Summer Milestone 10:Reputation systems-Trust Among Strangers in Internet Transactions(TwoCodeGirls)

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Introduction:

This paper talks about the reputation system that exists for the eBay platform and tries to explain how this system works.It does a comparison between internet based reputation versus reputation system of the traditional systems. Trust in traditional systems is created by the following factors:

  1. Most retail transactions are conducted locally, which gives individuals the opportunity to inspect them, as say with fruit in a rural market. If quality is discernible, no trust is needed.
  2. Retail operations tend to be large relative to their local market, be they vegetable sellers or the local department store. Buyers have frequent interaction with the same seller, and learn whom they can trust.
  3. Even when one’s personal interactions are limited, given that a retailer’s sales are concentrated in a locale makes it easy to develop reputations so customers learn about retailers from their peers.
  4. Retailer reputations are borrowed from other contexts. For example, retailers are likely to be pillars of the church and community, and would be highly reluctant to sacrifice the status that comes from such reputations.
  5. Reputations are built over many years; witness the reputations of Sotheby’s and Christies, the leading auction houses, which are hundreds of years old.
  6. Reputations are borrowed from others. Thus celebrities will attest to the quality of products.
  7. New goods benefit from established brand names, and policing of quality by those who own them. The product, not the retailer, wins the reputation.
  8. Significant expenditures – e.g., building a fancy store on Manhattan's Fifth Avenue -- indicates that one will be reliable, lest this expenditure be wasted, a form of signaling.
  • Although the reputation system is in place, nobody seems to be aware of exactly how it works, and that this knowledge/awareness is not necessary for it to be successful, rather they just need to believe that the system works.
  • The disincentive to give negative feedback might be far stronger since they fear lawsuits and retaliatory feedback


How eBay works:

  1. Register with email id and username (can be any name/moniker)
  2. Neither buyer or seller can see real name or address info
  3. Initially anybody was allowed to give feedback about buyer/seller, later on they changed the rule to feedback given has to be tied to a particular transaction. i.e. only the seller and the winning bidder can give feedback about each other
  4. Reputation of buyer is far less important as sellers can hold goods until they are paid and it would do no good for the seller to sell based on buyer’s reputation since it is not possible to exclude buyers with bad reputation from their auctions.


Data sets & analysis:

  1. Are most transactions between strangers or do users develop ongoing trading relationships?
Analyzed 1000 sellers. Found that 17.9% of all sales involved a seller and buyer who had done business with each other before.
  • Nature of feedback?
  • Mostly positive feedback(99.1%).Very few negative(0.6%) and neutral(0.3%) feedback. Analysis of the various reasons for negative feedback is done.
  • Reasons for negative feedback:
  • Backed out of transaction(did not contact or respond to high bidder)
  • No item received after sending payment
  • Other communication problems
  • Reasons for neutral feedback:
  • Arrived in poor condition, item not as advertised, replica rather than original
  • Slow shipping
  • Positive about transaction
  • Does prior feedback predict future performance?
  1. We model the feedback the buyer provides about the transaction as a function of the seller’s previous feedback profile. It is not obvious exactly what features of the feedback profile should be most diagnostic of performance. The analysis above suggests that more experienced sellers are better, up to a point.
  2. The feedback profiles of buyers follow a similar pattern, although they have somewhat less problematic feedback overall than sellers. One possible explanation is that those who were buyers in the transactions in our sample tended to have more of their prior feedback from purchases than did our sellers. Since buyers offer a standard good (money) and offer it first, one might expect buyers to receive less negative feedback overall than sellers.
  3. It is worth noting here that since negative feedback is rare, for experienced buyers the positive feedback score is almost the same as the net score of positives minus negatives. Thus, predicting performance based only the net score that eBay computes would treat the sellers with 100 positives and 0 or 3 negatives as almost the same, while this model suggests that the risk of problematic transactions is quite different for the two profiles.
  • Do buyers reward better reputations?
  1. They might choose to do so in a self interested fashion, because they believed reputations were diagnostic of future performance, i.e., they would pay more to get what was likely to be a better quality good. Or, perhaps seeking a warm glow, they could be selflessly policing the system, seeking to provide appropriate incentives to sellers.
  2. The logistic regression model predicts that a seller with no prior feedback has a probability of sale of 72%, approximately the same as that for sellers with 2 positives and 1 problematic. 12 positives and no neutrals or negatives is approximately equivalent to 37 positives with 1 problematic, leading to a probability of sale around 90%. 70 positives and no problems brings the modeled probability of sale up to 96%.
  3. The logistic regression model predicts that a seller with no prior feedback has a probability of sale of 48%. 12 positives and no neutrals or negatives is approximately equivalent to 36 positives with 1 negative, leading to a probability of sale around 80%. 78 positives and no negatives brings the probability of sale up to 91%.
  • Incentive effects: Do sellers pay attention to to their feedback profiles?
  1. To assess this question more systematically, they examine two pieces of empirical evidence, the propensity of sellers to respond to negative feedback, and whether their behavior changes after receiving negative feedback. If sellers were concerned about how negative feedback would affect their reputations, they might expect them to provide explanations in response to negative feedback.
  2. According to the data, recipients entered explanatory text 29% of the time.


Conclusion:

What matters is not how the system works, but how its participants believe it works, or even whether they believe it works even if they have no concern about why.In the paper they make an analogy drawn from grander considerations: the behavior of man in a world without a God might be fully moral and God fearing if its denizens believed there was a God who would judge them and possibly punish them in the hereafter.Internet auction sites have developed an ingenious feedback system which enable sellers to build reputations from satisfied customers thus making up for the lack of traditional feedback mechanisms.The system is better off the way it currently is i.e. mildly dissatisfied buyers do not record their dissatisfaction. If this were done honestly by each buyer, it might destroy the overall faith that people have in the marketplace. With auctions of objects, sellers have strong incentives to exaggerate the quality of or misrepresent the authenticity of their items. Yet judging by the volume of transactions, sellers successfully build reputations of trust. This analysis provides some data on how seller reputations are created, and sketched some mechanisms that are turning such reputations into trust.