The Evolution of Cooperation in Anonymous Online Markets

Transcription

1 The Evolution of Cooperation in Anonymous Online Markets An Empirical Investigation of the Process of Reputation Formation Andreas Diekmann, Ben Jann Wojtek Przepiorka, Stefan Wehrli ETH Zürich New Developments in Signaling and Game Theory Monte Verità, Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

2 Cooperation problems in anonymous online markets Main Interest: Study the functioning of real online auction markets here ebay Germany and analyze how sellers and buyers manage to establish cooperative exchange. Problem 1: How to trust your transaction partner on ebay? - Non-repeated interactions of anonymous actors - Asymmetric information ( Lemon Markets ) - Solution: Reputation system with reputation premiums Problem 2: Why do sellers and buyers submit (truthful) feedback? - Paradox of Voting - Feedback as collective good threatened by freeriding - Solution: Small propensities to leave feedback based on altruism or strategic behavior paired with strong reciprocal partner behavior. Online auctions as a ongoing natural experiment for the evolution of cooperation where repeated interactions ( shadow of the future / control effect) are replaced with reputation ( shadow of the past / learning effect). Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

3 What we do Based on a large sample of unobtrusive auction data obtained from ebay.de, we... 1 investigate the effect of positive and negative ratings (reputation) on sales and selling prices and (Reviews: Bajari & Hortacsu 2004, Resnick et al. 2006, this paper) 2 investigate traders motives to give feedback after a transaction. (Dellarocas and Wood 2008, Bolton et al. 2009, Jian et al. 2010) Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

4 Simplified Game Structure ➀ Transaction Stage ➁ Feedback Stage ➀ C D ➁ D C (R, R) (S, T) (P, P) ➀ C D ➁ ➁ C D C D (R, R) (S, T) (T, S) (P, P) Binary trust game where seller has second mover advantage. ➀ Buyer C: send money ➁ Seller C: ship advertised quality T > R > P > S Sequential PD Game with first (➀) and second (➁) mover. C: submit feedback D: don t submit feedback T > R > P > S Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

5 Simplified Game Structure Nature p 1 p ➀ ➀ C D C D ➁ ➁ C D C D (R, R + δ) (S, T) (P, P) (R, R) (S, T) (P, P) ➀ + ➁ ➁ + + a c, a c aγ c, a c c, a a c, aγ c aγ c, aγ c c, aγ a, c + ➁ aγ, c 0, 0 Transaction: Trust game with incomplete information T > R > P > S, δ > 0, 0 > p > 1 Feedbacks: Sequential Rating Game with a > c > 0, γ > 1 Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

6 Anonymous Online Market Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

7 Data Selected products out of a dataset consisting of 1.05 Mio auctions, observed on ebay.de between December 04 - January 05 (before introduction of new reputation system / Feedback 2.0). New mobile phones: Homogeneous, common value products. N=5499, mean end price = 215 Euro, 95% sold. Used mobiles phones: Heterogenous, common value products with higher quality uncertainty. N=7687, mean end price = 175 Euro, 95% sold. DVD Market: Heterogenous, common value products with low price. N= , mean end price = 7.4 Euro, 50% sold. Dependent variables: (1) probability of sale, (2) auction end price, (3) time to feedback. Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

8 Part I How does a reputation system help to solve trust problems in anonymous online markets? Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

9 Hypotheses Transaction Stage Building a good reputation is costly. Only sellers with a long-term business perspective will be compensated for their investment. Cheaters have no incentive to invest in reputation. Sellers with a short feedback history will be more likely to cheat. Sellers entering the market have to accept lower prices since buyers want to be compensated for higher uncertainty. H1 The number of positive (negative) ratings increases (decreases) the probability of a sale. H2 The number of positive (negative) ratings increases (decreases) the selling price. H3 The effects of negative ratings are stronger than the effects of positive ratings. H4 The reputation effects are stronger for used than for new products (due to higher uncertainty). Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

10 Probability of Sale Results: Logit of sales (0/1) New mobiles Used mobiles DVDs Seller s positive ratings (log).344 * (.136).019 (.064).117 ** (.040) Seller s negative ratings (log).670 * (.301).207 (.132).145 * (.058) Seller has private profile.476 (.865).532 (.593).583 * (.292) Seller has verified identity *** (.712).235 (.366).085 (.206) Seller has Me-Page.185 (.602).100 (.327).724 *** (.153) Seller is PowerSeller.024 (.775).126 (.419).203 (.165) Starting price.110 *** (.010).056 *** (.005).109 *** (.028) No picture.883 (.803).594 * (.276).009 (.155) Description length (log).423 ** (.159).047 (.078).115 *** (.028) Competition (log).880 (.677) *** (.279).656 *** (.111) Listed with thumbnail.026 (.385).280 (.175).692 *** (.150) Listed in bold (.679).432 (.289).731 * (.307) Constant *** (3.686) *** (2.018) *** (.983) Further covariates (Chi 2 -tests): Payment modes (4 df) 9.7 * ** Auction duration (4 df) *** End of auction (16/29 df) ** *** Product subcategory (6 df) 82.8 *** *** 65.5 *** McFadden R-squared Number of observations Number of sellers Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

11 OLS of log selling prices in EUR Results: OLS of log selling price in EUR New mobiles Used mobiles DVDs Seller s positive ratings (log).005 *** (.001).008 * (.003).053 *** (.015) Seller s negative ratings (log).011 *** (.002).018 ** (.006).101 ** (.035) Seller has private profile.019 (.011).018 (.020).027 (.103) Seller has verified identity.001 (.007).002 (.019).107 (.154) Seller has Me-Page.007 (.005).019 (.016).267 *** (.076) Seller is PowerSeller.006 (.012).000 (.034).020 (.118) No picture.024 ** (.009).044 * (.022).067 (.089) Description length (log).004 *** (.001).026 *** (.004).026 * (.011) Competition (log).018 ** (.006).037 * (.016).022 (.027) Listed with thumbnail.010 *** (.003).036 *** (.007).321 *** (.052) Listed in bold.006 (.004).032 ** (.010).521 *** (.081) Constant *** (.031) *** (.085) *** (.244) Further covariates (F-tests): Payment modes (4 df) 5.4 *** ** Auction duration (4 df) End of auction (16/29 df) 26.6 *** 7.6 *** 4.6 *** Product subcategory (6 df) *** *** 18.6 *** R-squared Number of observations Number of sellers Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

12 Part II What are the traders motives to participate in the feedback forum and to provide truthful ratings? Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

13 Hypotheses Feedback Stage I In a two-sided rating system, buyers can rate sellers and sellers can rate buyers after each transaction. Non-strategic (i.e. strong) reciprocity (Gintis 2000) H5: Traders inclination to submit a positive (negative) rating increases after receiving a positive (negative) feedback from their trading partner. Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

14 Hypotheses Feedback Stage II Other-regarding motives based on partner s reputation: Altruism (e.g. Becker 1976): H6a: The higher the seller s score, the lower the buyer s propensity to leave feedback. Signalling rating propensity (e.g. Gambetta 2009): H6b: The buyer s propensity to leave feedback is unaffected by the seller s score. Indirect reciprocity (e.g. Nowak and Sigmund 2005): H6c: The higher the seller s score, the higher the buyer s propensity to leave feedback. Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

15 Hypotheses Feedback Stage III Strategic motives: Retaliation threat vs. reciprocity trap: H7a: The higher a seller s score, the higher the seller s propensity to leave feedback first. Reciprocity expectations: H7b: The higher the buyer s score, the higher the seller s propensity to leave feedback first. Previous interaction: H7c: Traders are less likely to leave feedback if they received a rating from the same interaction partner in a previous interaction. Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

16 Feedback Timing Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

17 Feedback Patterns (in %) Evaluation Mobile phones DVDs Positive Feedbacks from Buyer Positive Feedbacks from Seller Timing Auctions with mutual feedback - buyer rated first seller rated first simultaneous Auctions with buyer feedback only Auctions with seller feedback only Auctions without feedback Total Observations Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

18 Hazards Results: of Feedback Hazards Provision of feedback (DVDs) provision (DVDs) Positive first move by partner (timedependent) Neutral first move by partner (timedependent) Negative first move by partner (time-dependent) Positive Feedback Negative Feedback Buyer Seller Buyer Seller *** (.112).410 (.732) (.327) Positive ratings (log) *** (.183) Negative ratings (log) (.508) Partner s positive ratings (log) *** (.137) Partner s negative ratings (log) *** (.259) Previous interaction rating: Received only (.459) Provided only *** (.428) Received and provided *** (.219) *** (1.013) *** (.461) *** (.461) (.234) (.258) (.269) (.755).943 (.596) * (.676) (.286).003 (.004) ** (.004).535 ** (.166) * (.012) * (.051).017 *** (.005) ** (.014) Received or provided.002 (.005) ** (.002).440 *** (.109) *** (.311) ** (.004) (.006).021 *** (.006) (.035).006 * (.003) Number of observations Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

19 Conclusions In online markets, anonymous buyers and sellers trade with each other over large geographic distances. Reputation systems help to solve trust problems by providing incentives for cooperative behaviour. Sellers invest in reputation, buyers pay a reputation premium. Traders voluntary provision of ratings is crucial for the effectiveness of a reputation system to deter cheaters. We find that non-strategic reciprocity and altruism motivate traders to rate their interaction partners. Strategic motives also seem to play a role, but they are difficult to identify with observational data. Open problems: Strategic and untruthful use of the rating system, building up fake reputations, retaliation with negative ratings,... Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

20 Thank you for your attention! Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

21 Descriptive Statistics I Data (1/4) Mobile phones All auctions Successful auctions DVDs All auctions Successful auctions Model (mobiles) or Genre (DVDs) Sony T610 / Action Sony T630 / Drama Nokia 6230/ Classics Nokia 6310i/ Comedy Motorola V600 / Crime Samsung E800 / Cartoon Samsung E700/ Children Number of observations Number of sellers Number of buyers Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24 10

22 Descriptive Statistics IIData (2/4) Mobile phones All auctions Successful auctions DVDs All auctions Successful auctions Auction: Selling price (Euros) (54.3) 7.4 (1.2) Starting price 27.0 (62.1) 18.8 (47.2) 3.7 (6.6) 2.6 (5.1) No picture Description length (log) 7.08 (1.14) 7.09 (1.14) 7.04 (1.36) 6.74 (1.43) Competition (log) 3.79 (.94) 3.80 (.94) 6.92 (.87) 6.84 (.90) Listed with thumbnail Listed in bold Payment modes offered: No bank transfer PayPal or credit card Cash on pick up Cash on delivery Auction duration: 1, 3, 5, 7, Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

23 Descriptive Statistics IIIData (3/4) Mobile phones All auctions Successful auctions DVDs All auctions Successful auctions Seller: Seller s positive ratings (log) 3.75 (1.60) 3.76 (1.59) 7.00 (2.65) 6.47 (2.45) Seller s negative ratings (log).45 (.70).44 (.68) 2.00 (2.01) 1.53 (1.85) Seller has private profile Seller has verified identity Seller has Me-Page Seller is PowerSeller Buyer: Buyer s positive ratings (log) 3.12 (1.78) 3.92 (1.69) Buyer s negative ratings (log).30 (.59).28 (.54) Buyer has private profile Buyer has verified identity Buyer has Me-Page Buyer is PowerSeller Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24

24 Descriptive Statistics IVData (4/4) Mobile phones All auctions Successful auctions All auctions DVDs Successful auctions Feedback: Time until seller feedback (censored) 35.0 (36.2) 24.9 (3.1) Positive seller feedback Neutral seller feedback Negative seller feedback Seller has previous rating from buyer Time until buyer feedback 37.1 (36.5) 25.2 (3.2) (censored) Positive buyer feedback Neutral buyer feedback Negative buyer feedback Buyer has previous rating from seller Diekmann et al. (ETH Zürich) Cooperation in Online Markets Monte Verità, / 24