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Online feedback and reputation

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Estimating trading risks in the presence of reporting bias

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Online feedback and reputation

  1. 1. Luigi Reggi Rockefeller College University at Albany SUNY PAD 703 – Economic and Financial Theory 1 Online feedback and reputation
  2. 2. Readings 2 Dellarocas, C., & Wood, C. A. (2008). The sound of silence in online feedback: Estimating trading risks in the presence of reporting bias. Management Science, 54(3), 460-476 Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management science, 49(10), 1407-1424
  3. 3. Research Questions Is eBay online feedback biased? What determines this bias? Main conclusions: - Yes, our quantitative estimates of positive transaction outcomes are lower than positive feedback posted online - Evidence of both positive and negative reciprocation 3
  4. 4. Agenda • Background • Dataset and descriptive statistics • Methodology and results of 2 models • Discussion • Conclusions 4
  5. 5. Background • Online feedback –elicits good behavior and cooperation –facilitates transaction among strangers –improve efficiency of online markets • Internet Auctions accounts for 16% of all consumer frauds 5
  6. 6. eBay feedback mechanism • Voluntary self-reporting of the outcomes of the transactions –Public report of private outcomes: OUTCOME ====> FEEDBACK • Bi-directional 6
  7. 7. eBay feedback mechanism 7
  8. 8. Dataset • 51,052 eBay rare coin auctions in 2002 –16,045 Buyers –6,242 Sellers • Info on: seller, buyer and feedback posted (type, timing, etc.) 8
  9. 9. Feedback 9 Type of feedback Seller’s feedback Buyer’s feedback Positive (+) 99.3% 98.9% Neutral (o) 0.1% 0.5% Negative (-) 0.6% 0.5% % of auctions where seller or buyer posted feedback: % of auctions where seller posted feedback: 77.5% % of auctions where buyer posted feedback: 67.8%
  10. 10. Type of feedback and relative order 25 combinations 10 Who comments first Seller’s feedback Buyer’s feedback % of auctions Seller Positive (+) Positive (+) 38.4% Buyer Positive (+) Positive (+) 18.2% Seller Positive (+) SILENCE 20.2% Buyer SILENCE Positive (+) 10.4% / SILENCE SILENCE 11.8% Subtotal 99.0% All other 20 combinations 1.0% Total (25 combinations) 100.0%
  11. 11. 1st model: simultaneous equations 11 Pr(jb, js) = SUM [ Pr(ib, is) * Pr(jb|ib) * Pr(js|is) ] Prob of observing a FEEDBACK PATTERN (e.g. positive for buyer, positive for seller) j = feedback reported i = outcomes of transaction s = seller b = buyer Prob of given OUTCOMES of transaction Prob that the buyer reports feedback j given oucome i Prob that the seller reports feedback j given oucome i
  12. 12. 2 assumptions • Assumption 1: one-to-one mapping between outcomes and feedback types: good outcome => positive feedback mediocre outcome => neutral feedback bad outcome => negative feedback • Assumption 2: traders tell the truth 12
  13. 13. Estimation • Maximum likelihood method • Estimated probabilities of observing a given outcome: 13 Seller’s outcome Buyer’s outcome Good 88.6% 81.3% Mediocre 10.4% 17.4% Bad 0.1% 1.1%
  14. 14. 2nd model: adding feedback timing • Makes use of the 25 combination of type of feedback and temporal ordering • Estimated probabilities of observing a given outcome: 14 Seller’s outcome Buyer’s outcome Good 85.6% 78.9% Mediocre 13.7% 20.4% Bad 0.6% 0.7%
  15. 15. 2nd model: adding feedback timing 2nd mover propensity to report given 1st mover feedback 15 Second mover Oucome experienced by second mover First mover’s feedback Positive Neutral Negative Seller Good increase decrease Mediocre decrease increase Bad decrease increase increase Buyer Good increase decrease Mediocre decrease Bad increase
  16. 16. Conclusions 1. We derived quantitative estimates of satisfaction => BIAS 2. We could extract information from silent transactions 3. Reciprocity in people’s online reporting behavior has an impact both on negative and positive feedback 4. General methodology that can be applied to a variety of bidirectional feedback mechanisms 16
  17. 17. Add silent feedback! 17
  18. 18. II PART Connections to PAD 703 materials 18
  19. 19. Bayes’ rule • Pr(feedback type| experienced outcome) • Pr(feedback type of 2nd mover | feedback type of 1st mover) 19
  20. 20. Sequential games (1/2) 20 Seller Buyer Buyer Buyer Buyer positive (+) neutral (0) silence (s) negative (-) + 0 - s + 0 - s + 0 - s + 0 - s +1 +1 0 +1 -1 +1 0 +1 +1 0 0 0 -1 0 0 0 +1 -1 0 -1 -1 -1 0 -1 +1 0 0 0 -1 0 0 0 p depends on the outcome of transaction p of reporting depends on the outcome of transaction AND on Seller first move 19,613 0 60 10,220 7 1 7 4 2 12 163 5,318 64 93 6,026 18 eBay score no. of auctions
  21. 21. Sequential games (2/2) 21 Buyer Seller Seller Seller Seller positive (+) neutral (0) silence (s) negative (-) + 0 - s + 0 - s + 0 - s + 0 - s +1 +1 0 +1 -1 +1 0 +1 +1 0 0 0 -1 0 0 0 +1 -1 0 -1 -1 -1 0 -1 +1 0 0 0 -1 0 0 0 9,303 0 12 5,318 0 31 93 4 2 31 93 10,220 163 93 6,026 4 eBay score no. of auctions
  22. 22. Reputation • Repeated play • Incentive to “cooperate” • Works in the long-run and rewards the most patient player 22 P2 Cooperate Defect P1 Cooperate 1, 1 -1, 2 Defect 2, -1 0,0
  23. 23. Reputation • High promised future gains from reputation => overcome short-term temptation to cheat • Supported by a trigger strategy cheating => bad reputation! • Not showing the whole history of received feedback => Incentive to keep on “cooperating” 23

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