willingness to pay for reputation : an experimental study on

Willingness to pay for Reputation : an experimental study on Feedback

Mechanism a preliminary version

Marianne LumeauCREM, Univ-Rennes1

David MascletCNRS-CREM, Univ-Rennes 1

and CIRANO (Montréal)

Thierry PenardCREM, Univ-Rennes 1

Willingness to pay for Reputation : an experimental study on Feedback

Mechanism a preliminary version

Marianne LumeauCREM, Univ-Rennes1

David MascletCNRS-CREM, Univ-Rennes 1

and CIRANO (Montréal)

Thierry PenardCREM, Univ-Rennes 1

The success of online markets constitutes a challenge for economists

– Anonymous traders– Geographical distance– Moral hazard :

Seller can :

– Misrepresent the good

– Not ship the good Buyer can not send the payment

Under the hypothesis of rational anticipation, transactions might not take place.

Current thinkingCurrent thinking

How can opportunistic behaviors be reduced?

On online market, some reputation mechanisms are generally implemented

Roles that reputation plays in the social interactions :– Informational role– Sanctioning role

Current thinkingCurrent thinking

A kind of a reputation mechanism is the Feedback Mechanism

Example of eBay’s “Feedback Forum” A decentralized system Rating can be positive, neutral or negative This rating is stored in the respective user’s rating

profile

Current thinkingCurrent thinking

A brief surveyA brief survey

Empirical studies show that Feedback Mechanism encourages the honest behaviors, as far as changing reputation is costly. (see Resnick et al. (2006) and Dellarocas (2006) for a survey of these studies).

A good Reputation positively affects : the probability of selling the good the good sell price the probability of individual bidders entering the actions the number of bidders the buyer’s subjective assessments of a seller’s

trustworthiness

A brief surveyA brief survey

But other empirical studies have shown that the feedback system is however far from being perfect.

The informational content of feeback profile are reduced by the fact that :

Agents can modify their reputation profile- change pseudonym- incentive and negotiation with their partner

strategic ratings (or non-ratings) (see Dellarocas et al. (2006), Klein et al. (2006)).

- Feedback Retaliation after a negative rating- Feedback Reciprocity after a positive rating- Strategic feedback timing

A brief surveyA brief survey

Experiences may be usefull to recreate in the lab the conditions of a Feedback Mechanism :

- Bolton and al. (2004) : a buyer and seller game + a 2d step of indirect and unilateral (towards seller) evaluating system

Results • the introduction of a reputation system increases the exchanges efficiency • but doesn’t allow reaching the existing efficiency levels in a repeated relationship.

Limits : moral hazard doesn’t come only from the fact that the seller doesn’t send the good when the buyer send the payment.

A brief surveyA brief survey

- Keser (2002) : a sequential trust game + a 2nd step of direct and unilateral (towards seller) evaluating system

ResultsAn evaluating system :

•improve the cooperation between players• increase the level profit (particularly for the 1st player)• reduce the seller dominant position in favor of the buyer

Limits 1.Online evaluating system are not unilateral but bilateral2. It’s costly in time, in attention, and in energy to evaluate

the partner

A brief surveyA brief survey

- Masclet and Penard (2007) : a simultaneous trust game + a 2nd step of direct, bilateral and costly evaluating system.

Results•Simple Internet-Based Reputation Systems have positive effects on cooperation• However too complex evaluating systems generate many strategic evaluations that may negatively affect cooperation.

Limits this game doesn’t take into account the effect of time in evaluation stage in the cooperation level

Research questionResearch question

The goal of this study is twofold :

First we make the game more realistic by introducing “Timing” effects in the evaluation decision In particular we investigate to what extent such timing effects may affect both evaluations and cooperation.

Second, we seek to investigate to what extent the Feedback Mechanism may suffer from agents’ manipulations, by allowing agents to costly change their reputation profile.

Experimental designExperimental design

Treatments STAGE 1 STAGE 2 STAGE 3

SEQ_ENDOSimultaneous trust game

Sequential and endogenous rating,  

  Rating Cost = 1 ECU  

REAL_TIMESimultaneous trust game Real time rating  

  Rating Cost = 1 ECU  

MODIF_LCSimultaneous trust game Real time rating

Manipulation on reputation

  Rating Cost = 1 ECUManipulation Cost = 1 ECU

MODIF_HCSimultaneous trust game Real time rating

Manipulation on reputation

  Rating Cost = 1 ECUManipulation Cost = 2 ECU

Experimental designExperimental designDecrease of time

Rating result second by second

Amount invest or return

Research questionResearch question

Conjecture 1: Real time rating may affect positively thetrust of the player A and the trustworthiness of the

player BIndeed it might be possible that introducing “real timing” may make

the evaluation system more efficient by inducing more emotional

content (hot vs cold)

Conjecture 2: One should observe more negative evaluations in the last seconds, by fear of retaliation. In contrast, one should expect more positive ratings in the first seconds in order to encourage reciprocity.

Research questionResearch question

Conjecture 3: Manipulations may have a direct negative effect on trust, because of the noise introduced in the feedback mechanism.

Conjecture 4: In contrast, it may induce a positive effect on cooperation by artificially increasing the number of positive ratings.

Conjecture 5: Increasing the cost of manipulations should improve (reduce) the efficiency of the system if the negative effects of manipulations are higher (lower) than positive ones.

Experimental designExperimental design

Sessions TreatmentsNumber of participants by

sessions1 to 6 SEQ_ENDO 60

7 to 12 MODIF_HC 6013 to 18 MODIF_LC 6019 to 24 REAL_TIME 5625 to 26 MODIF_LC 20

27 to 28 MODIF_HC 20

Total number of players   276

Parameters of the experience : • Computerized experience using Z-tree Software• Tested on student • From February to April 2008•In LABEX, University of Rennes 1• Average Payment : 15 euros +3 euros

Results : InvestmentsResults : Investments

Table 3 : Average levels of investment and of return according to the experimental treatments, expressed in ECU (standard error in parenthesis)

  SEQ_ENDO REAL_TIME MODIF_LC MODIF_HC

Investment of player A 3.02 4.14 3.81 4.1(3.10) (3.28) (3.55) (3.39)

Profits of player A 9.45 10.91 8.72 9.06(3.66) (4.47) (4.25) (4.45)

Investment of player B 2.76 5.37 3.01 3.69(4.68) (6.39) (5.05) (5.25)

     Return of investment 29.23% 39.41% 25.17% 29.39%

     Profits of player B 16.01 16.76 17.90 17.65

(7.21) (6.55) (8.44) (7.95)Observations 1200 1120 1600 1600

Results : RatingResults : Rating

Table 4. Detailed description of the rating choice

   Eval from A

to B B’s Invest  Eval from B

to A A’s Invest

SEQ_ENDO Non -evaluated 72,83% 2,2   72,67% 2,64

Eval négative 20,17% 1,93 10,50% 1,37

  Eval positive 7% 10,96   16,83% 5,69

REAL_TIME Non-evaluated 66,29% 4,52 68,75% 3,47

Eval négative 19,64% 2,92 12,50% 2,84

  Eval positive 11,07% 14,98   18,75% 7,49

MODIF_LC Non-evaluated 70,88% 2,87   82,88% 3,4

Eval négative 25,37% 1,8 7,75% 3,15

  Eval positive 3,75% 13,77   9,38% 7,93

MODIF_HC Non -evaluated 66,00% 3,36   73,00% 3,86

Eval négative 21,88% 1,59 10,13% 1,44

  Eval positive 12,13% 9,24   16,88% 6,72

Results : Investment and real timeResults : Investment and real time

Result 1 : Real time rating affects

positively player A’s trust and player B’s

trustworthiness, because of a higher

number of ratings (emotional effect).

Results : Rating in real timeResults : Rating in real time

Fig 3. Player A’s evaluation timing

Results : Rating in real timeResults : Rating in real time

Fig 4. Player B’s evaluation timing

Results : Rating in real timeResults : Rating in real time

Result 2 : Player B have a more

strategic behavior than player A, in extent

that he leave more negative ratings in the

last seconds, by fear of retaliation, and

more positive ratings in the first seconds,

in order to encourage reciprocity.

Results : Investment and reputation manipulationResults : Investment and reputation manipulation

Fig 5. Player A’s average invest : effect of changing reputation

Results : Investment and reputation manipulationResults : Investment and reputation manipulation

Fig 6. Player B’s average invest : effect of changing reputation

Results : Investment and reputation manipulationResults : Investment and reputation manipulation

Table 4 : Differences between received and final ratings

   Eval from

A to BFinal eval

of BGrowth

rate

Eval from B

to AFinal

eval of AGrowth

rate

TPS_REELNone-evaluated 66,29% 66,29% 0% 68,75% 68,75% 0%

negative eval 19,64% 19,64% 0% 12,50% 12,50% 0%

  Positive eval 11,07% 11,07% 0% 18,75% 18,75% 0%

MODIF_LCNone-evaluated 70,88% 70,63% -0,35% 82,88% 77,06% -7,02%

negative eval 25,37% 13,38% -47,26% 7,75% 6,44% -16,90%

  positive eval 3,75% 13% 246,67% 9,38% 16,50% 75,91%

MODIF_HCNone-evaluated 66,00% 63,88% -3,21% 73,00% 71,31% -2,32%

negative eval 21,88% 16,69% -23,72% 10,13% 9,31% -8,09%

  positive eval 12,13% 19,44% 60,26% 16,88% 19,38% 14,81%

Results : Reputation manipulationResults : Reputation manipulation

Table 5 : Frequences of Ratings manipulation by type and treatment

Conclusions and extensionsConclusions and extensions

Main conclusion : Player B (seller) seems to have more strategic behaviors than player A (buyer).

Extensions Sessions are very heterogenous, that explain the high level of standard errors New sessions will be realised Econometric tests will be conducted

Econometric tests will be conducted