Understanding Sharing Relationships in
Online Barter Markets
Shun Ye
Il-Horn Hann Siva Viswanathan
Decision, Operations and Information Technologies
Robert H. Smith School of Business
University of Maryland, College Park
College Park, MD 20742
e-mail: {sye, ihann, sviswana}@rhsmith.umd.edu
January 2014
Introduction
The rapid development of Internet and e-commerce technologies has led to the exponential growth of
online markets in the past decade. Given that these markets are sustained by a community of buyers and
sellers, an increasing number of studies have examined online buyer-seller exchange relationships.
Whereas most of the existing research is focused on mechanisms to facilitate buyer-seller interactions
(e.g., Ba and Pavlou 2002; Ba et al. 2003; Dellarocas 2003; Maslet and Penard 2012), very little is known
about how market participants actually view transactions and establish the buyer-seller relationships.
Our study represents an effort to fill this gap in the literature. In particular, we seek to understand the
differences in exchange relationships between transacting partners and the drivers and consequences of
such differences in exchange relationships. In particular, drawing upon theories of exchange relationship
in the marketing and supply chain literature, we identify two different types of exchanges between
participants: one-time transactional and long-term relational. Empirically using a unique dataset of
detailed transaction and weblog data from a leading online barter market for book exchanges, we show
that “non-avid users” have a transactional-orientation towards exchange relationship and they only focus
on the current transaction. In contrast, “avid users” tend to have a relational view of market exchange as
they also care about future repeated transactions. We further show that the magnitude of this relationalorientation of avid users depends on the breadth of their interests. Whereas several studies have
examined buyer-seller exchange orientation in the offlineB2B or B2C contexts solely from the perspective
of the buyer or the seller (e.g., Kalwani and Narayandas 1995; Coviello et al. 2002; Cannon et al. 2010),
our study is among the first to empirically examine the existence of different exchange orientations in
online C2C markets in which each individual is typically both a buyer and a seller. Our study is also
among the first to study a growing phenomenon, online barter markets as well as the first to examine the
factors that drive the choice of exchange relationships among individuals in an atomistic market.
The rest of the paper is structured as follows. Section 2 reviews relevant literature. Section 3 describes the
research context of the study. Section 4 presents the preliminary data analyses and results. Section 5
describes the ongoing research and concludes.
Related Research
One major barrier to developing buyer-seller exchange relationships in online markets is information
asymmetry between the buyer and the seller (Dellarocas 2005). Prior studies have examined various
1
mechanisms that help reduce the information asymmetry problem and promote transactions in online
markets. For example, Pavlou and Gefen (2004) find that the availability of institutional mechanisms
such as escrow services and credit card guarantees increases buyers’ intentions to engage in online
exchanges. Ba and Pavlou (2002) show that electronic reputation systems benefit buyer-seller
relationships by increasing calculus-based credibility trust between buyers and sellers without repeated
transactions. The findings are echoed by many other studies that are focused on reputation system design
(e.g., Bolton et al. 2004; Fan et al. 2005; Maslet and Penard 2012; Melnik and Alm 2002).
Despite the increasing understanding of trust building mechanism that facilitates buyer-seller
relationships, there is a lack of knowledge about the nature of online exchange relationships. Research in
marketing views an exchange between the buyer and the seller as discrete (or transactional) or relational
(Macneil 1980; Ganesan 1994; Wilson 1995). The discrete transaction view treats exchanges as
characterized by very little communication between the buyer and the seller, one-time interaction and a
sharp ending of the buyer-seller relationship. In this view, the exchange between the buyer and the seller
is pure transactional and it excludes relational elements. The relational view treats exchange between the
buyer and the seller as ongoing relationships that transpire over time. In this view, the buyer and the
seller may develop obligations and norms such as reciprocity and trust to facilitate future collaboration.
Exchanges built from the relational view are often repeated as the buyer and the seller engage in social
exchange. Researchers like Dwyer et al. (1987) argue that some elements of a “relationship” underlie all
transactions and the exchange between the buyer and the seller should be treated as a continuum, ranging
from transactional to relational.
The transactional-relational continuum review of buyer-seller exchange has been applied in many
different contexts. For example, Coviello et al. (2002) find that different firms view transactions with
customers in different ways: some firms are predominantly transactional-oriented, some firms are
predominantly relational-oriented, and some firms are transactional/relational hybrid. Pels (1999) argue
that the transactional- or relational-oriented view does not only apply to firms, but apply to its customers.
In the supply chain context, Cannon et al. (2010) examine the exchange relationship between a buyer firm
and its suppliers and find that firms in different cultural regions have different exchange orientations. In
the IT outsourcing context, researchers have suggested that an outsourcing contract should be governed
by transactional mechanisms or relational mechanisms depending on the nature of the client-vendor
relationship (Cannon et al. 2000; Zaheer and Venkatraman 1995).
Whereas the transactional-relational continuum view of buyer-seller relationship is examined in
traditional offline contexts where formal contracts are often used, we believe it is also applicable to online
market wherein no formal contracts are available. On one hand, the lack of face-to-face communication
and the availability of large alternative choices might induce individuals to be more transactional-oriented
without commitment to long-term relationships. On the other, the ease of keeping track of transaction
partners and the interactive features such as messaging and social networking might encourage market
participants to be relational-oriented. Therefore, we use the transactional-relational continuum view of
buyer-seller relationship as a lens to examine how different individuals establish buyer-seller
relationships in online markets.
Research Context
We collected data from one of the leading P2P online barter marketplaces for books. Even though the site
operates internationally, most of the participants are from the United States. Regardless of the monetary
sales value, each book is valued as 1 virtual point in the market. In general for any book transaction, 1
point will be transferred from the book seeker to the book owner. However in contrast to traditional
online markets such as eBay in which the buyer pays for the shipping, the book owner actually pays for
the postage to ship the book to the book requester. Therefore to encourage overseas transactions, the book
requester has to pay 2 extra points to the book owner for international requests. Each market participant
can create an inventory list of books they want to get rid of and a wishlist of books they want. For any
individual, the inventory list and the wishlist reflect her book taste and are publicly available to any other
participant in the market.
To reduce information asymmetry among participants, the market tracks each transaction and displays an
individual’s past performance in her public profile through a variety of measures, including number of
2
Exchange Orientation in Online Markets
books requested from others, number of books given to others, number of rejections to others’ requests,
number of books reported as lost in the mail by the transaction partner, etc. In addition, the market
provides a feedback mechanism which allows transaction partners to rate each other’s performance. Each
individual’s feedback score is also publicly available in her profile page.
We collected six months’ detailed transaction data from November 1st 2010 to April 30th 2011 in the
marketplace. In addition, we collected three months’ detailed weblog data from November 1st 2010 to
January 31st 2011 regarding how individuals searched for books in the market.
Preliminary Analyses
Transactional vs. Relational Orientation among Individuals
As highlighted earlier, individuals may have different views of exchange relationships. We identify two
broad categories of users: “avid users” who request at least 2 books per month in the market, and “nonavid users” who request fewer than 2 books per month in the market 1. Our sample comprises of 6155 avid
users and 8118 non-avid users. In addition, we also define the “breadth of interest” for an individual as the
number of book genres the individual is interested in as indicated by her “inventory list” and/or her “wish
list”. For simplicity, we only report the results of measuring breadth of interest only based on individuals’
inventory lists whereas consistent results are yielded using only the wish list or both the inventory list and
the wishlist.
Individuals’ exchange orientations are reflected in how they choose their transaction partners. Individuals
with a transactional view are more likely to care about the smoothness of the current transaction rather
than the possibility of future repeated interactions. However, individuals with a relational view are likely
to be forward-looking and more likely to choose to transact with partners with whom they can engage in
future transactions in addition to the current encounter. Therefore, we expect that whereas the reputation
of the transaction partner matters for all individuals, the possibility of future transactions between the
book requester and book owner matters more for avid users and more so for those avid users with
narrower interests.
An individual’s reputation was measured by three major variables: the feedback score (feedback_score),
number of rejections to others’ requests (rejected), and number of books that were reported by others as
lost in the mail (sent_lost). The more similar two individuals are in terms of book tastes, the more likely
they have the books each other wants, and thereby the higher possibility of repeated transactions between
them. Therefore from individual A’s perspective, the possibility of having repeated transactions with the
partner B is indicated by the extent they share similar book tastes and the depth of B’s inventory and we
used four proxies to measure it: the cosine similarity index based on book genres (taste_similarity) (Tan
et al. 2005)2, the number of shared book genres (shared_genre), the total number of books the partner B
has in all the shared genres (shared_genre_depth), and the total number of books the partner B has in
the focal genre that the transacted book belongs to (focal_genre_depth).
Any successful transaction in the market happens through three steps: (1) the book seeker finds the book
her wants and ask the book owner for the book; (2) the book owner receives the request and decides
whether to accept or reject the request; (3) if the request is approved, the book owner pays for the postage
As a robustness check, we narrowed or broadened the definition for avid users and still got consistent
results.
1
For individuals A and B, suppose Ai and Bi denotes their percentages of books in book genre i
respectively. Then, the cosine similarity is calculated using the following formula:
2
n
similarity( A, B)  cos( ) 
A B

A B
 Ai * Bi
i 1
n
n
2
2
 Ai *  Bi
i 1
i 1
The resulting similarity ranges from 0 to 1, with 0 indicating totally different book tastes, 1 indicating
exactly the same book taste, and in-between values indicating some level of similarity.
3
and ships the book to the book seeker. The first step decision is made by the book requester and the
decisions in the next two steps are made by the book owner or book giver. The following sections examine
how different individuals make decisions in each step. In all the regression analyses of an individual A’s
decision making, in addition to controlling for the partner B’s reputation and the possibility of future
interactions with the partner B from A’s perspective, we also controlled for the partner B’s other
characteristics such as whether she has a bio (if_bio), whether she shares a photo of herself (if_photo),
how many months she has joined the market (tenure_month), the last time she logged into the market
(last_login_month), whether A is a friend of B (if_friend), whether A and B are in the same country
(if_same_country), and several other variables related to past and concurrent transactions between A
and B. Variables on the book requester side are marked with prefix “r_” and variables on the book giver
side are marked with prefix “g_”.
How Book Requesters Make Decisions
We first examined how individuals choose from whom to request a book when facing multiple potential
books givers. Given that the choice set is different for each specific book request, we used the conditional
logit model to estimate the marginal effect of each explanatory variable given an individual's specific
choice sets. The dependent variable is a dummy variable if_chosen indicating whether a book owner in
the choice set is chosen by the book requester or not. The results are shown in Table 1.
Table 1. The Book Requester's Choice of Book Givers
Dependent Variable: if_chosen
Variables
Giver Characteristics
g_if_bio
g_if_photo
log(g_feedback_score)
g_rejected
g_sent_lost
g_tenure_month
g_to_send
g_last_login_month
Possibility of Future Transactions
taste_similarity
shared_genre
log(g_focalgenre_depth)
log(g_sharedgenre_depth)
Past and Concurrent Transactions
g_r_past
r_g_past
g_r_pending
r_g_pending
Other Variables:
if_same_country
if_friend
Interaction Effects:
r_interest_breadth * taste_similarity
r_interest_breadth * shared_genre
r_interest_breadth * log(g_focalgenre_depth)
r_interest_breadth * log(g_sharedgenre_depth)
4
Non-Avid User Sample
Avid User Sample
0.119*** (0.016)
0.083*** (0.018)
0.355*** (0.016)
-0.003*** (0.001)
-0.027*** (0.002)
-0.062*** (0.001)
0.028*** (0.001)
-0.002*** (0.000)
0.105*** (0.009)
0.103*** (0.010)
0.353***(0.009)
-0.003*** (0.000)
-0.022*** (0.001)
-0.060*** (0.000)
0.026*** (0.000)
-0.002***(0.000)
-0.052 (0.055)
0.012 (0.019)
-0.185 (0.127)
0.038(0.143)
0.264* (0.130)
0.023*** (0.005)
-0.188 (0.286)
0.046* (0.020)
0.296*** (0.032)
0.067(0.038)
3.760*** (0.070)
0.838*** (0.202)
0.022***(0.001)
0.006***(0.001)
2.586***(0.020)
0.238***(0.027)
1.827*** (0.034)
1.002* (0.52)
1.706***(0.018)
1.050**(0.402)
-0.011*(0.005)
-2.98e-04*(1.67e-04)
0.001(0.001)
-0.002*(0.001)
Exchange Orientation in Online Markets
r_interest_breadth * if_friend
# of Observations
Pseudo R2
1009925
0.210
0.012(0.018)
2518374
0.264
In both the non-avid user sample and the avid user sample, the coefficients of g_if_bio and g_if_photo
are significantly positive. This suggests that book owners who provide more information about themselves
are more likely to be chosen. The coefficients of g_sent_lost and g_rejected are significantly negative in
both samples, suggesting that book owners with worse reputations are less likely to be chosen. These
results, together with the significantly positive coefficient of log(g_feedback_score), provide support that
all individuals take into consideration the partner’s reputation when requesting a book.
The coefficients of taste_similarity and shared_genre are significantly positive only in the avid user
sample. This result is consistent with our prediction that an avid user is more likely to have a relational
view of the exchange and chooses transaction partners with whom future interactions are more likely.
More interestingly, the significant and negative coefficients of the interactions effects r_interest_breadth
* taste_similarity and r_interest_breadth * shared_genre suggest that the broader the breadth of
interest of the book requester, the less she cares about (i.e., less likely to choose) a partner who is similar
to her in interests. In other words, the relational view of the exchange is weaker for individuals with
broader interests.
How Book Givers Make Decisions
We then examined how individuals make decisions about book requests from others. The individual who
is eager to get a book might send a private message to the book owner, resulting in the book owner
accepting the request and meanwhile sending out the book quickly. This suggests that there might be a
sample selection bias if we analyze the book owner’s two decisions separately. To overcome this problem,
we followed the Heckman two-stage model. In the first stage, we used if_same_country as an instrument
variable to predict how likely a request is to be rejected by the book owner. Whereas if_same_country
might have an influence on rejection decision as a domestic request is less likely to be rejected due to
lower shipping cost compared to an overseas request, it should not affect how sooner the book giver mails
the book once the request is accepted. Therefore, if_same_country serves as a good instrument variable
in the first stage. In the second stage of estimating delivery speed, we included the inverse mill’s ratio
calculated from the first stage to control for sample selection bias. Delivery speed was reversely measured
by the interval in days between the request date and the shipping date. The smaller the interval, the faster
the delivery speed. In order to save transportation costs, individuals might visit the post office to mail
books until they have sufficient books to send out. To account for this possible batch-processing tendency
of individuals, we controlled for the number of other books to be sent by the book owner before sending
the focal book (g_to_send) in the second stage of predicting delivery speed. The results of the Heckman
two-stage model are shown in Table 2.
For both non-avid users and avid users, the coefficient of if_same_country is significantly negative,
suggesting that overseas requests are more likely to be rejected. In addition, the coefficient of the inverse
mill’s ratio is significant in the second stage for both samples, confirming the existence of potential
sample selection bias and the valid use of the Heckman two-stage model. In addition, the coefficient of
log(price) is significantly positive in predicting request rejection. This indicates that more expensive
books are more likely to be rejected. Interestingly, whereas taste_similarity and shared_genre have no
impact on request rejection decision, they have an influence on avid users’ delivery speed decision. The
significantly negative coefficients in the avid user sample suggest that avid users are more likely to send
out books faster to book requesters with whom future transactions are more likely to happen. This
preference is stronger for users with narrower interests, as indicated by the significantly positive
interaction terms. The possible batch-sending habit of individuals is supported by the significant and
negative coefficient of g_to_send.
Overall, our analyses of how individuals make transactional decisions provide evidence that: (1) non-avid
users are more transaction-oriented and avid users and more relation-oriented; and (2) among avid users,
users with narrower interests are more relation-oriented than users with broader interests.
5
Additional Evidence
Individuals’ exchange orientation should also be reflected in how they search for books in the market.
There are two major ways to find a book in the marketplace: (1) type in a keyword and find the book in the
search results, which we call keyword search; and (2) browse through an individual’s inventory and find
the book, which we call browse. Since relational oriented individuals are inclined to develop long-term
transactional relationships with others, they should do more browses of others’ inventory lists and fewer
keyword searches to find a book than individuals with a transactional view. Indeed, we found that the
average percentage of keyword search of transactional-oriented non-avid users is 90.50%, much higher
than the 76.50% of relational-oriented avid users. Furthermore, as shown in Figure 2, the narrower the
avid user’s interest is (i.e., the lower the quantile in interest breadth is), the lower the percentage of
keyword search of the user is. This provides additional evidence that the relational orientation is stronger
for users with narrower interests.
Table 2. Non-Avid Users’ Decisions on Requests from Others
Dependent Variables
Variables
Non-Avid User Sample
Avid User Sample
if_reject
if_reject
Intercept
Receiver Characteristics
r_if_bio
0.000(0.000)
r_if_photo
-0.074(0.076)
log(r_feedback_score)
0.034(0.035)
r_rejected
0.149*(0.037)
r_sent_lost
0.009(0.005)
r_receive_give_ratio
0.001***(0.000)
r_tenure_month
-0.001(0.001)
Past and Concurrent Transactions
g_r_past
-0.062(0.060)
r_g_past
0.075(0.246)
g_r_pending
0.580(0.444)
r_g_pending
-0.184*** (0.047)
Book Characteristics
log(price)
0.042**(0.014)
num_choices
0.001(0.000)
Possibility of Future Transactions
taste_similarity
-0.408(0.225)
shared_genre
-0.008(0.007)
log(r_focalgenre_depth)
-0.034(0.073)
log(r_sharedgenre_depth)
0.022(0.069)
Interaction Effects:
g_interest_breadth *
-0.408(0.225)
taste_similarity
g_interest_breadth *
-0.008(0.007)
shared_genre
g_interest_breadth *
-0.034(0.073)
log(g_focalgenre_depth)
g_interest_breadth *
0.022(0.069)
log(g_sharedgenre_depth)
6
Delivery_speed
9.754***(0.427)
Delivery_speed
8.778***(0.232)
0.109(0.168)
0.020(0.178)
-0.198(0.184)
-0.001(0.005)
-0.007(0.013)
0.000(0.000)
-0.002(0.002)
0.032(0.044)
0.000(0.046)
-0.053*(0.022)
0.001(0.001)
-0.002(0.003)
-0.000(0.000)
-0.000(0.000)
-0.014(0.082)
0.098(0.086)
0.013(0.044)
-0.007(0.003)
0.009(0.007)
-0.000(0.000)
-0.004(0.001)
0.076(0.070)
0.900(0.407)
-0.108(0.072)
-2.187*(0.805)
-0.005(0.005)
-0.004(0.009)
-0.009(0.018)
-0.396*(0.146)
0.027(0.024)
0.021(0.046)
-0.035(0.070)
-1.231***(0.052)
-0.024(0.033)
0.044***(0.009)
-0.000(0.000)
-0.027(0.018)
-0.453(0.542)
0.013(0.017)
-0.301(0.179)
0.322(0.170)
0.297(0.235)
0.040(0.023)
0.167(0.312)
-0.353(0.303)
-0.705*(0.350)
-0.082*(0.040)
-0.421(0.518)
-0.227(0.503)
-0.453(0.542)
-0.027(0.025)
0.095*(0.045)
0.013(0.017)
-0.001(0.001)
0.002*(0.001)
-0.301(0.179)
-0.010(0.014)
0.033(0.024)
0.322(0.170)
0.016(0.015)
-0.028(0.024)
Exchange Orientation in Online Markets
g_interest_breadth *
-0.408(0.225)
if_friend
Instrument Variables & Other Variables:
if_same_country
-0.703***(0.113)
g_to_send
0.021(0.025)
if_friend
-1.762***(0.058)
Inverse Mill’s Ratio
# of Observations
35158
-0.453(0.542)
-0.506***(0.120)
-3.014***(0.526)
3.268*(1.502)
32095
Figure 2. Percentage of Keyword Search for
Avid Users
0.002(0.027)
-0.416***(0.068)
0.015(0.040)
-1.209***(0.064)
105635
0.012(0.035)
-0.441***(0.082)
-2.550***(0.703)
4.020*(1.626)
101403
Figure 3. Change in Percentage of Keyword
Search for Avid Users
Given the above result, we should also expect avid users to reduce keyword search and rely more on
reciprocal and repeated partners’ inventories after they gain more reciprocal partners. To investigate if
this is true, we focused on 758 individuals who gained one additional reciprocal partner in December
2010 and further examined how they changed their search patterns from November 2010 before the gain
to January 2011 after the gain. As shown in Figure 3, the narrower the avid user’s interest is, the bigger
the drop in the percentage of keyword search is. This result corroborates the previous finding that avid
users with narrower interest are more relational-oriented.
Conclusion and Ongoing Research
As one of the very first attempts to empirically examine how different individuals use different exchange
orientations to develop buyer-seller relationships in online markets, this study provides support for the
theory of transactional-relational continuum. In particular, we find that whereas non-avid users are more
transactional-oriented, avid users are more relational-oriented. More interestingly, we find that the extent
of relational-orientation differs for users with different breadths of interest. The differences in exchange
orientations for different market participants are not only salient in their choices of transactional
partners, but also significant in their decisions on transaction fulfillment.
Our study has important implications for online market design. First, given that different individuals
choose transaction partners in different ways, different algorithms should be designed in
recommendation systems that intelligently recommend potential partners to reduce market participants’
search costs. Second, our finding suggests that features facilitating relations such as social networking
7
might be only useful for certain users in the market. The market designer needs to carefully evaluate
whether the partial benefit outweigh the implementation cost.
Currently we are extending the study in two major ways. First, we are conducting social network analysis
to examine if different exchange orientations of individuals lead to different transaction network
structures and friendship network structures. Second, based on the demand and supply of books in the
market, we are analyzing the implications of individuals’ exchange orientation on market efficiency. These
findings will further deepen our understanding of exchange relationship and online market design.
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