To Sell or Not to Sell: Exploring Seller’s Trust and Risk of Chargeback Fraud in Cross-Border Electronic Commerce

Abstract

Over the past few decades, chargeback fraud from buyers has been identified as a major risk faced by online sellers, particularly small- and medium-sized enterprises, in cross-border electronic commerce. However, most previous studies have focused on trust and perceived risk from the buyers' perspective and in domestic online marketplaces, while neglecting the importance of sellers' trust and perceived risk in the success of online transactions and the significance of cross-border transactions. To fill this gap in the literature, this study examines both the antecedents and the impacts of sellers' trust in buyers and their perceived risk of chargeback fraud on sellers' intention to trade with buyers in the context of cross-border e-commerce. To this end, we develop a conceptual model that identifies a set of institutional mechanisms to enhance sellers' trust and reduce their perceived risk. Hypotheses are tested via a survey of 443 sellers on DHgate.com, one of the major cross-border e-commerce websites connecting the small- and medium-sized enterprises of mainland China with overseas buyers. Our research makes concrete contributions to e-commerce research and generates useful insights for third-party online transaction platforms and online trade policy makers.

Full text

Electronic copy available at: https://ssrn.com/abstract=2964381
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To Sell or Not to Sell: Exploring Sellers’ Trust and Risk of Chargeback Fraud in Cross-
Border Electronic Commerce
Yue Guo
Hohai University
Yongchuan Bao
University of Alabama in Huntsville
Stuart Barnes
King’s College London
Khuong Le‐Nguyen
Kent State University
March 2017
Accepted by Information Systems Journal

Electronic copy available at: https://ssrn.com/abstract=2964381
2
Abstract
Over the past few decades, chargeback fraud from buyers has been identified as a major risk faced by
online sellers, particularly small- and- medium-sized enterprises (SMEs), in cross-border electronic
commerce. However, most previous studies have focused upon trust and perceived risk from the buyers’
perspective and in domestic online marketplaces, while neglecting the importance of sellers’ trust and
perceived risk in the success of online transactions and the significance of cross-border transactions. In
order to fill this gap in the literature, this study examines both the antecedents and impacts of sellers’ trust
in buyers and their perceived risk of chargeback fraud on sellers’ intention to trade with buyers in the
context of cross-border e-commerce. To this end, we develop a conceptual model that identifies a set of
institutional mechanisms to enhance sellers’ trust and reduce their perceived risk. Hypotheses are tested
via a survey of 443 sellers on DHgate.com, one of the major cross-border e-commerce websites
connecting the SMEs of mainland China with overseas buyers. Our research makes concrete contributions
to e-commerce research and generates useful insights for third-party online transaction platforms and
online trade policy makers.
Keywords: e-commerce; sellers; trust; cross-border; chargeback fraud; PLS-PM.
To Sell or Not to Sell: Exploring Seller’s Trust and Risk of Chargeback Fraud in Cross-
Border Electronic Commerce

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1. Introduction
The rise of third-party trading platforms, such as eBay.com and DHgate.com, has enabled small-
and- medium-sized enterprises (SMEs) to sell products and services online to a large number of potential
buyers from all over the world. These SMEs seek to avoid intense competition in their home markets and
to seek actively new business opportunities in cross-border online commerce. As emphasized in the extant
literature on e-commerce, the success and continuance of online transactions hinge on the trust between
transacting parties, particularly as a result of the social complexity, uncertainty, and risks involved in
online trading (Gefen et al. 2003; McKnight and Chervany 2002; Pavlou and Dimoka 2006; Yoon and
Occeña 2015). Indeed, “price does not rule the web; trust does” (Reichheld and Schefter 2000, p. 107). It
is notable that nearly all previous studies examine the determinants of trust and transaction risks from the
buyers’ perspective, assuming that buyers were placed in a disadvantaged position relative to sellers,
which subjects the former to the opportunistic behavior of the latter (e.g. Fang et al. 2014; Gefen et al.
2003; Koh, Fichman, and Kraut 2012; Pavlou 2003; Pavlou and Gefen 2004; Pennington, Wilcox, and
Grover 2003).
This assumption may not always hold, considering the common problem of chargeback fraud
faced by SMEs and other merchants in e-commerce transactions. Chargeback fraud, also known as
friendly fraud, occurs when buyers claim a refund for purchased items without returning the items to
sellers, typically based on some unfounded excuses such as “items are not delivered” or “transactions are
unauthorized” (Khan 2015). The fraudulent incentive of buyers to get “free” items online is accentuated
by the credit card protection policy commonly adopted by third-party online platforms, which often
allows buyers to reverse charges for up to 180 days if they are not satisfied with the ordered items
(Clemons 2007). Unlike face-to-face transactions with buyers in stores where the credit card institutions
take full responsibility in disputes of chargebacks, an online merchant is held accountable for the loss of

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delivered items despite all the measures she has taken to verify the transactions (Riley 2008). In addition
to the refund, the seller is further required to pay chargeback fees to credit card companies and bears the
risk of account termination on an online trading platform if the chargeback claims are excessive or the
unsatisfied buyers leave negative comments on the platform. According to a LexisNexis report (2013),
merchants incur a $279 loss for every $100 of fraud loss
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. Some online marketplaces do not even allow
sellers to leave negative feedback for buyers (Sun 2010), thus encouraging buyers to engage in
opportunistic behavior. As a result, sellers experience a high risk of chargeback fraud associated with the
rapid growth of credit card use in online transactions.
On the other hand, cross-border e-commerce represents an emerging online market for SMEs. A
PayPal-commissioned report indicates that more than 130 million cross-border shoppers world-wide will
spend over US$300 billion by 2018 (PayPal Media 2013). Compared to domestic e-commerce, cross-
border e-commerce brings more business opportunities, especially from emerging markets, such as China
and Brazil (PayPal Media 2013). However, cross-border trading and delivery are far more complicated
and risky than either the traditional offline market or the domestic electronic market, due to the high
information asymmetry between international buyers and sellers, poor legal enforcement across countries,
language and culture barriers, and high shipping costs in international trading (Gomez-Herrera et al. 2014;
Savrula et al. 2014; Gessner 2015). Given the high complexity and uncertainty in cross-border
transactions, the risk of chargeback fraud looms larger for online merchants, especially SMEs who are
endowed with considerably less financial resources than large enterprises.
Even though chargeback fraud imposes transaction risk for sellers, which further increases in
cross-border transactions, relatively little research effort has been devoted to the determinants and
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A co-author of this study set up a small enterprise to conduct international business online in the capacity of a
seller. His company also experienced tremendous financial losses stemming from chargeback fraud.

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consequences of trust and perceived risk from the sellers’ perspective. The existing literature is
overwhelmingly concerned with the protection of buyers’ interests and with this standpoint pays
exclusive attentions to antecedents of buyers’ trust and perceived risks in online transactions. Researchers
have proposed a variety of institutional mechanisms (such as structural assurances, escrow services, and
credit card guarantees) that place an emphasis on enhancing buyers’ trust and mitigating their transaction
risks (Fang et al. 2014; Koh, Fichman, and Kraut 2012; Pavlou and Gefen 2004; Gefen and Pavlou 2012).
Although previous studies have enriched our knowledge of the buyers’ view of e-commerce and
developed effective solutions for counteracting buyers’ risks in e-commerce, the success of online
transactions requires not only buyers’ trust but also sellers’ trust and continued use of online marketplaces
(Sun 2010). In the absence of transaction mechanisms that can protect sellers from buyers’ opportunistic
behavior, sellers can choose to walk away from transactions that have suspicious incentives. Thus,
understanding the sellers’ perspective on trust and perceived risk is as equally important to the
continuance of e-commerce as the buyers’ perspective.
This study seeks to bridge the above research gaps by shifting the focus to sellers’ trust and
perceived risk of chargeback fraud in the context of cross-border e-commerce. Drawing on information
signaling theory (Spence 1974) and the sociological perspective (Shapiro 1987; Zucker 1986), we develop
a conceptual model that identifies a comprehensive set of determinants of sellers’ trust and perceived risk
of chargeback fraud. To demonstrate the necessity to consider the sellers’ view, we also aim to examine
the consequences of sellers’ trust and perceived risk on their intention to sell online. We select a leading
cross-border e-commerce platform, DHgate.com, one of the largest e-commerce marketplaces in China,
as the empirical setting for a hypothesis test of the conceptual model. China has surpassed the U.S. as the
world’s biggest trading nation and is a growing influence in global commerce (Bloomberg News, 2013),
suggesting that it is an ideal setting for our research.

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This paper makes contributions to e-commerce research in the following ways. First, we extend
existing e-commerce literature by examining the determinants of the critical foundations of e-commerce –
trust and perceived risk of transacting parties – from the sellers’ perspective. Specifically, we identify
institutional mechanisms that can enhance sellers’ trust and mitigate their perceived risk of chargeback
fraud. Second, we expand previous models of institution-based trust and perceived risk by
conceptualizing and testing the effects of institutional mechanisms that accommodate the more
complicated and risky cross-border e-commerce context. Third, we provide strong evidence about the
critical role of sellers in the continuance of e-commerce transactions. The findings about the effects of
sellers’ trust and perceived risk on sellers’ intention to trade challenge the dominant assumption that the
success of online transactions primarily depends on buyers’ trust and perceived risk because buyers are
subject to sellers’ opportunistic behavior. As an alternative, our research indicates that sellers are also
susceptible to buyers’ fraudulent behavior and both their trust and perceived risk determine the intention
to trade with buyers.
2. Literature Review
Previous studies in e-commerce indicate that trust and perceived risks are the principle
determinants of online transaction behavior (e.g., Pavlou 2003; Gefen et al. 2003). Trust refers to both a
belief that the trusted party will behave in accordance with the trusting party’s confident expectations of
the former party’s benevolence, integrity, and ability, and the willingness of the trusting party to accept
vulnerability based on these expectations (Mayer et al. 1995; Gefen et al. 2003; Fang et al. 2014). Trust
acts as a cornerstone for successful online transactions and the formation of buyer-seller relationships in
e-commerce because online transactions feature high uncertainty and risks that arise from the information
asymmetry between buyers and sellers (Chiu et al. 2012; Pavlou and Gefen 2004). Most studies have
exclusively examined trust of buyers in sellers based on the assumption that the sellers are in a more
advantageous position to behave opportunistically in online transactions (e.g. Fang et al. 2014; Gefen et al.

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2003; Gefen and Straub 2004; Hong and Cho 2011; Jarvenpaa et al. 2000; Pavlou and Gefen 2004).
Buyers’ trust in sellers is portrayed in these studies as a functional mechanism to sustain online
transactions and purchase intention. Recent literature demonstrates that the importance of trust in
stimulating online purchases of buyers depends on the effectiveness of institutional mechanisms (Fang et
al. 2014; Gefen and Pavlou 2012).
Perceived risk refers to the belief of a transaction party that a loss could possibly occur as a result
of the opportunistic behavior of another party (Jarvenpaa et al. 2000; Pavlou and Gefen 2004). The extant
literature in e-commerce is principally concerned with the online transaction risk to buyers, arguing that
the incomplete information possessed by buyers of sellers engenders uncertainty of transaction and
subjects buyers to the opportunistic behavior of sellers (Bélanger and Carter 2008; Gefen and Pavlou
2012; Pavlou and Gefen 2004). The perceived risks of online transactions, such as low product quality,
poor after-sales service, theft of credit card information, and breach of privacy, inhibit buyers’ purchase
intention (Corbitt et al. 2003; Jarvenpaa and Tractinsky 1999; Kim and Benbasat 2006). Recent studies
also indicate that the effects of buyers’ perceived risk on their purchase decision are contingent on the
effectiveness of institutional structures (Gefen and Pavlou 2012).
Consistently, existing studies have examined the institution-based antecedents of trust and the
perceived risk of online transactions from the buyer’s perspective (e.g. Gefen and Straub 2004; Gefen et
al. 2003; Koh et al. 2012; Pavlou and Gefen 2004). For example, Gefen et al. (2003) find that a buyer’s
trust in an e-vendor is heavily influenced by the buyer’s perception of institution-based antecedents, such
as situational normality (buyer’s assessment of transaction success based on how normal the situation
appears to be), structural assurance (buyer’s assessment of transaction success based on the security
mechanisms of online transaction), as well as the buyer’s perception of the ease of use of a web site.
Pavlou and Gefen (2004) also indicate that a buyer’s perceived effectiveness of institutional mechanisms
(PEIM) designed by third parties or online transaction intermediaries exerts strong impacts on a buyer’s

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trust and perceived risk in sellers. Focusing on returning customers, Fang et al. (2014) corroborate the
functional role of PEIM in shaping buyers’ trust, finding that PEIM positively moderates the relationship
between buyers’ satisfaction and their trust in vendors. Koh et al. (2012) instead emphasize the role of
information indices and signals of sellers in determining a buyer’s trust, specifically demonstrating the
positive effects of both information indices about sellers’ attributes (such as country of origin) and
information signals acquired by sellers on buyers’ trust in sellers.
In sum, while the existing literature has long established relationships between trust, perceived
risk, and online transaction activities, this research stream approaches the phenomenon predominantly
from the buyers’ perspective and examines the effects of various institutional mechanisms that are
intended to protect buyers from the opportunistic conducts of sellers, based on the assumption that buyers
are more likely to be subject to sellers’ opportunism rather than vice versa, and thus their attitude and
perception determine the success of online transactions. This view is apparently biased to the extent that it
paints a partial picture of the e-commerce marketplace, since sellers, as the players on the supply side,
also have the option of discontinuing transactions or even rejecting orders from buyers when they lose
trust in the latter or perceive a high risk of transaction with buyers. Like sellers, buyers also harbor
incentives to behave opportunistically. As aforementioned, sellers face the common risk of chargeback
fraud of buyers, which causes a loss of delivered goods and incurs the penalty of chargeback fees. Thus,
research effort is warranted for the examination of trust and perceived risk from the sellers’ perspective;
in so doing, such research will complement existing e-commerce research to develop a balanced account
of the forces that can sustain online transactions.
In addition, the existing literature assumes away the e-commerce context of country of origin, as
a result of an exclusive focus on domestic settings where buyers and sellers are in the same country, with
very few exceptions that investigate the antecedents of buyers’ trust in cross-border, global B2B e-
commerce (Koh et al. 2012). Prior research indicates that order fulfillment and delivery of products or

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services ordered online acts as a critical factor influencing buyers’ trust in online vendors, which
determines the repurchase decisions of buyers, because the delivery capability of vendors is out of the
control of buyers and thus places buyers in a vulnerable position (Bart et al., 2005; Qureshi et al., 2009).
Failure to deliver ordered items on time or with the shipping method promised would instigate a violation
of psychological contract – the perceived obligations of vendors (Morrison and Robinson, 1997), and
consequently impairs the trust of buyers in vendors (Pavlou and Gefen, 2005). While the body of extant
literature acknowledges the risks arising from delivery problems to online buyers and the implications for
buyers’ trust in vendors, it neglects to examine the risks imposed on vendors that may result from buyers’
opportunistic behavior after online orders are delivered, such as chargeback fraud. Moreover, the
opportunism of buyers, such as unjustifiable claims for refunds, could also lead to violation of the
psychological contract held by vendors towards buyers’ obligations in e-commerce transactions.
The aforementioned risks to vendors become even higher in cross-border e-commerce. In these
situations, the information asymmetry between buyers and sellers tends to be very high because
transactions across countries face a variety of barriers such as culture, language, and legal enforcement,
which increase trade costs and risks of cross-border delivery, especially for SMEs (Gessner 2015;
Gomez-Herrera et al. 2014). Moreover, cross-border transaction intermediaries normally require sellers to
upload company information online for seller account approval, whereas buyers can open accounts
immediately without approval, which accentuates information asymmetry between buyers and sellers.
Thus, international e-commerce is more complicated and risky than the domestic online market,
suggesting that trust is even harder to establish in the cross-border context. Although researchers have
started to pay some attention to the cross-border context of e-commerce (Koh et al. 2012), concerns with
buyers’ trust and risk perception continue to dominate the focus of research, even though buyers and
sellers are likely to have different perceptions of trust and risks in cross-border transactions.
3. Theoretical Development and Hypotheses

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This paper aims to address the limitations of the existing literature by investigating the
antecedents of trust and perceived risk from the sellers’ perspective in the context of cross-border B2C
online transactions. To highlight the importance of the sellers’ perspective for e-commerce transactions,
we also examine the effects of trust and perceived risk on sellers’ intention to trade. We combine the
sociological perspective on trust (Shapiro 1987; Zucker 1986) and signaling theory (Spence 1974) to
develop our conceptual model. First, we adapt the prior conceptualization of trust (Mayer et al. 1995) to
define seller’s trust in buyers as the willingness of sellers to accept vulnerability based upon positive
expectations of buyers’ integrity and benevolence. Because a seller faces the entire population of buyers
who have access to a specific e-commerce website, a seller’s trust is conceptualized as trust in a
community of buyers. This is in line with the view of Pavlou and Gefen (2004), who argue that the nature
of online e-commerce renders “one-to-many” trust deserving of special attention. Second, we adapt the
concept of perceived risk (Jarvenpaa et al. 2000; Pavlou and Gefen 2004) to our particular research
context and define it as a seller’s belief that a loss could possibly occur as a result of buyers’ chargeback
fraud. This notion is different from transaction risks unrelated to the integrity of buyers, such as the
legitimate request for a refund due to product loss during delivery.
3.1. Conceptual model
According to the sociological perspective, trust is produced by institutional mechanisms to
govern economic transactions because these relatively independent social infrastructures create a shared
basis for common understandings of “how things are done” (Shapiro 1987; Zucker 1986, p. 64). As a
result, the installment of institutional mechanisms provides assurance of functional economic exchanges
to transacting parties and control the risks associated with transactions. In e-commerce, institutional
mechanisms are established by online transaction intermediaries and platforms to mitigate transaction
risks and facilitate transaction success (Fang et al. 2014; Pavlou and Gefen 2004). Given that the
mechanism design is not tailored to any specific transactions or traders but rather institutionalized to

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create the confidence of exchange parties that the transactions will take place as promised (Fang et al.
2014), it should provide a security net for sellers to assure them that buyers will behave as expected. Thus,
third-party online platforms should design an institutional mechanism to protect sellers and help them to
secure the payments of buyers and solve the problems arising from online transactions.
In addition to third-party specific mechanisms that are developed by specific web sites or online
marketplaces, there exist general online transaction mechanisms that operate beyond the control of any
specific online platforms (Fang et al. 2014; Grabner-Krauter and Kaluscha 2003). In the context of cross-
border e-commerce, sellers’ trust and risk assessment may also depend on a general institutional
mechanism that can produce a system of cross-border delivery to ensure reliable and effective shipment
of online orders from sellers to buyers. Without the safeguard of this mechanism, the challenges of
international shipments would increase the opportunistic incentives of transacting parties, especially the
likelihood of chargeback fraud of buyers (Gomez-Herrera et al. 2014). For example, fraudulent buyers
could conveniently use the excuse of “items not received” to claim refunds for items actually received.
When a cross-border delivery system is weak, sellers would face high uncertainty in item delivery.
Alternatively, institutional mechanisms can increase the confidence of sellers and mitigate their
transaction risk by providing information about buyers that can facilitate sellers to distinguish the good or
trustworthy buyers from the bad or opportunistic buyers. As indicated in the literature review, information
asymmetry between buyers and sellers increases concerns and risk perceptions about transactions. To
bridge the information gap, sellers could rely on information cues that are transmitted through the third-
party online platforms and signal types of buyers. According to signaling theory (Spence 1974),
information cues that serve as an effective signal should lead to a separating equilibrium, in which the bad
type and good type of buyers demonstrate unique characteristics or engage in distinctive behaviors. In
online shopping, buyers could reveal their types through either intrinsic characteristics that direct their
transaction behavior or purchase behavior that is driven by their types.

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In the context of cross-border e-commerce, a prominent information cue that can set honest
shoppers apart from deceitful ones is the nationality of buyers, since countries are distinctive entities that
represent unique value systems, culture, and social norms, which connote differential degrees of
trustworthiness and integrity of country of residence (Koh et al. 2012). Moreover, past purchase behavior
also manifests a buyer’s type because an opportunistic buyer is more likely to engage in fraudulent
behavior than a trustworthy buyer is. Historical data can be collected and made accessible to sellers
through an institutional feedback mechanism established by an online platform to facilitate the success of
online transactions (Pavlou and Gefen 2004). This third-party specific mechanism would offer credible
feedback to sellers about the past trading behavior of buyers in cross-border e-commerce.
Overall, based on the sociological perspective (Shapiro 1987; Zucker 1986) and the signaling
theory (Spence 1974), we uncover a set of third-party specific and third-party independent institutional
mechanisms that are posited to influence sellers’ trust in buyers and their perceived risk of chargeback
fraud: seller protection mechanism, cross-border delivery mechanism, feedback mechanism, and
mechanism on buyers’ national integrity. In accordance with our research context, we further classify
these into country-level mechanisms (cross-border delivery and buyers’ national integrity) and
marketplace-level mechanisms (seller protection and feedback mechanism). Consistent with existing
literature (e.g. Fang et al. 2014; Gefen et al. 2003; Pavlou and Gefen 2004), we adopt the view that it is
traders’ perception about the effectiveness of transaction mechanisms that determines their attitudes
towards other transacting parties and risk perceptions. As a result, we develop a conceptual model (Figure
1) that encompasses the effects of the following institutional factors on sellers’ trust and perceived risk:
perceived national integrity of buyers, perceived effectiveness of cross-border delivery, perceived
effectiveness of feedback mechanism, and perceived effectiveness of seller protection.
Insert Figure 1 here

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3.2.1 Perceived effectiveness of feedback mechanism
Many third-party online transaction platforms, such as DHgate or eBay, develop mutual feedback
mechanisms so that both buyers and sellers can provide comments about each party’s behavior in
transactions and get access to past transaction records. In essence, feedback mechanisms are reputation
systems that accumulate and disseminate information about each party’s trading behavior (Pavlou and
Gefen 2004). From the seller’s perspective, a feedback mechanism is only effective when it provides
credible information cues that can help sellers to better distinguish between honest and fraudulent buyers.
In other words, the effectiveness of feedback mechanism is determined by the ability of a third-party
transaction platform to display buyers’ integrity based on their past transaction activities, such as sellers’
comments, buyer account status (e.g., premium buyers in DHgate.com), and age of the registered account.
Thus, we define perceived effectiveness of feedback mechanism as the extent to which a seller can
ascertain that the feedback mechanism designed by a third-party online platform provides accurate and
reliable information about buyers’ past trading activities.
Prior studies have established that an effective feedback mechanism can help buyers to establish
trust not only in individual sellers but also in the entire community of sellers (Ba and Pavlou 2002;
Houser and Wooders 2006; Lee et al. 2000; Pavlou and Gefen 2004). From the perspective of sellers, we
argue that feedback mechanisms also enable sellers to assess whether the marketplace functions as
expected and disseminate cues about buyers’ past transaction activities that may provide sellers with the
basis to build trust in buyers. Thus, an effective feedback mechanism facilitates the building of
transaction norms that bolsters sellers’ confidence and trust in the community of the marketplace (Pavlou
and Gefen 2004).
Moreover, according to information signaling theory, within a normal and trustworthy
marketplace, buyers’ reputation derived from their trading behavior would be viewed as an effective and
reliable index of their integrity. These information cues about transaction activities can be collectively

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viewed as a surrogate for the reputation of online buyers that can help to build sellers’ trust in the
community of buyers. The greater the information indices on buyers’ reputation, the higher the trust level
that sellers may develop, because sellers would have a better ability to distinguish good and bad buyers in
a functioning market environment.
Furthermore, because an effective feedback mechanism provides credible and accurate
information that enables the distinction between honest and fraudulent buyers, it helps sellers identify
(and thus avoid) fraudulent buyers. Further, it also sends a strong signal to buyers that sellers could rely
on the feedback mechanism to set apart good buyers from bad. As a result, a functional feedback
mechanism should deter buyers from acting opportunistically or reduce their incentive for doing so,
thereby reducing sellers’ concerns regarding possible chargeback fraud. In addition, it creates an implicit
norm that each party is expected to abide by and violation of these rules would result in sanctioning
(Fukuyama 1995; Pavlou and Gefen 2004), thus providing assurance to sellers about the buyers’ conduct
in online transactions.
Therefore, we posit that:
H1a. The perceived effectiveness of a feedback mechanism increases sellers’ trust in buyers.
H1b. The perceived effectiveness of a feedback mechanism reduces sellers’ perception of risk
of chargeback fraud when transacting with buyers.
3.2.2 Perceived effectiveness of seller protection
Some third-party platforms provide seller protection mechanisms that safeguard merchants
against financial losses in the event of an unauthorized purchase, such as a chargeback request based on
an "item not received" claim. These protection mechanisms operate on different requirements and vary
across different third-party platforms. For instance, eBay and DHgate.com both cover physical items that
are sold and shipped with proof of delivery, but countries and regions are different in terms of coverage.
Moreover, eBay requires a signature confirmation of delivery in addition to proof of shipment for all
payments over US$750, while DHgate.com does not impose such requirement. In contrast, Amazon’s

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seller protection policies only cover payment-related chargebacks, such as stolen credit cards, while
Amazon sellers are responsible for chargeback fraud associated with other service-related reasons, such as
non-receipt of goods. Thus, the effectiveness of seller protection as perceived by sellers would be
different across sellers, due to the restrictions and requirements of seller protection policies applied to
different scenarios.
Adapting the concept of a protection mechanism for buyers from prior research (Chellappa and
Pavlou 2002; Pavlou and Gefen 2004) to the protection of sellers in cross-border transactions, we define
the perceived effectiveness of seller protection as the extent to which sellers believe that these
mechanisms ensure that their trading with buyers in a cross-border transaction platform can be fulfilled in
accordance with their expectations. According to the sociological perspective on trust (Shapiro 1987;
Zucker 1986), the institutionalization of operating mechanisms that are not customized to any particular
transactions or traders would produce trust of exchange parties by establishing rules and norms to control
exchange behavior and provide insurance against future deviant behaviors and outcomes. Seller
protection mechanisms create a security net for sellers to ensure payments from buyers and resolve
disputes in online transactions.
The guarantees supported by the institutional protection mechanisms increase sellers’ confidence
in the fulfillment of transactions and also send a signal to the community of buyers about the expected
purchase behavior in online transactions. As a result, the protection mechanisms facilitate the building of
sellers’ trust in buyers. Further, by installing protection mechanisms to protect sellers’ interests, third
party platforms mitigate sellers’ perceived risk of fraudulent buyer behavior, because the protection
mechanisms reduce social uncertainty by providing a framework to govern transactions and direct buyers
to behave in a socially acceptable way (Gefen 2000; Pavlou and Gefen 2004). By restraining buyers’
opportunistic incentives and minimizing payment uncertainty, a functional protection mechanism helps to
lower sellers’ perceived risk of buyers’ chargeback fraud. As the effectiveness of protection mechanisms

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as perceived by sellers increases, their trust in buyers grows and their perceived risk recedes. Therefore,
we posit that,
H2a. The perceived effectiveness of seller protection increases sellers’ trust in buyers.
H2b. The perceived effectiveness of seller protection reduces sellers’ perceived risk of
chargeback fraud.
3.2.3 Perceived effectiveness of cross-border delivery
Compared to domestic e-commerce, the success of international trade is more heavily dependent
on the delivery network between buyers and sellers across countries. Because it is also more challenging
to manage international delivery logistics, sellers face higher risk of transaction and uncertainty of
payment in cross-border e-commerce. For instance, cross-border delivery is more likely to be subject to
delay and errors, hence increasing the chance of “item not received” claims as well as an incentive for
chargeback fraud. As noted in the literature, poor delivery performance is viewed as a major factor
contributing to cross-border transaction risk (Lopez-Nicolas and Molina-Castillo, 2008; Koh et al. 2012).
Thus, a reliable and effective cross-border delivery mechanism is needed to ensure timely receipt of
online orders and to support the success of cross-border e-commerce. Consistent with prior studies, the
perceived effectiveness of the cross-border delivery mechanism is defined as the extent to which sellers
believe that cross-border e-commerce platforms have developed effective order management mechanisms
to guarantee that their goods can be delivered on time with proof of delivery.
From the sociological perspective on trust (Shapiro 1987; Zucker 1986), an effective cross-border
delivery mechanism should create a sense of security for sellers based on objective structures and
institutions that ensure delivery of online orders to international buyers. When the security of delivery is
institutionalized, transacting parties would develop a shared understanding that successful delivery, as a
normal situation, is what it ought to be (Gefen et al. 2003; McKnight et al. 1998; Zucker 1986). Because
the delivery outcome is in accordance with what sellers expect, they develop trust in the community of

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international buyers. In contrast, when the delivery mechanism is weak, successful cross-border delivery
would not be deemed typical or as anticipated (Gefen et al. 2003); as a result, sellers’ trust in buyers
would be reduced.
Moreover, sellers’ concern regarding buyers’ fraudulent incentives will grow if they perceive that
the cross-border delivery system is ineffective, because in this situation, buyers could justifiably file
claims for items not received. Thus, a functional delivery mechanism can effectively mitigate sellers’
perceived risk. For example, some e-commerce platforms cooperate with logistics and insurance
companies to offer shipping insurance for a package that is lost or damaged in transit. In addition, cross-
border delivery mechanisms can effectively manage online orders by requesting proof of delivery and
signature confirmation, which demonstrate a buyer’s identity and delivery address and consequently
reduce the risk of chargeback owing to the “item not received” claim initiated by a buyer. Because
infrastructures for international delivery vary across countries, sellers perceive the effectiveness of cross-
border delivery at differential levels, which causes variation in their trust and perceived risk. Based on the
above arguments, we posit that:
H3a. The perceived effectiveness of cross-border delivery increases sellers’ trust in buyers.
H3b. The perceived effectiveness of cross-border delivery reduces sellers’ perception of risk of
chargeback fraud when transacting with buyers.
3.2.4 Perceived national integrity
Cross-border e-commerce presents more challenges for online transactions than domestic e-
commerce due to the drastic differences in culture, language, and legal enforcement between countries
(Gessner 2015; Gomez-Herrera et al. 2014; Hofstede 2001). The cross-country differences give rise to
differences in characteristics and behavior of consumers in global trading (Yavas and Green 1992;
Walters 1997). To the extent that the value systems, cultures, and institutions vary significantly across
countries, country-of-origin sends a reliable signal about the traits of local residents that are shaped by

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these social norms (Koh et al. 2012). Prior research indicates that the nationality of a firm or a seller
serves as a reliable information cue to judge the firm’s or the seller’s trustworthiness (Koh et al. 2012;
Zaheer and Zaheer 2006).
In cross-border transactions, transacting parties tend to have high information uncertainty
regarding the nature of their counterparts; a salient cue that can bridge the information gap in this context
is the perceived integrity of the country-of-origin of the traders, which signals the expected behavior and
beliefs about the moral character of the traders in a country (Koh et al. 2012). Following Koh et al. (2012),
we define the perceived national integrity of buyers as the extent to which buyers located in a country are
presumed to adhere to moral principles in their actions. The value system and culture as overarching
social norms of a country shape buyers’ behavior in an expected direction. Thus, sellers would expect that
buyers from a country with a high national integrity would conform to social norms that value adherence
to moral principles; thus, the community of buyers from this type of country is perceived as possessing
high integrity, which induces high trust from sellers. This proposition is consistent with the view that trust
builds on social norms that most people are expected to conform to (Fukuyama 1995; Mackie 2001).
Given that high national integrity signals social norms that value adherence to moral and ethical
principles, buyers from a country with high integrity would be expected to follow social rules and
customs, which prevent them from engaging in opportunism (Doney et al. 1998). The higher the national
integrity of a country, the less likely it is that buyers from the country will deviate from social virtues and
act opportunistically. In contrast, countries with low integrity may exhibit less conformity to social
virtues and opportunistic conduct will tend to be more tolerated, hence triggering sellers’ concern
regarding the fraudulent behavior of buyers. Taken together, we thus posit that:
H4a. The perceived national integrity of buyers increases sellers’ trust in the buyers.
H4b. The perceived national integrity of buyers reduces sellers’ perceived risk of chargeback
fraud when transacting with buyers.

19
3.3. Consequences of sellers’ trust and perceived risk
To highlight the importance of sellers’ trust and perceived risk, we will now examine their
impacts on sellers’ intention to trade. Adapting the concept of transaction intention from prior research
(Gefen et al. 2003; Pavlou and Gefen 2004) to our research context, we define sellers’ intention to trade
as a seller’s intention to sell products to the community of buyers. Most cross-border e-commerce
platforms, such as DHgate.com (the online platform where we collected data for empirical test), allow
sellers to cancel orders before shipping without any penalty imposed by the platforms, given that various
unexpected contingencies could occur, including out-of-stock items, unavailability of the delivery
network, and buyers’ requests, among others. In-depth interviews conducted with 21 sellers indicated that
these sellers would definitely cancel an order if they were suspicious of buyers’ incentives or if they
perceived a high likelihood of chargeback risk, such as when buyers had poor transaction reviews or
unverified addresses. One co-author has set-up an online store and his e-commerce experience as a seller
confirmed these insights from the in-depth interviews.
To the extent that trust represents a trustor’s expectation of a trustee’s integrity (Mayer et al. 1995;
Gefen et al. 2003) and perceived risk reflects a transacting party’s belief about possible unexpected losses
in transactions (Jarvenpaa et al. 2000; Mayer 1995), both the positive expectation of traders’ traits and the
negative perception of transaction risk give rise to attitudinal changes, which cause behavioral intentions
that are consistent with the organizational cognition (Jarvenpaa et al. 2000; Pavlou and Gefen 2004).
Given this overarching logic, we argue that sellers’ trust increases and perceived risk of chargeback fraud
dampens their intention to trade. Since trust lessens the high social uncertainty of online transactions
(Gefen and Straub 2004), sellers’ trust in buyers leads to the expectation that buyers would not engage in
fraudulent behavior and that payment from buyers can be secured. As a result, high trust triggers a
positive attitude toward transaction with buyers, which fosters the behavioral intention to trade. This
proposition parallels the positive association robustly supported in extant literature between buyers’ trust

20
in online vendors and their purchase intention (e.g. Gefen 2000; Gefen and Straub 2004; Pavlou and
Gefen 2004).
In contrast, when sellers perceive a high risk of chargeback fraud from buyers, the negative
perception activates an unfavorable attitude toward trading with the opportunistic buyers, which inhibits
their incentive to sell. As noted before, the challenge of order management in cross-border delivery,
coupled with the one-time nature of an online transaction (Gefen and Straub 2004), accentuates the
chargeback risk for sellers. In this one-shot game across national borders, fraudulent buyers have a strong
motivation to act opportunistically. Thus, sellers must exercise caution in dealing with potentially
opportunistic buyers in order to minimize the possibility of financial losses stemming from buyers’
fraudulent behavior. The negative effect of sellers’ perceived risk on their intention to trade echoes the
negative relationship between buyers’ perceived risk and their behavioral intention in e-commerce (e.g.
Gefen 2002; Jarvenpaa et al. 2000; Pavlou 2003).
Following previous studies examining the relationship between trust and the perceived risk of
buyers (e.g. Gefen 2002; Jarvenpaa et al. 2000; Luo, 2002; Pavlou and Gefen 2004), we postulate a
negative relationship between sellers’ trust and their perceived risk of chargeback fraud to complete the
structuring of our conceptual model. The key reasoning is that sellers’ trust, as indicative of their positive
expectations of buyers’ integrity and the willingness to accept vulnerability, attenuates sellers’ concerns
regarding buyers’ fraudulent incentives and behavior. In other words, when sellers develop high trust in
buyers, they are confident that buyers will not do harm to them by filing fraudulent chargeback claims.
The above arguments lead us to posit that:
H5. Sellers’ trust in buyers increases sellers’ intention to trade in the cross-border online
marketplace.
H6. Sellers’ trust in buyers reduces their perceived risk of chargeback fraud.
H7. The perceived risk of chargeback fraud from buyers decreases sellers’ intention to trade
in the cross-border online marketplace.

21
3.5. Control variables
To examine the influence of the above mentioned antecedents of trust and perceived risk of
chargeback fraud on transaction intention, and the relationships among these antecedents, this study
controls for four factors: one factor that may influence perceived risk, product type; one factor that may
influence trust, trust propensity; and two factors that may influence trust and perceived risk, buyer
verification and sellers’ past experience.
3.5.1. Product type
We use dummy coding to control for two kinds of product types, tangible (physical) goods and
intangible goods (e.g. digital content). Tangible goods are those that can be physically touched (e.g., a TV)
while intangible goods do not have a physical nature (e.g., e-books, commercial software, audio or video
files, or virtual currencies). Currently, most seller protection policies do not cover intangible goods since
their delivery does not include verifiable and traceable shipping documentation, such as that provided by
third-party logistical companies (e.g. DHL or UPS). Thus, we expect that product type will affect sellers’
perceptions of risk in a cross-border online marketplace. The dummy variable is coded as ‘1’ if vendors
mainly sell tangible goods, and ‘0’ if this is not the case.
3.5.2. Buyer verification
We use a dummy variable to control for whether or not a buyer is verified. A verified buyer has
provided additional evidence to third-party platforms to confirm their identity or shipping address. Sellers
tend to be more confident that delivering goods to these buyers will not result in chargeback fraud.
Therefore, we expect that buyers with a verified identity status will receive higher trust from sellers, while
unverified accounts are likely to increase the level of risk perceived by sellers.
3.5.3. Trust propensity
Individual propensity to trust, also known as disposition to trust, refers to a person’s
psychological tendency to be willing to depend on others in different contexts (McKnight et al. 1998;

22
Kim and Kim 2005; Gefen 2000; Mayer et al. 1995). In this research, trust propensity relates to the
internal personal characteristics of sellers. Some sellers have a naturally higher inclination to believe that
people are in general trustworthy and that their behaviors conform to social norms. Sellers with a high
degree of trust propensity are more likely to believe that buyers participating in an online transaction
market are ingenuous. In this study, we control for the effect of trust propensity on sellers’ trust in buyers.
3.5.4. Seller’s past experience
The number of successful transactions between a buyer and a seller represents the performance
quality of both sellers and buyers in an online marketplace. As the number of successful transactions with
buyers grows, sellers can gradually build general opinions regarding the integrity of buyers (Tirole 1996).
Successful transactions are effective signals that a buyer can transmit to manifest his or her honesty or
integrity to sellers. Thus, sellers will adjust their assessment of the trustworthiness of a community of
buyers’ as the number of successful transactions increases. Specifically, more positive experiences with
buyers enhance sellers’ trust in buyers and encourages sellers to approve transaction orders and fulfill
transaction obligations, such as delivering goods on time.
4. Study Design and Methodology
In this research, we do not consider third-party platforms that only support online buyer-seller
information exchange and that do not have integrated transaction mechanisms (e.g. Alibaba.com). Our
proposed hypotheses are tested via sellers on the DHgate online marketplace. DHgate.com is one of the
biggest e-commerce websites connecting mainland China-based SMEs with overseas buyers, providing a
platform in which people can order Chinese manufactured products directly through the site – similar to
eBay, Amazon, and Yahoo auctions where many international small merchants sell items around the
world. Several payment methods are available on DHgate.com, including PayPal, credit card and Skrill.
As a transaction platform, DHgate targets mainly at small- and medium-sized Chinese sellers, and buyers
from all over the world. All sellers on DHgate.com are registered members. When they register, a

23
professional team from DHgate will verify their qualifications by checking their business license and
other legal certificates. Only verified small- and medium-sized Chinese sellers can become VIP DHgate
members and sell items on Chinese wholesale website.
The measurement items in our study were adapted from prior studies. The items were modified
based on a major pre-test of the survey instrument with a sample of 65 sellers on DHgate. Constructs
were measured using items on seven-point Likert-type scales anchored from “strongly disagree” (1) to
“strongly agree” (7) (see Appendix). In addition, there were two dummy control variables (0 or 1).
To obtain data for our research, an online survey was carried out using a leading Chinese web-
based survey platform. We created a questionnaire in English that was reviewed for content validity by a
group of IS academics from three universities. As the questionnaire was administered in Chinese, we
translated the English questionnaire to Chinese and then back to English to ensure translation equivalence
(Brislin 1970). A professional translator and two research assistants independently translated the original
items from English into Chinese. The researchers analyzed the independently translated Chinese versions
of the items and came to an agreement on the final version for the questionnaire. The questionnaire was
then translated back into English by another professional translator to confirm translation equivalence.
The URL of the questionnaire was authorized and then published in the official seller forum of DHgate
(http://bbs.dhgate.com/forum.php#hp-lc-8). Forum members also received a private message from the
forum manager soliciting their participation in a survey of sellers’ trust in buyers. The message described
our research purpose, provided the URL of the questionnaire, and, as an incentive, offered respondents
the opportunity to register in a draw to win an iPhone6. The questionnaire was pilot tested among a group
of 53 sellers, who were not included in the main survey. We found preliminary evidence that the scales
were reliable and valid.
For the main survey, a total of 500 completed survey responses were received within one month
and 57 invalid or suspicious responses were removed (e.g., duplicate IP addresses or unreasonable survey

24
completion times). Subsequently, 443 qualified responses were obtained for quantitative data analysis.
Prior to data collection, the required sample size was computed based on the power analysis technique
using G*Power 3.0 (Faul et al. 2007). For our conceptual model and a medium effect size (1 - β = 0.95, α
= 0.05) the sample size should be at least 121. Thus, 443 responses exceeded the requirements for
detecting a medium effect using the PLS-PM technique. All of the respondents were DHgate sellers. To
test for nonresponse bias, we compared the demographic characteristics of respondents in the early and
late waves of data collection and found no significant differences. Likewise, we compared the
demographic characteristics of respondents and non-respondents in the second wave of data collection
and found no significant differences.
4.1. Data analysis
Our proposed research model was evaluated via PLS path modeling in SmartPLS 3.0M. PLS path
modeling has become popular in modern quantitative research, particularly because it has notable
advantages, such as minimal demands on measurement scales, sample distribution, and sample size. It
excels at causal-predictive analysis in which hypothesized relationships are complex and few bases have
been established (Hair et al. 2014). The control variables were included as additional exogenous variables.
The majority of respondents were male (82.30%), educated (80.95% with at least a bachelor
degree), and below 45 years of age (96.04%, as shown in Table 1). This is consistent with our expectation:
most small- to medium-sized sellers participating in cross-border e-commerce are younger and have a
good educational background, enabling them to learn and understand how to use effectively the e-
commerce platforms. The top four markets for global sales were: USA (36.28%), UK (28.34%), Canada
(12.13%), and Australia (11.53%). These four countries accounted for the majority (88%) of trade.
Insert Table 1 here
4.2. Construct reliability, convergent validity, and discriminant validity

25
To test convergent validity and reliability, we used three metrics: average variance extracted
(AVE), Cronbach’s alpha and composite reliability (CR). As illustrated in Table 2, the values of AVE and
CR for all constructs are satisfactory, with composite reliabilities of 0.860 or more and AVEs of 0.673 or
above. Further, as suggested by Nunnally (1978), Cronbach’s alpha is greater than 0.70 for all constructs.
Thus, the measurement items appear reliable and converged on the latent constructs.
Insert Table 2 here
To assess discriminant validity, we used the techniques of Fornell and Larcker (1981), Chin
(1998) and Henseler (2015). First, we compared the square-root of the AVE for each construct to the
inter-correlations with other constructs (see Table 3). We found that the square-root of AVE for each
construct was higher than its inter-correlations with other constructs. Second, we assessed discriminant
validity by making a comparison between the loadings of an item on its associated construct and its cross-
loading on other constructs. For our model, all items loaded on their corresponding constructs more
strongly than on other constructs (see Table 4). Third, the heterotrait-monotrait ratio of correlations
(HTMT), a new approach for assessing discriminant validity in variance-based SEM, as suggested by
Henseler (2015), was used. Table 5 shows that all HTMT values were below the 0.90 threshold. To
further test for multicollinearity, we computed variance inflation factors (VIFs). These ranged between 1
and 5, suggesting that multicollinearity was not a problem in our study. Overall, there was strong
empirical support for the reliability and validity of the constructs in our research model.
Insert Table 3 – 5 here
4.3. Common method bias

26
We conducted several tests to assess the potential threat of common method bias (CMB). First,
we performed Harman’s single-factor test by entering all of the constructs into a principal components
factor analysis (Podsakoff and Organ 1986). Five factors were produced and the first accounted for just
33.26% of the variance. This suggests that there is unlikely to be significant common method bias.
Second, following the recommendation of Kock (2015) and Kock and Lynn (2012), we conducted a full
collinearity test and found that all VIFs were lower than 3.3. Thus, common method bias does not appear
to be of concern in our study. Third, following the recommendation of Podsakoff et al. (2003), we
performed a method factor test via PLS-PM. The results suggest no significant common method bias in
our data.
5. Results
5.1. Hypotheses testing
In total, the statistical results supported nine of the eleven hypotheses in our research model. We
computed t-statistics and path significance levels for each of the hypothesized relationships using the
bootstrapping method. Path coefficients and R2 values were obtained by running the PLS algorithm to
assess the predictive performance of the structural model. The construct measuring sellers’ intention to
deal with orders had an R2 value of 0.341, indicating that the model accounted for 34.1% of the variance
in sellers’ intention to process and deliver goods after receiving buyers’ payment. Moreover, more than
half of the transaction risk perceived by sellers (R2=0.526) was explained by their perceptions of feedback
mechanism effectiveness, seller protection policies, national integrity, the effectiveness of cross-border
delivery, and sellers’ trust in the community of buyers. Moreover, 36.6% of the variance in sellers’ trust
in buyers was captured by our four exogenous variables. Overall, the empirical results strongly confirmed
the power of our research model in explaining sellers’ intentions to receive payment and deliver goods.
As shown in Figure 2, most hypotheses received strong support. Perceived effectiveness of the
feedback mechanism had a significant impact on perceived risk (β = -0.257, t=4.945, p < 0.001),

27
supporting H1b. However, it did not have a significant impact on seller’s trust (β = .001, t=.019), failing
to support H1a. Seller protection mechanism has a significant positive effect in sellers’ trust in buyers (β
= 0.133, t=2.486, p<0.05) and a significant negative effect on sellers’ perceived risk of chargeback fraud
(β = -0.191, t=3.127, p<0.01), supporting H2a and H2b respectively. Perceived effectiveness of cross-
border delivery has a significant positive effect on sellers’ trust (β =.232, t=2.672, p < 0.01), supporting
H3a, but it has a null effect on perceived risk of chargeback fraud (β = - .052, t=.921). Thus, H3b is not
supported. As expected, perceived national integrity has a significant, positive effect on sellers’ trust (β
= .229, t=3.139, p<0.01), but a significant, negative effect on perceived risk of chargeback fraud (β = -
.167, t=2.859, p<0.01), thus supporting H4a and H4b. While sellers’ trust significantly enhances their
intention to trade with buyers (β = .323, t=4.080, p<0.001), their perceived risk of chargeback fraud
reduces the intention (β = -.352, t=5.442, p<0.001), thus supporting H5 and H7, respectively. Finally,
sellers’ trust significantly reduces their perceived risk of chargeback fraud (β = -.149, t=2.539, p<0.05),
thus supporting H6.
Insert Figure 2 here
5.2. Post hoc assessments of mediating effects
Given the conceptual model, we speculate that sellers’ trust (ST) and perceived risk (PR) act as
two mediating variables between the four antecedents and sellers’ intention to trade. We use the
bootstrapping method (Preacher and Hayes 2008) to test for multiple mediation effects. Bootstrapping is a
nonparametric resampling procedure that does not impose the assumption of normality on the sampling
distribution. This method involves repeatedly sampling from the data and estimating the indirect effects
of mediators in each resampled dataset. Based on the repeated samples, an empirical approximation of the
indirect effects can be estimated and used to construct 95% confidence intervals (CI) for the indirect
effects. If the confidence interval for a mediator contains zero, it means that the indirect effect is

28
insignificant and thus the mediating effect is not supported. In addition, a contrast between two mediators
can be conducted to show how their indirect effects can be distinguished in terms of magnitude on the
dependent variable (DV). Following Preacher and Hayes’ (2008) recommendations, the bias-corrected
(BC) bootstrapping method is used. Prior studies have suggested that bootstrapping is in general superior
to the Sobel test (e.g., Williams and MacKinnon 2008). The BC bootstrap performs best in terms of both
statistic power and Type I error rate (Preacher and Hayes 2008). Using Preacher and Hayes’ SPSS macro,
each independent variable (IV) can be tested in a separate model if two or more IVs are included. In each
model, one IV may be identified as the primary IV to be examined and other IVs may be treated as
covariates.
Table 6 shows the results of our tests for mediating effects, in which perceived national integrity
(PNI), perceived effectiveness of cross-border delivery (PECBD), perceived effectiveness of feedback
mechanism (PEFM), and perceived effectiveness of seller protection (PESP) are the IVs, sellers’ trust (ST)
and perceived risk (PR) are the mediators, and intention to trade is the DV. First, a model is examined in
which PNI is the independent variable (Model 1 in Table 6) with PECBD, PEFM and PESP treated as
covariates. As Table 6 shows, PNI does have a significant total effect on INT (β=0.272, t=6.477). When
the mediators, ST and PR, are introduced, PNI still has a significant direct effect on INT, but the effect is
decreased (β=0.219, t=5.030). An examination of the specific indirect effects shows that only ST acts as a
mediator, since its 95 percent CI does not contain zero. The contrast between ST and PR has a 95 percent
CI of -0.015 to 0.095, indicating that the indirect effects of ST and PR do not differ significantly, despite
the fact that one is significantly different from zero and the other is not. Similar findings are obtained
when we examine model 3, which has PECBD as the independent variable and model 4, which has PESP
as the independent variable, respectively. Finally, we examine model 2, in which PEFM is the
independent variable (see Table 6). Since the CI does contain zero (-0.015 to 0.064), this means that ST
and PR do not act as mediators. In other words, the direct impact of PEFM on INT is not mediated by ST

29
or PR. In summary, the analyses show that only ST partially mediates the impact of PNI, PESP and
PECBD on INT, whereas the impact of PEFM on INT is not mediated through ST or PR.
Insert Table 6 here
6. Discussion
6.1. Research Implications
While the trust and perceived risk of transacting parties are critical foundations for the success of
e-commerce, the extant literature is exclusively concerned with buyers’ trust and their perceived risk of
online transactions based on the assumption that buyers are subject to the opportunistic behavior of sellers,
owing to information asymmetry in online transactions. This study challenges this assumption and calls
attention to the need to protect sellers from the fraudulent behavior of buyers, such as chargeback fraud.
Drawing on the sociological perspective (Shapiro 1987; Zucker 1986) and the signaling theory (Spence
1974), we develop a conceptual model to examine the antecedents of sellers’ trust and perceived risk as
well as their effects on sellers’ intention to trade online in the context of cross-border e-commerce. In so
doing, this paper contributes to the e-commerce literature in the following ways.
First, this study extends the body of extant literature on the determinants of trust and perceived
risk from the sellers’ perspective. Specifically, we propose and test the effects of a comprehensive set of
institutional mechanisms on sellers’ trust and their perceived risk of chargeback fraud. We find that the
mechanism of perceived national integrity enhances sellers’ trust and reduces the perceived risk of
chargeback fraud, because the country of origin sends credible signals to sellers about the trustworthiness
of the community of buyers, thus narrowing the information asymmetry between sellers and buyers. This
finding complements Koh et al.’s study (2012) which demonstrates a positive association between the
national integrity of sellers and buyers’ trust, suggesting that the country of residence of both buyers and
sellers is a critical factor that can give rise to mutual trust in e-commerce. Further, we show that the
perceived effectiveness of seller protection mechanism also increases sellers’ trust and mitigates their

30
perceived risk of chargeback fraud. This finding highlights the importance of providing a payment
security net for sellers in online transactions, which stands in sharp contrast to the dominant view in the
extant literature about the necessity to protect buyers from the opportunistic behavior of sellers (e.g. Fang
et al. 2014; Gefen et al. 2003; Koh et al. 2012; Pavlou and Gefen 2004; Pennington et al. 2003). In this
sense, our research broadens the scope of existing literature by developing “the other half” of a balanced
account of mechanisms designed to protect transacting parties in e-commerce.
Contrary to our expectations, the perceived effectiveness of feedback mechanism does not
increase sellers’ trust. This surprising result might perhaps be due to the fact that the observed
information cues only register the buyers’ behavior, which go beyond the control of sellers and thus may
disguise the true type of buyers. For example, fraudulent buyers may repeatedly purchase from multiple
sellers to earn enough credits and maliciously initiate chargeback later. As a result, sellers do not perceive
a buyer’s information presented through the feedback mechanism as a credible signal of the buyer’s
integrity. The null effect of feedback mechanism about buyers’ behavior on sellers’ trust contrasts with
the positive effect of a feedback mechanism about sellers’ behavior on buyers’ trust, which exists in a
domestic online marketplace (Pavlou and Gefen 2004). The difference in the effects of feedback
mechanism between international and domestic e-commerce suggests that market geography
(international vs. domestic) may act as a contingent condition for the relationship between the feedback
mechanism and the trust of transacting parties. On the other hand, the feedback mechanism proves
effective in reducing sellers’ fear of buyers’ chargeback fraud. This result is consistent with prior studies
demonstrating the effectiveness of feedback mechanism in mitigating buyers’ perceived risk, further
corroborating the importance of providing transaction feedback for both parties.
Second, we contribute to existing literature by examining a third-party independent institutional
mechanism tailored specifically to cross-border e-commerce – i.e. the cross-border delivery mechanism.
The results show that the perceived effectiveness of this general institutional mechanism enhances sellers’

31
trust in buyers, but surprisingly does not influence the perceived risk of chargeback fraud. The lack of
effect on perceived risk might be due to the fact that this general mechanism is mainly intended to
facilitate the success of delivering ordered items from sellers to buyers as opposed to safeguarding against
any fraudulent behaviour of buyers. Interestingly, the asymmetric effects of this general institutional
mechanism on sellers’ trust and perceived risk contrast with the asymmetric effects of the third-party
specific feedback mechanism on sellers’ trust and perceived risk. The contrast suggests that different
institutional mechanisms play distinctive roles in shaping trust and perceived risk as two critical
foundations of e-commerce.
Furthermore, extant literature indicates that institutional mechanisms in e-commerce could
convey messages that evoke either positive or negative framing effects on the perceptions of transacting
parties (Fang et al. 2014). To the extent that the feedback mechanism provides evidence of the fraudulent
conduct of buyers, while the cross-border delivery mechanism emphasizes the assurance of successful
product shipment, they connote different outcomes of transactions with buyers, which may explain why
they trigger differential effects on sellers’ trust and perceived risk. From this standpoint, our research
provides additional support to the framing effect that exists in online transactions.
Third, our research demonstrates the strong effects of sellers’ trust and the perceived risk of
chargeback fraud on their intention to trade with buyers online, which challenges the conventional stance
that the continuance and success of online transactions hinge on buyers’ trust and perceived risk because
buyers are more likely to be subject to sellers’ opportunistic behavior (Fang et al. 2014; Gefen et al. 2003;
Koh et al. 2012; Pavlou 2003; Pavlou and Gefen 2004; Pennington et al. 2003). In addition, substantial
risk also arises from buyers’ fraudulent conduct in cross-border e-commerce. This study shows that either
when sellers’ perceived risk of chargeback fraud increases or when their trust declines in buyers and
further heightens the perceived risk, they would be much less inclined to sell products to buyers.
Moreover, sellers’ trust mediates the effects of most institutional mechanisms on sellers’ intention to trade.

32
Overall, these findings highlight the importance of sellers’ trust and perceived risk, underlining the
necessity to consider sellers’ interests in designing mechanisms to sustain cross-border online transactions.
6.2. Managerial implications
Our study provides useful recommendations for cross-border platform developers and cross-
border transaction policy makers. Cross-border online platforms should allocate enough resources to build
effective operational mechanisms to protect sellers against fraudulent buyers, in addition to the ones
designed for the protection of buyers. To enhance sellers’ trust in buyers and reduce their perceived risk
of chargeback fraud in cross-border trades, third-party specific platforms should strengthen the
institutional mechanism regarding the national identity of buyers and the institutional mechanism for
seller protection. Since sellers generally deem country of residence as a credible signal of buyers’
trustworthiness, cross-border platforms should flag-up for sellers the potential risks associated with
buyers from countries of low national integrity.
Investments in feedback mechanisms can mitigate sellers’ concerns regarding buyers’ chargeback
fraud, but may not work effectively to increase sellers’ trust in buyers. Specifically, online cross-border
platforms should consider implementing programs that increase the transparency of buyers’ identity. For
example, one idea to consider would be an online signature mechanism in which buyers are required to
“sign” online for each transaction. In addition, a biometric fingerprint identity mechanism integrated
within the mobile app of a cross-border transaction platform could be implemented to prevent
unauthorized transaction claims. Moreover, third-party platforms as an aggregate should work together to
design a standard delivery system that is tailored specifically to cross-border transactions. For example,
they may form a consortium to set up an effective tracking mechanism for goods ordered from
international buyers. In this way, they could increase substantially sellers’ trust.
To counteract the fraudulent incentives of buyers, financial and trade policy makers should be
advised to reconsider and reform the chargeback system that has been long in existence to mainly protect

33
buyers from the risks of online transactions. The chargeback mechanism has proven a double-edged
sword: while it safeguards buyers against sellers’ opportunism, it also fosters the fraudulent incentive of
chargeback claims on the part of buyers. As shown in our study, sellers’ concern regarding chargeback
fraud inhibits their intention to trade, which may potentially constrain the growth of cross-border e-
commerce. To curb buyers’ opportunism, policy makers may consider adopting a nationwide real-name
registration system in which buyers are required to associate their legal name with their online purchase
accounts so that fraudulent buyers cannot create multiple online accounts with different email addresses
to disguise their identity. Moreover, policy makers should consider adjusting the 180-day chargeback
period, which actually magnifies buyers’ fraudulent incentives. For example, for the “item not received”
chargeback claim, buyers should only be given the right for a short claim period, during which most
products are delivered in normal situations. This policy could also be applied to unauthorized transactions,
because credit card holders are expected to report any unauthorized payments promptly.
6.3. Limitations and future research
This study has several limitations, which create avenues for future research. First, while we focus
on the direct effects of the various mechanisms, further research efforts are merited regarding
examination of the boundary conditions of these effects on sellers’ trust and perceived risk. Useful
insights could also be generated from future studies into the conditions under which the institutional
mechanisms could substitute for the influences of trust or perceived risk. Second, the impacts of trust and
perceived risk on sellers’ intention to trade may depend on the effectiveness of institutional mechanisms.
Prior research indicates that when institutional mechanisms are either very strong or very weak, trust and
perceived risk of buyers become immaterial in influencing buyers’ transaction intention (Gefen and
Pavlou 2012). It thus merits research efforts to investigate the extent to which the strong effects of sellers’
trust and perceived risk on their intention to trade would vary at different levels of the effectiveness of the
institutional mechanisms examined in this study. Third, the effectiveness of the proposed mechanisms

34
may vary between SMEs and large enterprises. Future research can extend our conceptual model to this
group of online sellers. Fourth, our study is based on a cross-sectional research design, while the causal
effects of our conceptual model would ideally be examined in a longitudinal design. Finally, the proposed
effects of some institutional mechanisms on either sellers’ trust (feedback mechanism) or perceived risk
(cross-border delivery mechanism) are not supported in this study. Thus, future research may further
reexamine these specific relationships.
Acknowledgements
This work was supported by “Fundamental Research Funds for the Central Universities” (Project no.
2014B18914) and The Humanities and Social Sciences Foundation of the Ministry of Education in China
(Project No. 16YJC630028).
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39
marketplace-level
institutional mechanisms
Perceived effectiveness of
feedback mechanism
Perceived effectiveness of
seller protection
Perceived effectiveness of
cross-border delivery
Perceived national integrity
of buyers
country-level
institutional mechanisms
Sellers’ trust in
buyers
Perceived risk of
chargeback fraud
Sellers’ Intention
to trade
H1a, H2a
Buyer verificationProduct typeTrust Propensity
Sellers’ positive
experience
Control variables
H1b, H2b
H3a, H4a
H3b, H4b
H6
H5
H7
Figure 1. The research model

40
marketplace-level
institutional mechanisms
Perceived effectiveness of
feedback mechanism
Perceived effectiveness of
seller protection
Perceived effectiveness of
cross-border delivery
Perceived national integrity
of buyers
country-level
institutional mechanisms
Sellers’ trust in
buyers
Perceived risk of
chargeback fraud
Sellers’ Intention
to trade
H1a, H2a
Buyer verificationProduct typeTrust Propensity
Sellers’ positive
experience
Control variables
H1b, H2b
H3a, H4a
H3b, H4b
H6
-0.149*
(t=2.539)
R2=0.364
R2=0.341
R2=0.527
H7
-0.352***
(t=5.442)
H5
0.323***
(t=4.080)
Figure 2. The research model with empirical results
H1a: 0.001 (t=0.019); H2a: 0.133*(t=2.486); H3a:0.232**(t=2.672); H4a:0.229**(t=3.139)
H1b: -0.257*** (t=4.945); H2b: -0.191**(t=3.127); H3b: -0.052(t=0.921); H4b: -0.167** (t=2.859)
Note: * p<0.05; ** p<0.01; *** p<0.001.

41
Table 1. Descriptive statistics for sample
Gender
Male
82.30%
Female
17.70%
Age
18-24
6.12%
25-34
55.13%
35-44
34.79%
45-54
3.25%
55-64
0.71%
65+yrs
0.00%
Education Level
High school (non-graduate) or below
0.32%
High school graduate or equivalent
4.53%
College diploma graduate or equivalent
14.20%
Bachelor’s degree or equivalent
74.62%
Master’s degree or equivalent
5.77%
Doctoral degree or equivalent
0.56%
The Major Target Markets
USA (36.28%); UK (28.34%); Canada (12.13%); Australia
(11.53%); France (4.86%); Germany (4.75%); Other (2.11%)
Table 2. Item convergent validity measurement
Construct
Composite
Reliability
Cronbach’s
Alpha
AVE
Perceived effectiveness of feedback mechanism(PEFM)
0.886
0.806
0.721
Perceived effectiveness of cross-border delivery (PECDB)
0.896
0.767
0.811
Perceived effectiveness of seller protection (PESP)
0.901
0.834
0.751
Perceived national integrity (PNI)
0.864
0.763
0.680
Past positive experience (PPE)
0.872
0.781
0.695
Trust propensity (TP)
0.877
0.791
0.705
Perceived risk of chargeback fraud (PR)
0.884
0.802
0.717
Seller’s trust in buyer (ST)
0.860
0.753
0.673
Intention to deal with orders (INT)
0.885
0.801
0.721

42
Table 3. Correlations between constructs (square-root of AVE on diagonal).
Construct
PEFM
PECBD
PESP
PNI
PPE
TP
PR
ST
INT
PEFM
0.849
PECBD
0.222
0.900
PESP
0.531
0.345
0.867
PNI
0.458
0.198
0.510
0.824
PPE
0.421
0.316
0.513
0.363
0.833
TP
0.496
0.185
0.449
0.526
0.353
0.839
PR
-0.568
-0.322
-0.588
-0.521
-0.521
-0.364
0.847
ST
0.338
0.398
0.457
0.443
0.448
0.344
-0.496
0.820
INT
0.477
0.412
0.571
0.527
0.414
0.476
-0.512
0.497
0.849
Table 4. Loadings and cross-loadings
ITEM
PEFM
PECBD
PESP
PNI
PPE
TP
PR
ST
INT
PEFM1
0.821
0.225
0.442
0.430
0.347
0.375
-0.479
0.285
0.438
PEFM2
0.870
0.174
0.460
0.359
0.356
0.446
-0.480
0.284
0.382
PEFM3
0.856
0.168
0.450
0.378
0.369
0.440
-0.487
0.292
0.394
PECBD1
0.213
0.897
0.343
0.187
0.299
0.179
-0.292
0.348
0.375
PECBD2
0.188
0.904
0.279
0.170
0.271
0.154
-0.289
0.368
0.366
PESP1
0.500
0.314
0.857
0.450
0.430
0.396
-0.510
0.420
0.546
PESP2
0.453
0.311
0.884
0.430
0.454
0.403
-0.506
0.397
0.475
PESP3
0.427
0.271
0.859
0.445
0.452
0.369
-0.511
0.372
0.461
PNI1
0.362
0.138
0.437
0.855
0.305
0.429
-0.477
0.366
0.422
PNI2
0.372
0.231
0.420
0.862
0.347
0.439
-0.415
0.422
0.466
PNI3
0.407
0.114
0.406
0.751
0.241
0.439
-0.395
0.301
0.417
PPE1
0.371
0.255
0.433
0.317
0.850
0.315
-0.436
0.384
0.339
PPE2
0.363
0.218
0.452
0.256
0.825
0.256
-0.417
0.318
0.287
PPE3
0.322
0.311
0.402
0.330
0.825
0.306
-0.448
0.411
0.401
TP1
0.414
0.138
0.376
0.416
0.323
0.833
-0.372
0.302
0.415
TP2
0.410
0.163
0.356
0.434
0.270
0.846
-0.264
0.278
0.367
TP3
0.424
0.166
0.399
0.477
0.293
0.839
-0.276
0.284
0.415
PR1
-0.477
-0.257
-0.491
-0.431
-0.442
-0.322
0.847
-0.400
-0.456
PR2
-0.463
-0.280
-0.462
-0.448
-0.441
-0.271
0.846
-0.416
-0.423
PR3
-0.501
-0.282
-0.538
-0.445
-0.442
-0.330
0.846
-0.443
-0.421
ST1
0.287
0.339
0.385
0.367
0.352
0.296
-0.449
0.869
0.427
ST2
0.338
0.286
0.438
0.409
0.428
0.361
-0.398
0.727
0.400
ST3
0.193
0.353
0.286
0.302
0.311
0.173
-0.361
0.857
0.389
INT1
0.408
0.242
0.435
0.400
0.368
0.354
-0.444
0.392
0.734
INT2
0.377
0.426
0.497
0.448
0.300
0.405
-0.386
0.428
0.890
INT3
0.423
0.379
0.517
0.487
0.379
0.446
-0.466
0.442
0.912

43
Table 5. Heterotrait-monotrait ratio (HTMT)
PEFM
PECBD
PESP
PNI
PPE
TP
INT
PR
PEFM
PECBD
0.283
PESP
0.647
0.431
PNI
0.589
0.256
0.640
PPE
0.532
0.406
0.638
0.465
TP
0.620
0.238
0.552
0.681
0.445
INT
0.593
0.526
0.698
0.675
0.518
0.596
PR
0.706
0.411
0.717
0.666
0.657
0.454
0.637
ST
0.428
0.524
0.570
0.577
0.576
0.438
0.638
0.634
Table 6: Summary of the tests of mediating effects
Total Effect of IV on DV
Direct Effect of IV on DV
Indirect Effects
Coefficient
T value
Coefficient
t-value
Point
Estimate
BC 95% CI
Lower
Upper
Model 1: PNI as the IV
0.272
6.477
0.219
5.030
Total
0.053
0.009
0.124
Mediators
ST
0.040
0.008
0.101
PR
0.013
-0.014
0.047
Contrast
ST vs. PR
0.027
-0.015
0.095
Model 2: PEFM as the IV
0.158
3.707
0.134
3.033
Total
0.024
-0.015
0.064
Mediators
ST
0.008
-0.004
0.029
PR
0.016
-0.021
0.054
Contrast
ST vs. PR
-0.08
-0.049
0.034
Model 3: PECBD as the IV
0.230
6.179
0.183
4.770
Total
0.047
0.006
0.119
ST
0.040
0.004
0.113
PR
0.007
-0.005
0.038
Contrast
ST vs. PR
0.033
-0.005
0.112
Model 4: PESP as the IV
0.269
5.923
0.222
4.724
Total
0. 047
0.006
0.099
ST
0.031
0.007
0.078
PR
0.016
-0.017
0.059
Contrast
ST vs. PR
0.015
-0.035
0.074
IV: independent variable, DV: dependent variable, BC: Bias-Corrected Bootstrap
PNI: Perceived national integrity, PEFM: Perceived effectiveness of feedback mechanism, PEDBI: Perceived
effectiveness of seller protection, PESP: Perceived effectiveness of seller protection

44
APPENDIX. SURVEY ITEMS.
Sellers’ intention to trade (McKnight et al. 2002).
INT1. Given the chance, I predict that I would consider selling products to buyers in DHgate.com in the
future.
INT2. It is likely that I will sell products to buyers in DHgate.com in the near future.
INT3. Given the opportunity, I intend to sell products to buyers in DHgate.com.
Trust propensity (McKnight et al. 2002; Gefen 2000).
TP1. Most Internet buyers are reliable.
TP2. Most Internet buyers keep promises and commitments.
TP3. Most Internet buyers are honest.
Trust in buyers (Ba and Pavlou 2002; Doney and Cannon 1997; Gefen 2000; McKnight et al. 2002).
ST1. Buyers on DHgate.com are in general reliable.
ST2. Buyers on DHgate.com are in general honest.
ST3. Buyers on DHgate.com are in general trustworthy.
Perceived risk of chargeback fraud (Jarvenpaa et al. 2000; Pavlou and Gefen 2004).
PR1. There is a considerable chargeback fraud risk involved in selling goods to DHgate buyers.
PR2. There is a high potential for chargeback fraud involved in selling goods to DHgate buyers.
PR3. My decision to sell goods to DHgate buyers is risky owing to the high potential for chargeback
fraud.
Perceived effectiveness of feedback mechanism (Pavlou and Gefen 2004).
PDBI1. I feel confident that DHgate’s rating and feedback mechanism gives accurate information about
buyers’ credit.
PDBI2. A considerable amount of useful information about the transaction history of buyers is available
via DHgate’s transaction record mechanism.
PDBI3. I believe that the transaction record mechanism in DHgate is helpful.
Perceived national integrity (McKnight et al. 2002; Koh et al. 2012; Morgan and Shelby 1994).
To what extent do you agree or disagree with the following statements (where X represents the country of
residence for the majority of your customers):
PNI1. Buyers from country X generally behave with integrity.
PNI2. Most buyers from country X are honest in their dealings with others.

45
PNI3. In general, most buyers from country X keep their promises.
Perceived effectiveness of seller protection (Pavlou and Gefen 2004).
PESP1. I believe DHgate.com will protect me in case of problematic transactions with buyers as long as I
comply with its seller protection program.
PESP3. I am confident that receiving credit card payments is safe in case of disputed purchases from
buyers on DHgate.com as long as I comply with its seller protection program.
PESP3. I believe DHgate.com protects me from losing my money to claims and chargebacks resulting
from buyer complaints.
Past positive experience (Pavlou and Gefen 2004).
PPE1. My sales experience on DHgate.com is positive.
PPE2. I feel satisfaction about my past sales experience on DHgate.com.
PPE3. Regarding past sales experience, I am very happy with using DHgate.com for selling.
Perceived effectiveness of cross-border delivery (Kok et al. 2012; Doney and Cannon 1997).
To what extent do you agree or disagree with the following statements (where X represents the country of
residence for the majority of your customers):
PECBD1. I believe that shipping goods from China to X is effective.
PECBD2. I believe that shipping goods from China to X is reliable.