How People Select Their Payment Methods in Online Auctions? An Exploration of ebay Transactions

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1 How People Select Their Payment Methods in Online Auctions? An Exploration of ebay Transactions Haizheng Li School of Economics Georgia Institute of Technology Han Zhang DuPree College of Management Georgia Institute of Technology Abstract Online payments are a critical issue in electronic markets. This research investigates online payment choices using probit model based on the survey data we collected from ebay users. We discuss 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 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. 1. Introduction As an efficient and flexible sales channel, online auction has drawn significant attention from the research community. Concentrating on the asymmetric information problem [2] in electronic markets, much literature on online auctions addresses trust issues (e.g., [3], [7], [10]). Particularly, considerable attention has been devoted to the effects of reputation systems on online auctions (e.g., [4], [15], [16]). However, one critical area in online auction markets has remained unexplored: how do trading partners select a payment method for settling their transactions? As online auction markets become one of the major platforms for electronic commerce, payment methods have inevitably become a central issue. 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 [8]. Therefore, choosing an appropriate payment method for online auction participants to safely finish their transactions becomes significantly critical. Currently, there are different payment methods available in online auction markets. 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 significance of this research is twofold. Firstly, this research can help online auction participants understand the pros and cons of different payment choices, assisting them to find the appropriate payment method according to different product attributes, traders characteristics and payment attributes. Secondly, this research can shed light on the pattern of the online payment choices. For online auction houses and other online payment processing agencies, knowing the factors that affect payment choices will help them meet the needs of online traders and form strategies that provide them greater shares of their competitive market. Therefore, concentrating on the determinants of payment choices, this research will have important implications for e-commerce practices. The paper is organized as follows. Section 2 examines risk, convenience and cost related to payment methods. Section 3 discusses the survey. Section 4 presents the results from the probit model. Section 5 concludes the paper. 2. Risk, Convenience and Cost of Online Payment Abrazhevich [1] 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 [5] 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 /04 $17.00 (C) 2004 IEEE 1

2 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. 1 Therefore, it is reasonable to conclude that risk, convenience, and cost are among the most important issues in online payment transactions. Payment Method Money Order Personal Check Cashier s Check Credit Card Seller / Buyer S B S B S B S Table 1. Comparison of payment methods 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. Therefore, online auction sites usually excuse themselves from the Protection Cost Convenience - money is generally guaranteed - Cashing is traceable - 0 days float - check could be false or bounced due to insufficient funds - Cashing is traceable - 3 day avg. float - money is generally guaranteed - Cashing is traceable - 0 days float - a stolen card number is possible. No fee - Cashable in banks and in some stores - 3 day avg. mail time $0.90-$ purchase at store, post trivial verification cost Trivial 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 No fee - Cashable in banks - 3 day avg. mail time $6 - Must purchase at bank -transaction fee; -chargeback fees. B - 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 No fee Debit S - Similar to credit card Similar to credit Card card (offline) B -The credit card s Fair No fee Credit Billing Act protection does not apply. Many debit card issuers voluntarily providing protection similar to credit card -fund is traceable -The funds are debited almost immediately. - Automated payment collection and money management services - Instant verification Easy to give a card number Similar to credit card Similar to credit card In general, online payment methods differ in terms of risk, convenience, and cost. Most of the risk 1 Report of the Working Party on Electronic Money, Source: Paper version from U.S. Department of the Treasury, Public Affairs. Electronic version: The G10 countries are: France, Germany, Italy, Switzerland, the Netherlands, Belgium, Sweden, the UK, the US, Canada and Japan. responsibility of user authentication. 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 cannot control the quality, safety or /04 $17.00 (C) 2004 IEEE 2

3 legality of the items advertised, the truth or accuracy of the listings, thus exposing bidders to potential risks and fraudulent transactions. 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 cash-equivalents (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 purchased with a credit card. However, not all consumers like to use credit cards online due to various security concerns. 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, cashequivalents 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. Besides the transaction security, trading partners are also concerned about transaction convenience. Convenience is becoming an increasingly important issue in modern society. A major function of Internet business is to provide convenience in information gathering and order processing [9]. Therefore, shopping convenience plays an important role in the success of Internet commerce. 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. 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 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 the transaction fees depending on the payment method. 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 [12]. 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 other words, trading parties characteristics and product attributes may play a major role to affect people s decision about which payment method they would like to choose due to the concerns over risk, convenience and cost. 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 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) [6], because they differ in non-digital attributes, which can only be evaluated by perceiving them in person [11]. 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. 3. 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, making 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 ( is a /04 $17.00 (C) 2004 IEEE 3

4 trusted third party enabling consumers to send and receive payment online. Escrow ( is emerging as another type of trusted third party, which provides online escrow service for transferring products and payments between online sellers and buyers. ebay also launched its own online payment system, called Billpoint. 2 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. In the questionnaire, 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 (experienced sellers on ebay) and five student ebay sellers for pretest. 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. The original research plan attempted to follow Ba and Pavlou s survey methodology [4]. 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 system, and invited them to participate in our survey. Unfortunately, after we sent out about 20 s 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 system. Therefore, we turned to post our questionnaire on discussion forums on ebay and related auction sites, such as Auctionwatch ( and Auction Guild ( We did not offer a monetary reward for participation in the survey, 2 ebay announced the completion of its acquisition of PayPal in October When we did the survey, Paypal was an independent company. After ebay acquired PayPal, Billpoint remained a payment option on ebay through the end of 2002 and began to be phased out in early 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. 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 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. 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. Three questions were asked about the payment method(s) initially proposed by the seller and the buyer, respectively, as well as the actual method adopted in the transaction (Table 3). It appears that about 72% of payments are rendered using credit cards or debit cards, the majority of which being rendered via PayPal (61.54%). Money orders account for 12.69% of transactions, while personal checks account for 10.77%. Detailed information on payment choice is summarized in Table 3. This table demonstrates that although we focused on ebay sellers in the questionnaire, we can still get the basic information about their last transaction and we can still know what payment methods the buyers originally proposed and /04 $17.00 (C) 2004 IEEE 4

5 what the actual payment methods they eventually common characteristic is that cash equivalents must be adopted. physically mailed from the buyer to the seller, while Table 2. Descriptive statistics for the sample Standard Variable ObservationMean DeviationMinimum Maximum Credit/debit card payment method Seller extended credit card option product price ($000s) used product seller s estimate of buyer s desire to physically examine product warranty or guarantee with product seller s sales level per month ($000s) seller's number of sales transactions per month seller s selling experience on ebay (years) seller has no ebay buying experience seller is female seller s age (years) seller s education (years) seller s total reputations seller s positive reputations seller s positive reputation percentage seller has at least one negative reputation Table 3. Initial payment method proposal and actual payment method adopted Payment Method Adopted Percent Seller Proposals Buyer Proposals ebay Payment processed credit or debit card rd 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 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 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. 3 The 3 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, /04 $17.00 (C) 2004 IEEE 5

6 advantage of the probit analysis is that the effects of product attributes and traders characteristics on payment choices can be easily investigated. 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. In Probit model, a parameter estimate only provides information on the direction of the effect through its sign. Thus, for each model, we also calculate the marginal effects for each variable evaluated at the sample average of independent variables. The marginal effect for any continuous variable x j is calculated based on the following formula: ^ p( y = 1 x) = φ( x' β ) xj φ( x' β βj, where ) is the standard normal density. While for dummy variable x i, the marginal effect from changing x i from zero to one, holding all other variables fixed, is Φ xjβ j + βi) Φ( xjβj), ( j i j i where Φ( x' β ) is the standard normal cumulative distribution function. Furthermore, to evaluate the effectiveness of the models, we also reported the pseudo R-squared and perform a Likelihood Ratio test for each model. Pseudo R-squared is one measure of a probit model s goodness of fit [14]. It equals 1 - L ur /L 0, where L ur is the log-likelihood function for the estimated model, and L 0 is the log-likelihood function in the model with only an intercept. Table 4 reports the 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 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 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 minor. 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 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 18 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) /04 $17.00 (C) 2004 IEEE 6

7 Table 4. Probit analysis whether a credit card was used in the transaction Model 1: Seller Characteristics Model 2: Product Characteristics Model 3: Seller and Product Characteristics Model 4: Seller s reputation rating Variable Coeff. Marginal Seller has at least one negative reputation rating Seller s.0014** number of (2.25) monthly transactions Seller s ebay selling experience in years Seller has only ebay selling experience.036 (0.61) -.85 (-1.56) Seller s age (-1.51) Seller s -.22 gender (-1.24) Seller s education (-1.40) Price of product Used product Look and feel (rated by Seller) Coeff. Marginal Coeff * (1.82) (0.66) (-1.09) Marginal Coeff..19 (0.66) (1.32) (-0.29) (-1.49) (-0.02) (-0.92) (-1.62) (-0.44) (-0.22) (-1.31) (-0.71) (-1.53) (-1.34) Marginal Warranted product.58** (2.97).18.49** (2.45).15 Constant 1.62 (2.75).77 (3.89).95 (2.35) 2.06 (2.13) Log likelihood LR chi2(q) (q=6) (q=4) (q=8) 8.55 (q=6) Prob > chi Pseudo R Number of obs Results of two-tailed Z-test statistics are in the parenthesis. 2. ** significant at the 5% level; * significant at the 10% level. 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 cashequivalent. When product uncertainty is reduced because of a warranty, a new product or the 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. (Since the buyer first bids on price /04 $17.00 (C) 2004 IEEE 7

8 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.) 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 are preferable. From the buyer perspective, the higher the price, the higher the risk associated with the seller fraud, such as non-delivery. It is possible that price is correlated with some product attributes such as warranty. Yet, this is merely a multicollinearity problem. Therefore, credit cards are preferable. 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. 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. However, the effect of reputation turns out to be not statistically significant. In alternative specifications, we used percentage of positive rating, percentage of negative rating, and number of negative rating. The results are very similar. 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 selfprotection. Thus, the net effect becomes ambiguous. On the other hand, since the actual payment method adopted in a transaction 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 the 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., 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 is 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, 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 selfprotective 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 /04 $17.00 (C) 2004 IEEE 8

9 Table 5. Probit analysis whether the seller offered a credit card payment option for the transaction Model 1: Seller Characteristics Model 2: Product Characteristics Model 3: Seller s reputation rating Variable Coeff. Marginal Seller has at least one negative reputation rating Seller s number of.0018* monthly (1.65) transactions Seller s ebay selling experience in years Seller has only ebay selling experience 5. Conclusions.094 (1.30) -.20 (-0.30) Coeff. Using the survey data collected from ebay users, we apply a probit model to investigate the choice of payment methods in online auction markets. The main findings of this study can be summarized as follows. 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 cashequivalents will be more likely to be adopted. Thus, Marginal Coeff (-1.38) (1.60) * (1.89) Marginal Seller s age (-1.15) (-1.18) Seller s gender (0.23) (0.72) Seller s educ.0082 (0.23) ** (-2.06) Price of product (0.86) Used product -.61** (-2.53) Look and feel (rated by Seller) (-0.94) Warranted product.61** (2.21).089 Constant.98 (1.55) 1.55 (5.56) 4.34 (2.42) Log likelihood LR chi2(q) 7.68 (q=6) (q=4) (q=6) Prob > chi Pseudo R Number of observations Results of two-tailed Z-test statistics are in the parenthesis. 2. ** significant at the 5% level; * significant at the 10% level. 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 /04 $17.00 (C) 2004 IEEE 9

10 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. 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. 5. Acknowledgements The authors would like to thank Richard Ward for his hard work on collecting the data and his great contribution to the project at its early stage. 6. References [1] Abrazhevich, D., Electronic Payment Systems: Issues of User Acceptance, Working paper, Available: biz2001.pdf [2] Akerlof, G., The Market for Lemons: Quality Uncertainty and the Market Mechanism, Quarterly Journal of Economics, 1970, 84(3), pp [3] Ba, S., A. B. Whinston, and H. Zhang, Building Trust in Online Auction Markets through an Economic Incentive Mechanism, Decision Support System, 2003, Vol. 35(3), pp [4] Ba, S., and P. Pavlou, Evidence of the of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior, MIS Quarterly, 2002, 26 (3), pp [5] Carow, K. A., and M. E. Staten, Debit, credit, or cash: Survey evidence on gasoline purchases, Journal of Economics and Business, 1999, 51, pp [6] De Figueiredo, J., Finding sustainable profitability in electronic commerce, Sloan Management Review, 2000 (Summer), pp [7] Dellarocas, C., The Digitization of Word-of-Mouth: Promise and Challenges of Online Reputation Systems, working paper, Sloan School of Management, Massachusetts Institute of Technology, [8] Internet Fraud Watch, Available: [9] Kim, J., Jungwon Lee, Kwanghee Han and Moonkyu Lee, Business as Buildings: Metrics for the Architectural Quality of Internet Businesses, Information Systems Research, 2002, 13(3), pp [10] Kollock, P., The Production of Trust in Online Markets, in Lawler, E. J., M. Macy, S. Thyne, and H. A. Walker eds, Advances in Group Processes (Vol. 16), Greenwich, CT: JAI Press, [11] Lal, R., and M. Sarvary, When and how is the Internet likely to decrease price competition? Marketing Science, 1999, 18(4), pp [12] Lee, Mie-Yun, Low-cost measures can prevent highcost online fraud, Bizjournals, November 20, Available: iness_shopper/2000/11/20/column216.html. [13] Mantel, B., Why don t consumers use electronic banking: Towards a theory of obstacles, incentives and opportunities, Federal Reserve Bank of Chicago, occasional paper series, [14] McFadden, D. L., Econometric Analysis of Qualitative Response Models, in Handbook of Econometrics, Vol. 2, ed. Z. Griliches and M. D. Intriligator. Amsterdam: North Holland, 1984, pp [15] Melnik, M. I., and J. Alm, Does a Seller s Ecommerce Reputation Matter? Evidence From ebay Auctions, The Journal of Industrial Economics, 2002 (September), Vol. L, No. 3, pp [16] Resnick, P., and R. Zeckhauser, Trust Among Strangers in Internet Transactions: Empirical Analysis of ebay s Reputation System Working Paper for the NBER workshop on empirical studies of electronic commerce, Available: html /04 $17.00 (C) 2004 IEEE 10