4. Trading partner’s reputation. Most auction sites provide the historical records of a trader. This is a useful source that can be used to estimate a trading partner’s reputation.
Denote A-PRRt B-PRRt, and D-PRRt for average PRR, base PRR, and dynamic PRR, respectively, in trading time frame t. A trader’s PRR in trade i in trading time frame t is formalized as:
PRRi = Φi[Φt
B(A-PRRt-1, τ, γ), Φt
D(Repu)]
where τ is the factor of personal trading loss factor, γ is the trader’s risk preference factor, B-PRRt = ΦtB(A-PRRt-1, τ, γ) is a function of A-PRRt-1,τ and γ, A-PRRt-1 is an average of B-PRRt-1 over traders, and D-PRR = Φt
D(Repu) is a function of the trading partner’s reputation factor Repu.5
The simulation program accesses a pool of 160 traders. Each trader is assigned an initial base PRR ranging from 0.015 to 0.03, and the OES fee rate is preset as 2% of the transaction amount. That is, B-PRR0 is uniformly distributed at the beginning. Instances of Internet fraud are randomly generated among trades.
Traders, either sellers or buyers, decide the adoption of online escrow if they are of honest type. Then A-PRRt, B-PRRt D-PRRt and PRRi are recursively calculated. Figure 4 shows that A-PRRt converges to the loss rate, which is the observable indicator as the rate of committed fraud.
Trading Experience Risk Attitude
Trading Partner Reputation
Average PRR
Base PRR
PRR OES Adoption
Decision Average PRR is the mean
of Base PRR
Dynamic PRR
PRR Calculation Randomized
Factors
Figure 3. Measurement model for PRR
We further tested the sensitivity of simulation outcomes to the deviation of PRR estimate. Normally the value of PRR should be close to the loss rate because it is the estimate of the latter. Their ratio can be viewed as an indicator for the accuracy of the estimation. According to the PRR calculation formula PRR directly affects the OES adoption that finally suppresses fraud. However, since the resulted lower loss rate will conversely reduce PRR, there exists a dynamic equilibrium when the PRR estimate is deviated from the loss rate (either overestimating or underestimating the real situation). Define fraud rate as the probability that a fraud may happen in an online C2C trade, defrauding rate as the probability that a cheating-type trader decides to cheat in a trade, and fraud blocking rate as the probability that the adoption of OES will block a fraud attempt. The experiment shows that given a fraud rate, the deviation of PRR estimate positively affects the OES adoption rate and the fraud blocking rate (Figure 5a) and negatively affects the defrauding rate (Figure 5b).
However the fraud blocking rate curve is flatter than the OES adoption rate.
This indicates that although the overestimation of PRR increases the OES adoption rate, it may not be socially optimal.
Figure 4. Dynamics of average PRR (A-PRRt)
0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018
500 2500
4500 6500
8500 10500
12500 14500
16500 18500
20500 22500
24500 26500
28500 Trade #
Loss Rate Average PRR
Conclusions
This chapter shows the results of studies of the OES adoption problem for the honest trader and the OES fee optimization problem for the monopolist OES provider in Internet-based C2C auction markets. PRR, the subjective estimate of online trading risk, is used to link together the models for OES demand and supply sides. In adopting online services for electronic commerce, PPR becomes the driving factor in using the financial assurance services of a trusted Figure 5. Effect of PRR estimation on OES market equilibrium (fraud rate
= 2%)
0 .0 0 % 1 0.0 0%
2 0.0 0%
3 0.0 0%
4 0.0 0%
5 0.0 0%
6 0.0 0%
7 0.0 0%
-2 0 .00 % -1 0 .00 % 0 .0 0 % 1 0.0 0% 2 0.0 0% 3 0.0 0% 4 0.0 0%
PRR Es tim at io n Bia s Fra ud b loc kin g ra te
O ES ad o tion ra te
0 .0 0 % 1 0 .0 0 % 2 0 .0 0 % 3 0 .0 0 % 4 0 .0 0 % 5 0 .0 0 % 6 0 .0 0 % 7 0 .0 0 % 8 0 .0 0 % 9 0 .0 0 % 1 0 0 .0 0 %
- 2 0 .0 0 % - 1 0 .0 0 % 0 .0 0 % 1 0 .0 0 % 2 0 .0 0 % 3 0 .0 0 % 4 0 .0 0 % P RR Es tim a t io n Bia s
Defrauding Rate
(a) Sensitivities of OES adoption rate and fraud blocking rate to PRR estimation deviation from loss rate.
(b) Sensitivity of defrauding rate to PRR estimation deviation from loss rate.
third party, and the reduced risk under the protection of such a service improves the trustworthiness of the online auction marketplace. We briefly introduce a calculative model for PRR with a two-step recursive calculation.
The converging PRR from the simulation shows a normal-like distribution in a stable status.
Further theoretical research should achieve two objectives. The first one is to complete a game theoretic model by introducing a cheater-based decision- making process. This will be a sequential signaling game model with extensive sub-game, perfect Nash equilibrium analyses. The second objective is to further explore the relationship between perceived risk in using an online facility and the facility’s trustworthiness (for example, Kim & Prabhakar, 2000). The OES provides a good setup to explore that relationship when a TTP is present.
Moreover, promising outcomes may also come from empirical studies. One aspect is to conduct comprehensive computer experiments to study the relationship among PRR, OES adoption rate, fraud rate, and OES fee rate.
Both computer simulation and human-based experiments could be carried out.
In addition, from a behavioral point of view, the causal relationship between the underlying factors and PRR could be another interesting research issue in the next stage.
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Endnotes
1 See http://news.com.com/2100-1017-898154.html (last accessed on August 10, 2004).
2 The case of “joint payment” for the OES simply increases mathematical complexity with the same theoretical conclusions.
3 For example, eBay has entered into an alliance with Escrow.com (www.escrow.com).
4 The properties of merchandise should be one of the factors in PRR estimation. This model assumes that PRR is indifferent to this factor to reduce the complexity of analyses.
5 Due to the limited size of the chapter, detailed information about relations between components in Figure 3 has been omitted.