0.44 1.002 Market commonality 0.17 0.27 -0.12*** 1.003 Geographical distance 1.26 1.31 -0.09*** 0.03 1.004 Focal seller reputation 0.99 0.01 0.10*** 0.08** 0.07** 1.005 Focal seller tenure 13.54 15.68 0.04 -0.06* -0.09*** 0.09*** 1.006 Focal seller friendship links 13.08 14.00 0.29*** -0.09*** -0.01 0.04 0.03 1.007 Focal seller assurance mechanism 0.71 0.45 0.09*** -0.14*** 0.02 -0.10*** 0.21*** 0.02 1.00N = 924; * p < 0.1, ** p < 0.05, *** p < 0.01
Table 3. Descriptive statistics and Pearson correlation matrix for apparel industry (link seller → focal seller)Variable Mean S.D. 1 2 3 4 5 6 7
1 Reciprocal link 0.31 0.46 1.002 Market commonality 0.13 0.25 -0.08** 1.003 Geographical distance 1.07 1.09 -0.12*** -0.04 1.004 Link seller reputation 0.99 0.01 0.07** -0.03 -0.03 1.005 Link seller tenure 14.36 15.45 0.02 -0.07* -0.01 -0.01 1.006 Link seller friendship links 19.27 23.38 -0.11*** -0.02 -0.11*** 0.03 -0.11*** 1.007 Link seller assurance mechanism 0.62 0.49 0.08* -0.10** -0.02 -0.01 0.07** 0.01 1.00
N = 701; * p < 0.1, ** p < 0.05, *** p < 0.01
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Table 4. Regression results
Focal seller → Link seller Link seller → Focal seller(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Market commonality -0.966** -0.890** -0.844** -0.965** -0.767* -1.210** -0.990* -1.290** -1.206** -1.116**
(-2.31) (-2.10) (-2.00) (-2.30) (-1.79) (-2.39) (-1.94) (-2.49) (-2.38) (-2.15)
Geographical distance -0.183** -0.183** -0.183** -0.203** -0.201** -0.457*** -0.428*** -0.455*** -0.453*** -0.428***
(-2.11) (-2.11) (-2.13) (-2.18) (-2.17) (-4.02) (-3.71) (-4.02) (-3.94) (-3.71)
Seller reputation28.37** 28.66** 26.38** 31.29** 28.92** 36.79* 36.32* 35.07 37.13* 36.38*
(2.22) (2.22) (2.09) (2.26) (2.09) (1.72) (1.69) (1.61) (1.74) (1.66)Market commonality* Geographical distance
0.684** 0.666** 0.912* 0.888*
(2.17) (2.12) (1.81) (1.76)Market commonality*Seller reputation
-77.83* -76.50* -174.3** -165.0**
(-1.87) (-1.79) (-2.08) (-2.03)Geographical distance*Seller reputation
6.716 5.843 5.135 0.0752(0.60) (0.52) (-0.24) (0.00)
Seller tenure -0.0104 -0.0112 -0.0105 -0.0104 -0.0113 -0.0020 -0.0021 -0.0018 -0.00200 -0.00192(-1.28) (-1.37) (-1.31) (-1.28) (-1.40) (-0.28) (-0.29) (-0.25) (-0.28) (-0.27)
Seller friendship links 0.0500*** 0.0503*** 0.0498*** 0.0501*** 0.0502*** -0.0028 -0.0021 -0.0031 -0.00274 -0.00255(7.13) (7.14) (7.14) (7.14) (7.15) (-0.28) (-0.21) (-0.31) (-0.27) (-0.25)
Seller assurancemechanism
0.699** 0.704** 0.720** 0.704** 0.726** 0.288 0.304 0.296 0.287 0.309(2.32) (2.32) (2.42) (2.34) (2.43) (1.24) (1.30) (1.28) (1.23) (1.32)
Intercept -2.265*** -2.274*** -2.267*** -2.283*** -2.287*** -0.720*** -0.721*** -0.740*** -0.720** -0.738**
(-7.63) (-7.62) (-7.73) (-7.64) (-7.70) (-2.63) (-2.62) (-2.71) (-2.63) (-2.68)N 924 924 924 924 924 701 701 701 701 701Log likelihood -454.84 -452.56 -453.16 -454.65 -450.86 -381.12 -379.57 -379.11 -381.09 -377.60
t statistics in parentheses;* p < 0.1, ** p < 0.05, *** p < 0.01
17
6 DISCUSSION AND IMPLICATIONS
6.1 Discussion of results
This study examines the effects of two market conditions (market commonality and geographic
distance) as well as the moderating effects of seller reputation on hyperlink exchange between sellers
in an e-marketplace. The results that support H1 suggest that two sellers are more likely to exchange
hyperlinks if they have low market overlap. In e-marketplaces, customers can move among sellers
with low switching costs [29]. Thus, market commonality creates substantial competitive tension for
alliance partners and online sellers who rely on the same customer resources are likely to become
direct competitors. However, the results that support H3 and H4 delineate the theoretical boundaries
of the effects of market commonality. In particular, the results that support H3 show that market
commonality has a positive effect on two sellers exchanging hyperlinks if they are located far away
from each other (see Figure 1), and the results that support H4 show that market commonality has a
positive effect on two sellers exchanging hyperlinks if the seller initiating the hyperlink has poor
reputation (see Figure 2). Consistent with past findings on firm alliances [19, 40], these results
demonstrate that long distance between partners or poor partner reputation can reduce competition in
high commonality situations because sellers have a better chance of attracting the customers of their
partners.
Figure 1. Geographic distance moderating the effects of market commonality
18
Figure 2. Seller reputation moderating the effects of market commonality
The results that support H2 suggest that two sellers are more likely to exchange hyperlinks if
they are geographically close to each other. In e-marketplaces, collaborating with nearby partners
allow sellers to offer customers lower shipping costs and shorter delivery schedule for their purchases
from partner sellers collectively [63, 64]. However, the results that support H3 show the theoretical
boundaries of the effects of geographic distance. Specifically, geographic distance has a positive
effect on two sellers exchanging hyperlinks if they have high market overlap (see Figure 3). The
results that reject H5 show that the effects of geographic distance are not moderated by seller
reputation. Even though high reputation of prospective partners can reduce the costs and risk of sellers
collaborating with these prospective partners, such reputation information may have a weak influence
[74] considering that the e-marketplace allows sellers to gather reasonably accurate information about
prospective partners located anywhere (nearby or far away).
19
Figure 3. Market commonality moderating the effects of geographic distance
6.2 Theoretical implications
Prior studies have explained the outcomes of hyperlink exchanges among sellers [10, 14, 17] but
these studies have not provided theoretically-grounded explanations for the factors (or combination of
factors) that lead to hyperlink exchanges. Because hyperlink exchanges are critical for the sustenance
of the community of sellers in e-marketplaces [10], it is important to understand the factors that
promote or hinder such alliances among sellers. In this regard, this study goes beyond past studies in
determining the antecedents of hyperlink exchanges among sellers in e-marketplaces.
This study investigates alliance formation outside the traditional bricks-and-mortar context in the
e-marketplace context. The results re-assess the generalizability of theories (such as resource-based
view and transaction-cost rationale) in the new context. Extending past findings about the effects of
market commonality on alliance formation in the traditional brick-and-mortar context [22, 40, 43],
this study re-examines these effects of market commonality in the e-marketplace context and show
that the effects arising from market commonality can be moderated by geographic distance and seller
reputation. While past studies have discussed the paradox due to geographic distance [46, 47, 48, 49]
and suggest that geographic distance may be less important in e-marketplaces [53], this study shows
that geographic distance still affects the decision of sellers in e-marketplaces in terms of whether they
should exchange hyperlinks with prospective partners. Past studies on strategy argue that geographic
distance undermines collaboration [40, 43] but this study demonstrates that geographic distance can
facilitate collaboration in the context of e-marketplaces.20
The moderating effects uncovered in this study enrich our theoretical understanding about how
sellers make decisions on the exchange of hyperlinks in the e-marketplace context. The negative
effects of market commonality can become positive effects when partners are located far away from
each other or when the seller initiating the hyperlink has poor reputation. In these conditions, the
drawbacks of collaboration (i.e., mutual competition) are lessened while the benefits of collaboration
(i.e., increased market) are heightened. Similarly, the negative effects of geographical distance can
become positive effects when partners have high market overlap. In this situation, collaboration
barriers arising from coordination and monitoring costs are reduced.
6.3 Managerial implications
The results of this study have managerial implications for sellers and platform operators in e-
marketplaces. In spite of the ubiquity of hyperlinks in e-marketplaces, sellers have little guidance as to
how they may benefit from hyperlink exchanges in the past. This study guides sellers in their
decisions about when to exchange hyperlinks. Specifically, when sellers are considering prospective
partners with high market overlap, they are likely to be better off exchanging hyperlinks with those
that are located far away or those with poor reputation. But when sellers are considering prospective
partners located far away, they are likely to be better off exchanging hyperlinks with those that they
have high market overlap. For sellers with weak reputation (e.g., new sellers) that have a greater need
for customer resources [18], they are more likely to succeed in exchanging hyperlinks with partners
with high market overlap.
Sellers in e-marketplaces have more difficulty differentiating their products [75] but they also
have more opportunities to cooperate in the dynamic environment [45]. Because these sellers must
often collaborate for mutual survival, this study offers guidance to sellers for their decisions on when
to collaborate. Specifically, market conditions (e.g., market commonality and geographical distance)
as well as partner characteristics (e.g., reputation) should factor into such decisions. The results of this
study offer insights into the trade-off that occurs when different combinations of market conditions
and partner characteristics come into the decision process. The payoff in making the right trade-off
can be significant for the numerous sellers in e-marketplaces.
21
Platform operators can also leverage on the results of this study to improve collaboration among
numerous sellers. For example, there are many seller associations on Taobao and some are organized
by geography (e.g., Association of Shanghai Sellers). At present, the key activities of such seller
associations are limited to sharing experience among members. Given that it can be beneficial for
sellers located near each other to collaborate under some conditions, the platform operator can
encourage collaboration among relevant members of such seller associations based on the results of
this study. Other seller associations on Taobao are organized by industry (e.g., Association of Apparel
Sellers). Again, the key activities of such seller associations are limited to sharing experience among
members. Given that it can be beneficial for sellers with high market overlap to collaborate under
some conditions, the platform operator can encourage collaboration among relevant members of such
seller associations based on the results of this study. Mutually productive collaboration is instrumental
for the survival of sellers in e-marketplaces.
6.4 Limitations and future research
This study has several limitations. First, the measurement of market commonality is limited in
accuracy. Market commonality is based on product type as classified by Taobao. The dataset only has
the first two major product categories of each seller. In practice, sellers may be involved in more than
two major product categories. Also, information on the percentage of sales for each seller in each
product category is not available. Future research leveraging on a richer dataset can shed light on
additional factors that may affect the decisions of sellers in exchanging hyperlinks.
Second, future studies can investigate other forms of alliance which incur higher switching costs
than exchanging hyperlinks. Such forms of alliance include sellers involved in jointly sourcing and
sellers sharing inventory. Also, sellers in e-marketplaces can customize and personalize their product
and service offerings, thereby increasing switching costs for customers [6]. Higher switching costs
may then affect the decisions of sellers on hyperlink exchange and alliance formation [76]. More
research is needed on these topics.
Third, e-marketplace alliances are loose and easy to break if all partners do not benefit equally
[28]. This study is confined to alliances which offer positive reciprocity (i.e., how sellers enter into 22
collaboration). Future research can examine when and how sellers may end their collaboration (e.g.,
removing existing hyperlinks with partners).
7 CONCLUSION
Many sellers in e-marketplaces are small businesses [1-3]. The selection of alliance partners is a
strategic decision for these sellers because they often lack resources and struggle to survive in a
competitive environment [4]. Hyperlink exchange is an IT-enabled means of alliance formation.
Although hyperlinks are ubiquitous on e-marketplaces, past studies have rarely offered guidance to
sellers about when they should exchange hyperlinks so as to raise their performance in e-marketplaces
[10, 14, 17]. Going beyond past studies, this study extends our theoretical understanding in this area
and offer guidance to sellers in terms of when they should exchange hyperlinks so as to increase their
performance in e-marketplaces. As e-marketplaces continue to offer more and better IT capabilities,
sellers would need guidance about how to leverage these IT capabilities to improve their performance.
Research in the direction of this study can help to address such issues and benefit the numerous sellers
in e-marketplaces.
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