Risk, Convenience, Cost and Online Payment Choice:
A Study of eBay Transactions
Haizheng Li*
School of Economics
Georgia Institute of Technology
Atlanta, GA 30332-0615
Haizheng.li@econ.gatech.edu
Richard Ward
DuPree College of Management
Georgia Institute of Technology
Atlanta, GA 30332
Dick.ward@mgt.gatech.edu
Han Zhang
DuPree College of Management
Georgia Institute of Technology
Atlanta, GA 30332
Han.zhang@mgt.gatech.edu
_____________________
* The corresponding author. The research was supported in part by the iXL Electronic Commerce Center of DuPree
College of Management, Georgia Institute of Technology. We are grateful to eBay traders who participated in our
survey, and to Isaac Springfield, Haibo Huang and Lynn Yang for their helpful comments.
Abstract
Online payments are a critical issue in electronic markets. This research investigates
online payment choices using probit and nested logit model based on the survey data we
collected from eBay users. We develop a theoretical framework to model payment choices
between the trader partners based on risk, convenience and cost dimensions. Then, we analyze
how product attributes, traders’ characteristics and payment attributes affect the payment choice.
Our findings suggest that the cost and inconvenience associated with a payment method
discourages its use in online transactions. Product attributes, especially uncertainties associated
with the product quality, appear to have stronger effect in affecting payment choices than traders'
characteristics. We also find that a seller’s reputation rating does not have a significant effect on
actual payment choices, but affects the payment options offered by a seller.
J.E.L Code:
L14, L86
Key Words:
Payment Method, Online Auction, eBay, Nested Logit Model
1. Introduction
As an efficient and flexible sales channel, online auction businesses are becoming an
internationally successful phenomenon. Individuals use auction sites as a market to conduct
online “garage sales”; companies use auction sites to liquidate unwanted inventory, as well as to
assist in pricing new products, acquiring new markets for low-margin items, and reaching
markets that would otherwise be too expensive to reach.
Presently, eBay, Yahoo!, and Amazon.com are the major players in online auction
markets. According to eBay, its site has over 49 million registered users. Forrester Research
projects that online consumer marketplaces will conduct over 25% of all online sales by 2006.1
Recently, Forrester Research changed its definition of online retail to include auctions, because
survey respondents make 10 percent of their purchases on eBay.2
As a radically different channel for consumer purchases, online auction markets have
drawn significant attention from the research community. Concentrating on the asymmetric
information problem (Akerloff 1970) in electronic markets, much literature on online auctions
addresses trust issues (e.g., Kollock 1999, Brynjolfsson and Smith 2000, Ba et al. 2003).
Particularly, considerable attention has been devoted to the effects of reputation systems on
online auctions. For example, Resnick and Zeckhauser (2001), using a large data set from eBay,
have found that sellers with better reputations were more likely to sell their items, but they
enjoyed no boost in price. Melnik and Alm (2002) studied the effects of seller’s reputations on
the willingness of buyers to bid on their items, and find that reputations have a small, but
positive and statistically significant impact on price. Ba and Pavlou (2002) studied the extent to
1
Forrester Research 2001, “SMEs Present Latent Opportunity For Europe's Online Auctions, Says
Forrester”, http://www.forrester.com/ER/Press/Release/0,1769,498,00.html ,February 13
2
CNET 2002, Sept. 4, “Channel Advisor Announces First Annual Channel Advisor Strategy Summit”,
http://news.cnet.com/investor/news/newsitem/0-9900-1028-20363930-0.html
which trust can be induced by proper feedback mechanisms in electronic markets and find that
feedback systems can generate price premiums for reputable sellers.3
Yet, one critical area in online auction markets has remained unexplored: how do trading
partners select a payment method for settling their transactions? Payment systems are an
essential part of electronic commerce. Although common media of exchange are used in
electronic markets, the spatial distances between buyers and sellers in electronic markets
increase their anxieties about the successful completion of their transactions. According to the
Internet Fraud Watch, operated by the National Consumers League, online auction sales have
remained the number one source for Internet fraud in the past years. In 2001, 70% of the fraud
cases reported to the Internet Fraud Watch were online auction related. The average loss per
person in online auction fraud rose from $326 in 2000 to $411 in 2001.4
Currently, there are different payment methods available in online auction markets, such
as personal check, money order, cashier’s check, credit card and so on. These payment methods
differ in risk, convenience and cost. But what factors cause online traders to choose a particular
payment method? Does the seller’s reputation affect payment preference? This study attempts
to fill the research gap in the literature and to address these questions.
The determinants of payment choices will have important implications for e-commerce.
First of all, to facilitate the development of electronic markets, it is vital to have an efficient
online payment system. Alan Greenspan, Chairman of the Federal Reserve, commented that
“payment systems are critical to the functioning of a modern monetary economy” (Greenspan
1996, p. 695). Understanding the choice of payment transactions between online traders will
3
Similar results can be found from Lucking-Reiley et al. (1999), Brynjolfsson and Smith (2000), and Houser and
Wooders (2001).
4
Internet Fraud Watch. 2002. Available: http://www.fraud.org/internet/2001stats10mnt.htm.
2
contribute to designing such an efficient system. Secondly, for online payment processing
agencies, knowing the factors that affect payment choices will help them to meet the needs of
online traders and to form strategies that provide them greater shares of their competitive market.
Using the survey data we collected from eBay users, we study the choice of payment
methods in online transactions. We first develop a simple framework to model seller and buyer’s
behavior in terms of balancing risk, inconvenience and costs associated with each payment
method. Then we apply probit and nested logit model to analyze the factors that affect the
probability of choosing a particular payment method.
The paper is organized as follows. Section 2 discusses risk, convenience and cost related
to payment methods. Section 3 presents a theoretical analysis of payment method selection in
online auction markets. Section 4 discusses the survey. Section 5 and 6 discuss the results from
probit and nested logit models. Section 7 concludes the paper.
2. Risk, Convenience, and Cost of Online Payment
It appears that risk, convenience, and cost are among the most important issues in online
payment transactions. Abrazhevich (2002) conducted a survey of user attitudes towards
electronic payment systems, and found that ease of use, convertibility of funds, security and trust
are among the most important features. Carow and Staten (1999) investigated consumer
preferences among debit cards, credit cards, and cash for gasoline purchases and found that
convenience was the greatest determinant for using credit cards. Mantel (2000) also found that
low cost, convenience, privacy and security are among the key factors affecting consumers’
payment decisions. Notably, in the G10 working report on electronic money, developing low-
3
risk, low-cost, and convenient payment and financial services for consumers and businesses is
listed as a major objective for these countries’ banking and financial authorities.5
In general, online payment methods differ in terms of risk, convenience, and cost. Most
of the risk associated with online transactions arises from the physical separation between buyers
and sellers. Therefore, asymmetric information problems exist between the trading partners.
First, sellers and buyers cannot see each other, and they know each other not by real
identification, but by virtual ID. Furthermore, online auction sites usually excuse themselves
from the responsibility of user authentication. For example, eBay’s User Agreement states:
“Because user authentication on the Internet is difficult, eBay cannot and does not confirm each
user's purported identity.”
Second, in electronic markets, buyers cannot closely examine the product before
purchase. When bidders view a product listed at an online auction site, their experience of the
product’s quality is constrained by the limitations of the electronic medium. Online auction sites
often claim that they “have no control over the quality, safety or legality of the items advertised,
the truth or accuracy of the listings,” thus exposing bidders to potential risks and fraudulent
transactions.6
The risk in online transaction is also affected by the payment method itself. As
summarized in Table 1, for buyers, credit cards generally provide more protection than cashequivalents (e.g., cash, money order, cashier’s check, etc.), because under the Fair Credit Billing
Act, buyers have the right to withhold payment on poor-quality or damaged merchandise
5
“Report of the Working Party on Electronic Money”, Source: Paper version from U.S. Department of the Treasury,
Public Affairs. Electronic version: http://www.bis.org/publ/gten01.pdf. The G10 countries are: France, Germany,
Italy, Switzerland, the Netherlands, Belgium, Sweden, the UK, the US, Canada and Japan. The G10 grouping of
countries have set up various committees, including the Committee on Payment and Settlement Systems, which
report to, the G10 central bank Governors. The G10 established the famed Basel Capital Accord, which sets
standardized capital requirements for G10 banks, often regarded as the cornerstone of the global financial system.
6
Directly quoted from eBay’s User Agreement: http://pages.ebay.com/help/basics/f-agreement.html.
4
purchased with a credit card.7 However, not all consumers like to use credit cards online due to
various security concerns (Ba et al. 2000). For instance, buyers are afraid that their credit card
information may be intercepted by a malicious party during transmission over the Internet, or
that the sellers may misuse their credit card information.
From the seller’s point of view, however, cash-equivalents are more secure for on-line
transactions. If a buyer uses a stolen credit card number, the seller will suffer a loss, because the
seller will have to refund the sales price, in addition to losing the product.8
Besides the transaction security, trading partners are also concerned about transaction
convenience. Convenience is becoming an increasingly important issue in modern society. In
fact, “Every day, people are adopting new technologies that trade a small amount of their privacy
for greater convenience”.9 A major function of Internet business is to provide convenience in
information gathering and order processing (Kim et al. 2002). And shopping convenience plays
an important role in the success of Internet commerce (Torkzadeh and Dhillon 2002).
Payment methods have various levels of convenience. For example, a credit card is more
convenient for the buyer than a money order or a cashier’s check. A credit card can be
electronically processed, but money orders and cashier’s checks first must be obtained from an
issuer and then mailed.10 Moreover, the buyer has to wait longer to receive the product. For the
seller, credit cards are also more convenient, since the buyer’s payments are automatically
collected and forwarded to the seller’s bank quickly via electronic transmission. In addition, the
7
People usually think that payment method’s float is an important feature to protect buyers. Float is defined as the
point from reception of the payment by the seller until the point, where funds are withdrawn from the buyer's bank
account. In this research, we do not treat it as “protection” if a buyer just changes his/her mind during the float
duration before receiving the product.
8
In general, in online transactions, sellers can ship the product after receiving the fund. Thus, sellers are exposed to
less risk with non-credit card payments.
9
The Economist (2002), "The telephone is the tool – Digital cash schemes favor the mobile phone,” London, April.
10
Western Union offers online money order service through www.Bidpay.com. In this case, the buyer does not
need to take a trip to buy a money order. However, the money order still needs to be mailed physically to the seller.
5
seller receives some money management services, such as account detail and summary
information. For sellers with high selling volume, such features are particularly helpful.
In addition to risk and convenience, payment methods are also associated with costs.
Such costs are referred to as transaction fees. As listed in Table 1, either the seller or the buyer
pays for the transaction fees depending upon the payment method. For example, a money order
fee is charged to a buyer by an issuer; but a credit card transaction fee is charged to a seller by a
credit card merchant services company like PayPal.
In the event of an unsatisfactory product, the buyer requests a refund from the seller and
ships the product back to the seller. The original transaction fees may not be recovered and new
transaction fees for the reverse payment may be expended. For example, for credit cards, these
reverse transaction fees, called chargeback fees, are from $10 to $20. Additionally, if a seller’s
cumulative chargeback fees pass a certain threshold, the seller’s merchant services provider will
also increase the seller’s future transaction fees. As discussed in some literature, fraud costs to
online merchants include: chargeback fees, higher transaction fees, and fines imposed by credit
card associations.11
Therefore, payment methods for online transactions differ in risk, convenience, and cost.
As transactions vary, different payment methods become more or less attractive to buyers or
sellers. In the next section, we will develop a simple model for payment choices.
3. A Simple Model
Since online payment methods differ from each other, buyers and sellers may each have
different preferences. Therefore, the seller and buyer jointly decide on a payment choice.
11
Lee, Mie-Yun, “ Low-cost measures can prevent high-cost online fraud,” Bizjournals, November 20, 2000.
http://www.bizjournals.com/extraedge/consultants/savvy_business_shopper/2000/11/20/column216.html.
6
Presumably, if they find a mutually acceptable payment method, the transaction will proceed, or
the online sale will fail. We define a complete transaction as one where the buyer receives and
does not return the product and the seller receives payment in full amount. If a transaction is
completed, the seller gains profits from the sale and the buyer gains utility from the product.12
For the seller, assume that the potential gross profit for the sale is a function of the selling
price p and is defined as f(p). Besides price, the gross profit also depends on the cost the seller
paid of acquiring the product.13 During the transaction, the seller incurs certain sunk cost Co
associated with selling the product, such as the listing fee to an online auction agency and
advertisement cost (like photos). In addition, the seller incurs payment transaction costs. If
payment method j is used for the money transaction, the transaction costs for the seller include
direct cost Cdjs and the implicit cost Cej. The direct cost is the fee paid for using a particular
payment service, such as the fee charged by a credit card company. Implicit cost is the indirect
cost resulting from the inconvenience associated with using the payment method j. For example,
if paid by personal check, the seller will experience a delay before receiving the payment and
will have to physically deposit the check. Therefore, the sellers' potential net profit after the
transaction becomes:
Yj =f(p) - Co - Cdjs - Cej.
However, it is possible that the product may be returned by the buyer or the buyer may
use a fraudulent form of payment. If the probability that the product will be returned by the
buyer is Prj, and the seller’s direct cost for refunding is Crj (for example, the charge back fee for
a credit card), the seller’s total loss when the product is returned is (Co + Cdjs + Cej + Crj).14
12
To focus on payment method, we ignore the shipment issue.
For example, on eBay, the three basic listing fees are insertion fees, final value fees and listing upgrade fees.
14
Some fee components may be waived, for example, with PayPal, if the refund is through PayPal system, the seller
can re-capture Cdj and escape Crj.
13
7
Finally, assume the probability of buyer fraud is Pfjb (for example, when the buyer pays
for the product using a stolen credit card number). In this case, the total loss for the seller is (p +
Co + Cdjs + Cej).15
Therefore, for the seller, the expected net profit associated with the transaction for using
payment j is (the probability of return and fraud may depend on the payment method adopted.):
πj= [f(p) - Co - Cdjs - Cej]⋅(1-Prj-Pfjb) - (Co +Cdjs + Cej + Crj)⋅Prj - (p + Co + Cdjs + Cej)⋅Pfjb.
The seller will choose payment option j for the transaction if
πj= max{πs1 , πs2 …πsk}.
On the other hand, if the seller and buyer cannot agree on payment method, the
transaction will fail, and the loss for the seller will be the sunk cost Co. Thus, the seller has an
incentive to make the transaction if the expected profit is higher than the seller’s sunk cost, i.e.,
πj > - Co.
From the buyer’s perspective, he or she will gain a certain level of utility from consuming
the product. The utility depends on the price p and ∂u⁄∂p<0. The utility also depends on the
payment method j, including: 1) the direct cost for a payment method, Cdjb (e.g., fees for a
money order); 2) the extent of inconvenience for the payment method, Vj (for example, if using
a money order, the buyer must spend time to purchase it and send it to the seller, and wait for the
product); and 3) the risk associated with the payment method, Rj (for example, risks associated
with giving out credit card information).
Therefore, if the transaction succeeds via payment method j, the buyer’s utility is utj =
ub(p)-u(Cdjb, Vtj, Rtj), where ub(p) is the buyer’s utility from consuming the product, Vti and Rtj
15
We assume that the seller measures the loss from losing the product based on the true opportunity cost P, i.e., the
market value of the product realized in the selling process.
8
can be viewed as the respective inconvenience and risk concerns experienced by the buyer,
thereby lowering the total utility.
If the product is returned to the seller, the buyer does not consume the product and the
utility becomes urj = -u(Cdjb, Vtj+Vrj, Rtj), where Vrj is the additional buyer inconvenience
associated with waiting for and cashing a refund.
In the case of seller fraud, the seller does not ship the product after receiving the money.
The buyer’s utility becomes ufj=-u(p+Cdjb, Vtj, Rtj), where the money loss is specified as
equivalent to an increase in direct payment cost,
Assume that the probability of returning the product is Prj, and the probability of seller
fraud is Pfjs. Then, the expected utility for the buyer is
Uj= utj⋅(1-Prj -Pfjs)+urj⋅Prj+ufj⋅Pfjs.
Thus, among the k different payment options, the buyer will choose j if
Uj= max{U1 , U2 …Uk}.
We assume that the buyer’s utility is zero if the transaction stops at this stage.16 Thus the
buyer would accept a payment option j, if Uj > 0.
Although the buyer and the seller can accept any payment option j as long as πj> - Co and
Uj>0, the actual payment method chosen depends on the bargaining between the seller and the
buyer. Among k different payment options, define Minπ as the minimum expected profit for the
seller and MinU as the minimum expected utility for the buyer, thus the seller’s surplus of
choosing payment j can be defined as ∆ξj=(πj - Minπ), and the buyer’s surplus of choosing
payment j can be defined as ∆φj=g(Uj - MinU), where g is a function that converts utility
measure into money measure.
16
This assumption implies that there is no disutility associated with the searching process for the buyer.
9
Assume that the relative bargaining power for the seller is represented by λ and λЄ[0,1],
and the bargaining power for the buyer is (1-λ). The payment choice should be determined by
the weighted total surplus between the seller and the buyer. Thus, assuming a unique maximum
exists, the payment option l will be adopted if the weighted surplus
Tl=Max{(1-λ)·∆φj+λ·∆ξj, j=1, 2, …k}.
If the seller completely dominates the bargaining, λ=1, the payment method used will be
the best for the seller. If the seller has no bargaining power, λ=0, the buyer will decide the
payment method. In general case, both the buyer and the seller will influence the payment
choice. If the best option for the buyer and the seller happens to be the same, it will certainly be
adopted for the transaction.
Therefore, based on the simple model, the seller’s expected profit and the buyer’s
expected utility is affected by the risk, inconvenience and cost associated with each payment
method. Yet, the perceived risk, inconvenience and actual cost also depend on the attributes of
the product in the transaction and the characteristics of the traders. Therefore, the weighted
surplus for the trader partners Tj will be a function of product attributes Xp, the characteristics of
the trader partners Xt, and the features of a particular payment method Xm. For transaction i via
payment method j, the weighted surplus can be represented by:
Tij=h(Xpij, Xtij, Xmij) + εij, j=1,2,…,k,
where εij is the random error. Thus, Tij is the latent variable that determines the observed choice
of the payment method in a transaction. This equation forms a basis for our empirical analysis.
In general, seller’s and buyer’s characteristics include experience in online transactions,
reputation ratings, and even age and education. Product attributes include product price and
uncertainties associated with the product quality. Specifically, product price may affect the
10
perceived risk and the actual costs for a payment transaction. Moreover, the higher the
uncertainties associated with the product quality, the higher the risk involved. On the Internet, it
is easy to judge the quality of commodity products (e.g., oil, paper clips), but difficult to judge
“look and feel” products (e.g., suits, art) (De Figueiredo 2000), because they differ in non-digital
attributes, which can only be evaluated by perceiving them in person (Lal and Sarvary 1999).
When purchasing online, buyers generally have more confidence in the quality of commodity
products, but relatively less confidence in the quality of “look and feel” products due to
information asymmetry. In addition, product uncertainties will also affect the perceived
probability of returning the product. Therefore, product attributes, such as whether under
warranty, whether used or not, generally provide additional information to reduce the
uncertainty.
4. Survey and Data
In order to investigate the payment choice empirically, we conducted a survey of eBay
traders. eBay is the major player in the online auction market, with 85 percent market share.17
eBay makes its money by charging sellers fees for listing their items, plus commissions based on
a percentage of an item’s selling price. After an auction closes, the winning bidder and the seller
are responsible for completing the sale.
With the rapid development of online auctions, many online payment processing systems
have emerged. For example, PayPal (www.paypal.com) is a trusted third party enabling
consumers to send and receive payment online. Escrow (www.escrow.com) is emerging as
another type of trusted third party, which provides online escrow service for transferring
17
Cisneros, O.S., 2000, “eBay Accused of Monopolization”, WiredNews. Available:
http://www.wired.com/news/business/0,1367,37871,00.html, July 31.
11
products and payments between online sellers and buyers.18 eBay also launched its own online
payment system, called Billpoint.19 In addition, some sellers have their own credit card
processing capability to take payments directly. Thus, on eBay, bidders and sellers may choose
the following different payment methods: cash, personal check, money order, cashier’s check,
credit card (via PayPal, eBay, or seller’s own facility), debit card, electronic check and escrow
services.
Ideally, to study the factors that affect payment method selection, the questionnaire
should be designed for a particular buyer and seller pair involved in an online transaction.
However, it is practically impossible to contact pairs of buyers and sellers. Therefore, the
questionnaire was designed to solely target the eBay sellers.
Based on the simple model, we focused on the effect of payment features, product
attributes, and traders’ characteristics. The questionnaire was composed of the following
sections: 1) eBay experience, 2) Last eBay sales transaction, including various product attributes,
and 3) personal demographic information. The questionnaire was sent to five eBay “Assistance
Traders” and five student eBay sellers for pretest.20 Based on the feedback received, revisions
were made to the questionnaire. The questionnaire was then encoded into a web page using
Microsoft’s Active Server Pages web interface technology, and a Microsoft Access database was
connected to the web page.
18
Escrow service works as follows: First, the escrow company collects payment for the merchandise from the buyer.
When the payment clears, the seller is notified to ship the item. Second, the buyer notifies the escrow company
when the merchandise is received and is satisfactory. Finally, the escrow company will then release the payment to
the seller.
19
eBay announced the completion of its acquisition of PayPal in October 2002. Billpoint remained a payment
option on eBay through the end of 2002 and will begin to be phased out in early 2003. When we did the survey,
Paypal was an independent company.
20
On eBay, “Assistance Traders” are experienced sellers and volunteers to help other people in online transactions.
Their phone numbers, email addresses and eBay usernames are available online.
12
The original research plan attempted to follow Ba and Pavlou’s (2002) survey
methodology. First, we randomly selected sellers on eBay based on different types of products;
second, we sent a message, which contained the questionnaire’s URL, to the selected sellers
through eBay’s email system, and invited them to participate in our survey. Unfortunately, after
we sent out about 20 emails to the selected sellers in March 2002, we received a warning
message from eBay, indicating that we were violating an eBay rule by sending a URL through
eBay’s email system.
Therefore, we turned to post our questionnaire on discussion forums on eBay and related
auction sites, such as Auctionwatch (www.auctionwatch.com) and Auction Guild
(www.auctionguild.com). We did not offer a monetary reward for participation in the survey,
but we promised to make our research outcome available online. Fortunately, this method of
data collection was very satisfactory and many respondents were quite enthusiastic and even
offered many good comments, suggestions and questions about this research.
When we first posted the survey online, we asked the respondents to provide their eBay
ID, which would help us obtain the sellers’ reputation ratings on the eBay website (eBay posts its
members’ reputation ratings). Unlike the pretest, many respondents did not want to reveal their
eBay ID, even though we promised that their answers were completely confidential. Therefore,
we had to add one question about the seller’s eBay reputation rating (total number or ratings and
total number of positive ratings). Still, some respondents did not answer this question.
A total of 260 unique eBay Sellers completed the survey online.21 The descriptive
statistics are summarized in Table 2. Among sellers, the average age is 41 years old, average
education is 15 years, and 52% sellers are female. The average eBay selling experience is 3.8
21
Regarding the randomness of the sample here, we do not believe that those who choose to respond are correlated
with their preferences on payment methods. Thus, we do not see a sample selectivity problem here.
13
years. Only 2% sellers do not have any buying experience on eBay. Their average number of
monthly transactions for the past six months is 124, and the average monthly sale is $2,193.
Among the 260 respondents, 110 respondents reported their eBay reputation ratings.22 The
average total reputation rating is 447, and the average positive reputation rating is 439.
For the last product sold by the seller (the most recent transaction) on eBay, 33% are
under warranty, 57% are used products, and the average price for the products was $130 with a
standard deviation of $420. In addition, in order to measure the “look and feel” nature of the
product in the transaction, we include a question asking the seller to estimate the buyer's desire to
physically examine the product before purchasing on a 5-to-1 scale (high-to-low desires). The
average scale is 1.8.
It appears that 72% of payments are rendered using credit cards, the majority of which
being rendered via PayPal (61.5%).23 Money orders account for 12.7% of transactions, while
personal checks account for 10.8%. Three questions are asked on the payment method(s)
initially proposed by the seller, the buyer, and the actual method adopted in the transaction.
Detailed information on payment choice is summarized in Table 3.
5. Probit Analysis
In our sample, since escrow service is not used, all payment choices can be naturally
grouped into two categories: cash equivalents (cash, money order, cashier’s check, personal
check) or credit cards (via eBay or via Paypal or via seller processing). The common
22
It is possible that those with higher reputation ratings are more likely to report them. Yet the smaller sample that
contains information on reputation does not pose the sample selection problem in the regression analyses in the
following sections, given that reputation rating is exogenous to payment choices and that there is always some
chance of not observing any reputation ratings, see Wooldridge (2002, Chapter 17) for the discussion.
23
For comparison, a Gartner report states that credit cards account for 93% of online purchase payments
(CommerceNet Newsletter, “The Public Policy Report,” Vol. 3, No.5 May 2001). Note that Gartner report is not
limited to online auction.
14
characteristic is that cash equivalents must be physically mailed from the buyer to the seller,
while comparable credit/debit card transfers are electronic. Credit cards are generally more
convenient for both buyers and sellers.
We apply the bivariate probit model to investigate the choice between these two broad
categories.24 The advantage of the probit analysis is that the effects of product attributes and
traders’ characteristics on payment choices can be easily investigated. In a more complicated
model such as nested logit, discussed in the next section, it becomes necessary to interact these
variables with choice dummies in order to identify their effects. Thus, it greatly increases the
number of parameters to be estimated, and in addition, only relative effects can be identified (i.e.,
by normalizing one choice specific parameter to zero.)
According to the simple model in Section 3, the weighted surplus between the seller and
the buyer determines the actual payment choice, and thus can be viewed as the underlying latent
variable in the probit model.25 In the model, sellers’ characteristics include average number of
transactions per month, years of eBay experience, reputation rating, gender, age, and education;
and product attributes include price, whether the product is used or new, whether still under
warranty, and its degree of being a look and feel product.
Table 4 reports estimated coefficient and marginal effects for different models: Model 1
(seller characteristics only), Model 2 (product attributes only), Model 3 (both), and Model 4
(with seller’s reputation rating). Marginal effect is evaluated at the sample average of the
24
Because sellers usually cannot tell if buyers use credit card or debit card if the sellers get the payment through
PayPal or eBay payment system, we cannot separate credit card from debit card. However, they are equivalent in
terms of convenience and cost. As for protection, based on Federal Reserve Bank of Philadelphia, using credit card
is protected up to a point by provisions of Regulation Z, which incorporates the provisions of the Fair Credit Billing
Act. For debit card, certain provisions of Regulation E provide some protection. This protection is not as extensive
as that provided by Regulation Z, but many debit card issuers voluntarily provide protection similar to that provided
by Regulation Z for credit card purchases. Thus, the difference between the two is small.
25
Based on the payment selection model, the seller and the buyer can choose not to transact if they cannot agree on
payment methods. Since such data are not available, our model here excludes this possibility.
15
independent variables. Log-likelihood value, likelihood ratio test, and Pseudo R-squared are
reported with the results. The Likelihood Ratio tests show that each model is highly significant,
with P-values of Chi-square statistics less than 1%. Based on the log-likelihood values, Model 3
fits the best because it controls for both seller’s characteristics and product attributes. Since
these two groups of attributes are not correlated with each other, as expected, the results from
Model 3 are almost identical to Model 1 and 2 (minor changes in significance are caused by
multicollinearity and reduced degree of freedom).
Among seller characteristics, the monthly volume of transactions is significant at the 5%
level (Model 1). If the number of transactions increases by 100, the chance of selecting a credit
card increases 4.6 percentage points. In general, a seller should prefer cash equivalents given
their low costs and high safety nature. However, in terms of convenience, credit cards certainly
have advantages over payment collecting and record-keeping. Holding all else constant, if sales
volumes increase, the costs of inconvenience associated with payments increase, and expected
profits are lowered. Therefore, sellers with larger sale volumes prefer a credit card.
The effect of seller's type is almost significant at the 10% level. If a seller has never
bought anything through eBay, he or she is more likely to push for a cash-equivalent. For a
buyer, credit cards are generally preferable because of their protections, convenience, and low
cost relative to cash-equivalents. Thus, it is possible that the buying experience of a seller helps
him or her to understand the buyer’s preference for credit cards, and thus increases the seller’s
willingness to accept credit cards. On the other hand, years of selling experience do not appear
to affect payment choices.
Age and education are not significant at the 10% level. Their signs indicate a negative
effect on the probability of choosing a credit card. The same effect is found for gender. Based
16
on the sign, perhaps, older sellers and female sellers are relatively more conservative and risk
averse; educated sellers are generally aware of credit card risk better. Thus, their expected profit
of using credit card is lower, and they prefer cash-equivalents.
Among product attributes, product warranty is highly significant in affecting payment
(Model 2). If a product is still under warranty, the probability of choosing a credit card increases
17.8 percentage points. On the other hand, the higher the degree of the look and feel nature of
the product being sold, the more likely that a cash-equivalent method of payment will be chosen
(almost significant at 10% level). Used products, compared to new products, also increase the
probability of choosing a cash-equivalent payment (although statistically insignificant).
These results are consistent with the model in Section 3. Unwarranted products, used
products, and products possessing a high look and feel nature all increase product uncertainty.
Thus, the buyer’s probability of returning the product will be higher. In this case, a credit card
leads to additional chargeback costs for the seller and also makes the return of the product easier
for the buyer, thereby reducing the seller’s expected profit. A strategic seller thus will push for a
cash-equivalent. When product uncertainty is reduced because of a warranty, a new product or
low-degree look and feel nature, the seller becomes more willing to use credit cards, and thus
increase credit card use in actual transactions. Based on this result, it appears that the seller has
more bargaining power in deciding the payment method. Interestingly, the price does not have a
significant effect.26 This is probably due to a conflict of interest between buyer and seller.
Specifically, from the seller’s perspective, the higher the price, the higher the credit card
transaction fee and the higher the risk associated with credit card fraud. Thus, cash-equivalents
26
Since the buyer first bids on price and then negotiates payment method with the seller, price should not be
endogenous. To check this possibility, we estimate model 2 and 3 without price. The results are almost the same
for variables included.
17
are preferable. From the buyer perspective, the higher the price, the higher the risk associated
with the seller fraud, such as non-delivery.27 Therefore, credit cards are preferable.28
Since several studies find that reputation ratings have important implications for online
trading, it is interesting to investigate whether reputation ratings have any effects on payment
choices. Model 4 (Table 4) includes reputation rating as an additional seller’s characteristic.
Because the average percentage of positive ratings is very high (above 95%), we use a dummy
variable indicating whether a seller has at lease one negative reputation rating.29 However, the
effect of reputation turns out to be not statistically significant. Most likely, a buyer is concerned
most about a seller’s negative rating, and thus pushes for using a credit card, in order to have
more protection and easier product return capability; but the seller with a negative rating may
push for a cash-equivalent for self-protection. Thus, the net effect becomes ambiguous.
On the other hand, since the actual payment method used is determined jointly by the
seller and the buyer, it is desirable to investigate whether a seller would have behaved differently
if he or she can dictate the payment choice. This can also provide some insight on the payment
negotiation procedure. Therefore, we use probit model to study whether the seller offered a
credit card option. In this model, the underlying latent variable for the probit model is just the
seller’s surplus. The results are reported in Table 5.
Similarly, based on Model 1 in Table 5, the seller transaction volume is significant at the
10% level in increasing the chance of the seller’s extending a credit card option. Gender and
education variables change signs, but they are not significant. Statistically, the seller’s type (i.e.,
27
It is possible that price is correlated with some product attributes such as warranty. Yet, this is merely a
multicollinearity problem.
28
Credit card processors use cumulative transaction limits or single transaction limits to minimize risk exposure.
For example, PayPal requires information on a buyer’s bank account, if the buyer’s cumulative purchases reach a
$2000 limit. Since the average price is much lower than the limits, the effect, if any, should be negligible.
29
In alternative specifications, we used percentage of positive rating, percentage of negative rating, and number of
negative rating. The results do not vary much.
18
whether the seller has any buying experience on eBay) has a stronger effect on actual credit card
usage than in offering to accept credit cards, possibly because of the negotiation process.
The effect of product attributes on a seller’s offering a credit card option is presented in
Model 2. Again, used products have a highly significant negative effect upon offering credit
cards, while a product warranty has a highly significant positive effect. For example, a used
product (relative to a new one) will reduce the probability of offering credit cards by 9.7
percentage points. Consistent with the discussion for Table 4, with higher uncertainty associated
with the product, the chance of the return is higher and the expected profit for the seller from
using credit cards are lower; therefore, the seller is more reluctant to offer credit card options.
It appears that the degree of a product’s look and feel nature becomes statistically less
important in choosing to offer credit cards, compared with the actual use of credit cards. In the
actual transaction, the effect on credit card usage is almost significant at 10% level. Perhaps,
during the interaction with the buyer in the transaction, the seller senses the buyer’s concern
about product uncertainty and the high potential for dissatisfaction, which increases the chance
of a returned product and credit card chargeback. In such a transaction, the seller has a stronger
preference for a cash-equivalent.
When combining both seller characteristics and product attributes, the results are not very
different, but the model fits better (the results are not reported in the table). In addition, for
Model 1, the likelihood-ratio test cannot reject the null hypothesis that all slope parameters are
zero, but strongly rejects the null hypothesis for Model 2. Thus, it indicates that product
attributes are more important than seller’s characteristics for credit card offerings.
The effect of reputation ratings on credit card offerings is reported in Model 3 (Table 5).
It appears that a negative rating reduces the chance of a seller’s offering to accept credit cards,
19
and is significant at almost the 15% level. Statistically, this effect is much stronger than that
affecting actual credit card use. Perhaps, a seller with negative ratings is more self-protective
and thus prefers cash-equivalents, and the effect shows up statistically when there is no influence
from the buyer. A seller’s sale volume and years of selling experience both show a positive
effect on credit card offering, and is significant (or almost significant) at the 10% level. In
addition, seller education has a negative effect, significant at the 5% level.
6. Payment Choice Analysis with Nested Logit Models
The probit model provides a convenient tool to investigate the effects of product
attributes and seller’s characteristics on the choice of payment method. Moreover, it can also
investigate their effects on a seller’s offerings of payment methods. Thus, we can gain some
insight on the negotiation between the seller and the buyer regarding the payment transaction.
Yet, there are some limitations for the binary probit model. In particular, within one category,
one payment method is chosen more often than others, and the probit model cannot explain the
difference. For example, in the sample, traders use the PayPal credit card service more
frequently than using eBay credit card service; and among cash equivalents, money orders are
used most of the time. In addition, in the probit framework, we cannot estimate the effects of
payment-specific attributes on payment choice, because these variables perfectly predict the
choice; for example, if the seller’s fee is zero, the choice must be cash equivalents.
Therefore, a natural extension is to apply discrete choice analysis beyond the binary
choice model. Such a framework will allow us to investigate the choice probabilities for each
payment option, and moreover, to evaluate how the cost and inconvenience affects such choices.
One option would be to use multinomial logit model (or conditional logit model). However, this
20
model requires that the relative probabilities between choices must be independent of attributes
or existences of other alternatives (the IIA property).30 The similarity among different types of
credit card services and among cash equivalents makes the IIA assumption unlikely. Therefore,
we adopt a more general approach by using nested logit model (McFadden 1984).31
In a nested Logit model, similar choices are put into different groups (nests), and
correlated errors among choices within a group is allowed. The model process is to choose a
group (nest) first, then choose a particular alternative within the group. For alternatives in the
same nest, the IIA property is still required; while for alternatives in different nests, IIA property
in general is not required (Train, 2000). The resulting probability of choosing an option j is the
product of the marginal probability of choosing the group that contains the choice j and the
probability of choosing j conditional on choosing that group. In particular,
p( y = j ) = p( y = j | y ∈ B( j )) ⋅ p( y ∈ B( j ))
where B(j) is the nest to which alternative j belongs. The corresponding choice probability for
alternative j can be derived by assuming generalized extreme value (GEV) distribution. See,
among others, Train (2000) for the choice probability function. The nested logit model here
cannot be viewed as a random utility model because the payment choice is a joint decision
between the seller and the buyer.32 As discussed in the simple model (section 3), the actual
choice is determined by the latent variable—the weighted surplus among alternatives.
30
The IIA assumption follows from the initial assumption that the disturbances are independent and homoskedastic
for the random utility model.
31
Another alternative is multivariate Probit model. However, it has practical difficulty of computing the
multinormal integral and estimating an unrestricted correlation matrix.
32
There are two different specifications for the choice probability for nested logit model in the literature due to
different normalizations. In some cases, one of these specifications is not consistent with the random utility model
(see Koppelman and Wen 1998, Hensher and Greene 2000, and Heiss 2002 for discussions). This particular
specification is adopted in software package Stata, which will be used for our estimation. Since our model is not
based on random utility model, such a specification does not cause a concern here. Note that both specifications are
equivalent in terms of the implied marginal effects and elasticities.
21
In our sample, to apply a nested logit model, we exclude the choice of seller processed
credit card (4 observations) because it depends on whether the seller has the facility to process
credit card directly, and it is not a choice available to every transaction.33 We also exclude those
who send cash directly (3 observations). It is very rare to send cash directly in online
transactions due to its irretrievable nature.34 The use of cash should be under very special
circumstances and the determinants are very different from those of choosing other payment
methods.35 After excluding those observations, the sample contains 253 observations.
The corresponding tree structure for the nested logit model is specified in Figure 1. In
this structure, personal check enters as a degenerated nest, i.e., it has only one alternative. An
alternative structure would be to combine personal check with money order and cashier’s check.
However, it seems unlikely that the ratio of probability of choosing personal check to that of
choosing cashier’s check is independent of the existence of money order, so the IIA assumption
is violated in such a nest. Thus a separate nest for personal check is more appropriate.
In the nested logit model, three new alternative-specific variables are added. They are:
buyer’s fee, seller’s fee and payment time. These variables can directly evaluate the effect of
cost and convenience on payment choice. Buyer’s fee is specified as 0 for credit cards and
personal check, and $6 for cashier’s check, and fees for money order are listed in Table 1.36
Seller’s fee is 0 for money order, cashier’s check and personal check, and credit card transaction
fees are listed in Table 1.37 Finally, payment time is defined as the time between that the buyer
sends the payment and that the seller receives the money and ships the product. This variable
33
We have no information on whether every buyer has a credit card either, and thus assume this is the case.
The 3 observations using cash have a price of $4, $6, and $9. This is at the lower end of the prices.
35
One seller indicated to us that he used cash because the buyer was in the same town and he just dropped off the
product.
36
We do not have information on whether the seller passes the transaction fees on to the buyer.
37
The credit card transaction fees are based on their standard rate. Both eBay and PayPal have complicated
incentive discount schedule for sellers based on their volumes.
34
22
partially measures the convenience of a payment method: the shorter the pay time, the quicker
the seller will get the payment and the buyer will get the product. Pay time is specified as 0 for
credit cards, 3 days for money order and cashier’s check (average shipping time), and 6 days for
personal check (average shipping time plus 3 days average clearing time).
In a probit model, the alternative-specific variables perfectly predict the choice and thus
their effects cannot be identified. In our nested logit models, these variables are treated as
generic variables, i.e., each coefficient is the same for all alternatives. Such a structure helps to
save degrees of freedom. Because seller’s fee and buyer’s fee vary across choices and
observations, these two variables are used in the bottom level of the tree structure to explain the
choices of alternative payment methods. The pay time variable, however, does not vary within a
nest, thus is used at the first level to explain the choice of nest.
Individual-specific variables include product attributes and seller’s characteristics. Since
these variables do not vary across choices, it is necessary for them to interact with choice dummy
or nest dummy. Since the number of parameters increases rapidly with such variables, only
product warranty and seller’s monthly volume are included in the nested logit model. Based on
the probit model, they appear to be significant in most cases. These two individual-specific
variables interact with nest dummies in the model (the second branch--cash branch is left out for
normalization), because they appear to be more relevant to the choice of nests and such a
specification saves degrees of freedom.
The results of the nested logit model are reported in Table 6.38 Model 1 is a standard
specification for a nested logit model with four choice dummy variables. Because the variable
pay time is perfectly correlated with choice dummies, it is interacted with product price. The
38
The nested logit model is estimated using Stata, which does not calculate the marginal effect for nested logit
model.
23
model is highly significant based on the likelihood ratio test. Yet, most estimated parameters are
insignificant, indicating a high degree of multicollinearity. In fact, buyer’s fee and seller’s fee
are highly correlated with payment dummies.39 Based on the signs and magnitudes for the
payment dummy variables, it appears that PayPal is more attractive than eBay, and money order
and cashier’s check are almost indifferent. Buyer’s fee, seller’s fee, and pay time reduces the
probability of choosing the payment method.
To reduce the degree of multicollinearity, Model 2 drops dummy variables for alternative
choices while adding a dummy for each nest. As expected, the value of the log likelihood
function decreases substantially, but the model is still highly significant. Buyer’s fee and seller’s
fee is very significant. The higher the transaction fee is for the seller or the buyer, the lower the
probability choosing a payment method. Therefore, the payment cost does have a negative effect
on payment choices. Similar to Model 1, seller’s fee shows a stronger effect than buyer’s fee in
terms of magnitude. This result indicates that seller more strongly resists a higher fee, and thus it
is likely that the seller has more power in deciding the payment method.
On the other hand, pay time appears to have a negative effect on a payment method too.
As the pay time for a particular payment option grows longer, the probability of choosing it is
lowered. Clearly, traders prefer a more convenient way (less waiting time) to transfer money.
But it is only significant at the 25% level.
Based on the coefficients for nest dummies, credit cards seem to be more attractive than
money orders and cashier’s checks. Since costs and convenience are partly controlled in the
model, the attractiveness of credit cards can be attributed to the protections they provide.
39
Since the fees for money order changes with price, buyer’s fee is not perfect correlated with payment dummies.
24
Personal checks do not seem to be significantly different from money orders and cashier’s
checks. This is expected after controlling for the difference in cost and convenience.
The coefficients for individual-specific variables are normalized as a difference from the
coefficient for the nest of money order and cashier’s check (cash nest). The sign shows that a
warranty increases the probability of choosing credit cards relative to cash group, and it is
significant at the 10% level. As discussed in the probit section, a warranty reduces product
uncertainties, and thus reduces the chance of returning the product by the buyer. Therefore, it
increases the expected profit for the seller for using credit cards. On the other hand, product
warranty does not show a statistically significant difference between choosing check and the
cash nest. Such a result is expected. The similar results can be found for seller’s sales volume,
but the magnitude is very small, indicating little difference among cash, credit cards, and
personal check in terms of the effect of sales volume.40
Since the specification of interacting pay time and product price implicitly assumes that
the effect of pay time changes with product price, such a restriction may not be desirable.
Therefore, in Model 3, we use pay time directly in the model. To avoid perfect multicollinearity,
we also drop two nest dummies. The changes in other variables are small, but for pay time it is
substantial. The effect of pay time becomes highly significant in reducing the probability of
choosing a payment method. Yet, in this case, it is possible that the effect of pay time also
includes some other unobserved fixed effects of a payment method.
The parameter of the inclusive value (IV) for each nest is also reported in the table.
Based on Heiss (2002), in a nested logit specification that is consistent with random utility
40
When the measure “look and feel” nature of the product is added in the model, the results for other variables do
not change much. The effect of the “look and feel” nature is not statistically significant. The signs show that the
higher degree of uncertainty, the lower probability of choosing either credit cards. This is consistent with the probit
result that “look and feel” nature raises the chance of using cash equivalents.
25
model, the IV parameter measures the dissimilarity in each nest, and thus should be within a
[0,1] interval. In our results (based on Stata command), the IV parameters measure both
dissimilarity and the relative importance of the generic variables in the respective nest, thus the
interpretation is more difficult.
Finally, using the sub-sample, we include seller’s reputation measure in nested logit
models. As in the probit model, reputation does not seem to have any statistically significant
effect on the choice of payment method. The magnitude is also much smaller than that for
product warranty.
7. Conclusions
Due to physical separation between buyers and sellers, payment method is especially
crucial in online transactions for electronic commerce in general and for online auctions in
particular. Using the survey data collected from eBay users, we apply probit and nested logit
model to investigate the choice of payment methods in online auction markets.
The main findings of this study can be summarized as follows. First, the cost of a
payment method discourages its use in online transactions. The transaction fees for either buyers
or sellers reduce the probability of choosing the payment method. It appears that sellers have
stronger resistance to transaction fees. Second, the inconvenience associated with a payment
method, as measured by pay time, also reduces the chance of using this payment method.
Moreover, product attributes appear to have stronger effects on payment choices than
traders' characteristics. In general, if the uncertainties surrounding a product can be reduced, the
probability of using credit cards will increase, otherwise cash-equivalents will be more likely to
26
be adopted. Thus, product warranty, new product, and lower degree of "look and feel" nature of
a product have significant effect in raising the chance of using credit cards.
Furthermore, among traders' characteristics, the seller’s sales volume affects payment
choices. In addition, based on our sample, the seller's reputation rating does not have a
significant effect on the actual choice of a payment method. Yet, reputation rating appears to
have stronger effect on whether a seller offers credit cards as a payment option. Negative ratings
reduce the probability of seller's offering credit cards as a payment option.
As online auction markets become one of the major platforms for electronic commerce,
payment methods have inevitably become a central issue. Our research aims to advance the
understanding of payment choices for exchanging various products through online auctions.
Even though this study is based on online auctions, online bidding is not a specific feature for
our findings. Therefore, the results may apply to online payment transactions in other markets.
Based on our findings, the payment choice reflects a balanced evaluation of cost,
convenience, and protection. Therefore, it is important for payment transaction agencies to
balance these features. Over-emphasizing one feature but overlooking others may not produce a
successful payment method. Based on our sample from eBay, no transaction used escrow
services. This may not be an accident. Although escrow services provide a high degree of
protection, it is very inconvenient and very costly too, and thus is not preferred by traders.41
Furthermore, based on our findings, lowering transaction fees or increasing transaction
efficiency, while maintaining an adequate protection level, is certainly a favorable move for
online traders. In addition, since the payment choice is a joint decision between traders, fees for
buyers and for sellers both affect payment choices. Therefore, traders on both sides should be
41
The maximum price in our sample is $4,500. It is unclear whether escrow service will be used when the price is
high enough.
27
taken into account in designing payment means. Finally, since product attributes have
significant effects on the selection of payment methods, it is desirable to reduce product
uncertainties in order to facilitate online transactions. For example, offering guaranteed returns
with no refunding fees will help to increase the use of a payment method.
Given the data limitation, we only use data from sellers’ side in this study, and
information about buyers’ characteristics has been left out. This research can be largely
improved if buyer’s information for the same transactions is available. In addition, the relatively
small sample size, especially the small sample with reputation measures, may have affected our
estimation results. It would be of interest if future research moves toward these directions.
28
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30
Figure 1: Nesting Structure for Payment Choice
Credit cards
eBay credit card
PayPal credit card
Cash
Money order
Check
Personal check
Cashier’s check
31
Table 1: Comparison of Payment Methods
Payment
Method
Money
Order
Personal
Check
Seller/
Buyer
S
Protection
Cost
Convenience
- money is generally guaranteed
No fee
B
- Cashing is traceable
- 0 days float
- check could be false or bounce due to
insufficient funds
$0.90/$1.251
- Cashing is traceable
- 3 day avg. float
- money is generally guaranteed
Trivial
- Cashable in banks and
in some stores
- 3 day avg. mail time
- purchase at store, post
office or bank
- Cashable in banks and
in some stores
- 3 day avg. mail time +
3 days to verify funds
- Easy to write a check
S
B
Cashier’s
Check
S
B
Credit
Card
S
B
Debit
Card
(offline)6
1.
2.
3.
4.
5.
6.
S
B
- Cashing is traceable
- 0 days float
- a stolen card number is possible.3
- If the seller hasn’t received the buyer’s
signature, the buyer can withhold
payment on poor quality merchandise
under the Fair Credit Billing Act.
- fund is traceable
- Average 40 day float
- Similar to credit card
-The credit card’s Fair Credit Billing Act
protection does not apply. Many debit
card issuers voluntarily providing
protection similar to credit card
-fund is tracetable
-The funds are debited almost
immediately.
trivial verification cost
No fee
$62
- Cashable in banks
- 3 day avg. mail time
- Must purchase at bank
-transaction
fee;4
-chargeback
fees.5
No fee
- Automated payment
collection and money
management services
- Instant verification
Easy to give a card
number
Similar to
credit card
No fee
Similar to credit card
Similar to credit card
U.S. Post Office money order costs $0.90 for less than $500, and then $1.25 for up to $1000.
Based on Bank of America.
The seller can be reimbursed for product loss up to $250 through PayPal or eBay Payments, under certain
conditions. If the seller captured the buyer’s signature, greater reimbursement is possible. However,
sellers, who do their own credit card processing, suffer the loss.
For eBay payments, the fee is $0.35+3% (including 0.5% charge to transfer funds to seller’s bank account)
or $0.35 for a sales price less than $15. For Paypal, the fee $0.30+2.9%. For the seller’s own card
processing case, the credit card company will nominally charge 2.3%.
For the seller’s own card processing case, the chargeback fee is typically $15-20, and the eBay payment’s
chargeback is comparable (eBay payments passes the card company’s chargeback fee directly on to the
seller). The PayPal chargeback fee is $10.
Debit card offline use does not need a PIN number.
32
Table 2: Descriptive Statistics for the Sample
Standard
Variable
Observation Mean Deviation Minimum Maximum
Credit/debit card payment method
260
0.72
0.45
0.00
1.00
Seller extended credit card option
260
0.88
0.32
0.00
1.00
product price ($000s)
260
0.13
0.42
0.0013
4.50
used product
260
0.57
0.50
0.00
1.00
seller’s estimate of buyer’s desire
to physically examine product
260
1.76
1.08
1.00
5.00
warranty or guarantee with product
260
0.33
0.47
0.00
1.00
seller’s sales level per month ($000s)
260
2.19
0.01
0.00
100.00
seller's number of sales transactions per month 260
124.15 312.19
0.00 3300.00
seller’s selling experience on eBay (years)
260
3.81
1.48
0.33
7.25
seller has no eBay buying experience
260
0.02
0.15
0.00
1.00
seller is female
260
0.52
0.50
0.00
1.00
seller’s age (years)
260
40.54
11.82
0.00
65.00
seller’s education (years)
260
14.97
2.83
0.00
22.00
seller’s total reputations
110
447.21 912.64
0.00 7194.00
seller’s positive reputations
110
438.59 943.06
0.00 7146.00
seller’s positive reputation percentage
107
95.48
17.02
0.00
100.00
seller has at least one negative reputation
107
0.46
0.50
0.00
1.00
Table 3: Initial payment method proposal and actual payment method adopted
Payment Method
eBay Payment processed credit or
debit card
3rd party processed credit or debit
card (PayPal)
Seller processed credit or debit
card
eBay payment methods electronic
check
cashiers check
money order
Cash
escrow service
personal check
Adopted
Percent
Seller
Proposals
Buyer
Proposals
23
8.85
110
9
160
61.54
221
41
4
1.54
29
4
0
9
33
3
0
28
0.00
3.46
12.69
1.15
0.00
10.77
83
183
244
70
2
69
1
3
8
6
3
2
33
Table 4: Probit Analysis
Whether a Credit Card Was Used in the Transaction
Variable
Seller has at
least
one negative
reputation
rating
Seller’s
number of
monthly
transactions
Seller’s eBay
selling
experience in
years
Seller has only
eBay selling
experience
Seller’s age
Seller’s gender
Seller’s educ
Model 1:
Seller Characteristics
Model 2:
Product Characteristics
Coeff.
Coeff.
Marginal
Effect
Log likelihood
LR chi2(q)
Prob > chi2
Pseudo R2
Number of
observations
Model 4:
Seller’s reputation rating
Coeff.
.19
(0.66)
Marginal
Effect
.055
.0014**
(2.25)
.00046
.0012*
(1.82)
.00039
.0016
(1.32)
.00048
.036
(0.61)
.012
.040
(0.66)
.013
-.025
(-0.29)
-.0076
-.85
(-1.56)
-.32
-.63
(-1.09)
-.23
-.012
(-1.51)
-.22
(-1.24)
-.046
(-1.40)
-.0038
-.012
(-1.49)
-.0038
-.00025
(-0.02)
-.26
(-0.92)
-.088
(-1.62)
-.000074
-.071
-.015
Price of
product
Used product
Look and feel
(rated by
Seller)
Warranted
product
Constant
Marginal
Effect
Model 3:
Seller and Product
Characteristics
Coeff.
Marginal
Effect
1.62
(2.75)
-145.72
17.26
.00084
.056
260
(q=6)
-.092
(-0.44)
-.23
(-1.31)
-.12
(-1.53)
-.030
.58**
(2.97)
.77
(3.89)
-147.05
14.62
.00056
.047
260
.18
-.075
-.039
(q=4)
-.047
(-0.22)
-.13
(-0.71)
-.11
(-1.34)
-.015
.49**
(2.45)
.95
(2.35)
-143.23
22.25
.00045
.072
260
.15
-.078
-.026
-.042
-.034
(q=8)
2.06
(2.13)
-58.13
8.55
.02
0.069
110
(q=6)
1. Results of two-tailed Z-test statistics are in the parenthesis.
2. ** significant at 95-percent level; * significant at 90-percent level.
34
Table 5: Probit Analysis
Whether the Seller Offered a Credit Card Payment Option for the Transaction
Variable
Seller has at least
one negative
reputation rating
Seller’s number of
monthly
transactions
Seller’s eBay
selling experience
in years
Seller has only
eBay selling
experience
Seller’s age
Seller’s gender
Seller’s educ
Model 1:
Seller Characteristics
Model 2:
Product Characteristics
Model 3:
Seller’s reputation rating
Coeff.
Coeff.
Coeff.
Marginal
Effect
-.63
(-1.38)
.00032
.011
(1.60)
.00020
.094
(1.30)
.017
.27*
(1.89)
.0052
-.20
(-0.30)
-.040
-.010
(-1.15)
.049
(0.23)
.0082
(0.23)
-.0018
-.022
(-1.18)
.32
(0.72)
-.21**
(-2.06)
-.00042
.0086
.0014
Used product
Look and feel
(rated by Seller)
Warranted product
Log likelihood
LR chi2(q)
Prob > chi2
Pseudo R2
Number of
observations
Marginal
Effect
-.014
.0018*
(1.65)
Price of product
Constant
Marginal
Effect
.98
(1.55)
-91.16
7.68
.026
.040
260
(q=6)
.61
(0.86)
-.61**
(-2.53)
-.088
(-0.94)
.61**
(2.21)
1.55
(5.56)
-86.80
16.39
.00025
.086
260
.0061
-.0039
.10
-.097
-.015
.089
(q=4)
4.34
(2.42)
-25.21
16.59
.0011
0.25
110
(q=6)
1. Results of two-tailed Z-test statistics are in the parenthesis.
2. ** significant at 95-percent level; * significant at 90-percent level.
35
Table 6: Nested Logit Results
Variables
Payment option:
eBay credit card
PayPal credit card
Cashier’s check
Money order
Buyer’s fee
Seller’s fee
Model 1
14.80
(0.65)
16.73
(0.73)
10.66
(0.03)
10.84
(0.03)
-0.23
(-0.43)
-0.83
(-0.97)
-0.26**
(-3.44)
-0.81*
(-1.69)
0.65*
(1.61)
-0.44
(-0.67)
0.0011
(0.95)
-0.0060
(-1.45)
-0.41
(-1.02)
1.20*
(1.66)
0.15
(0.20)
0.67
(1.64)
-0.43
(-0.64)
0.0011
(0.92)
-0.0060
(1.46)
-0.68
(-1.15)
Payment nest:
Credit card nest
Personal check nest
Warranty × Credit card
× Check
Sales × Credit Card
× Check
Pay time × Price
Model 2
Pay time
IV parameters:
Credit card nest
Cash nest
Check nest
Log likelihood
LR test
P-value of LR test
Model 3
-0.34**
(-6.10)
-0.80*
(-1.64)
0.78*
(1.98)
-0.30
(-0.44)
0.0018
(1.29)
-0.0030
(-0.88)
-0.24**
(-5.21)
0.07
(0.77)
-0.0096
(-0.03)
0.5
0.14
(0.91)
4.60
(0.59)
0.5
0.02
(0.92)
1.62
(1.32)
0.5
-277.18
260.01
0.00
-333.32
147.73
0.00
-337.14
140.09
0.00
Note: 1. total number of observation 1265.
2. The result of z statistics are in the parenthesis.
3. ** significant at 5% level; * significant at almost 10% level (|z|>1.60)
36