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. 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. Agenda
• Background
• Dataset and descriptive statistics
• Methodology and results of 2 models
• Discussion
• Conclusions
4
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. eBay feedback mechanism
• Voluntary self-reporting of the outcomes
of the transactions
–Public report of private outcomes:
OUTCOME ====> FEEDBACK
• Bi-directional
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10. Type of feedback and relative order
25 combinations
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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. 1st model: simultaneous equations
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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. 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
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13. Estimation
• Maximum likelihood method
• Estimated probabilities of observing a given
outcome:
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Seller’s
outcome
Buyer’s
outcome
Good 88.6% 81.3%
Mediocre 10.4% 17.4%
Bad 0.1% 1.1%
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:
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Seller’s
outcome
Buyer’s
outcome
Good 85.6% 78.9%
Mediocre 13.7% 20.4%
Bad 0.6% 0.7%
15. 2nd model: adding feedback timing
2nd mover propensity to report given 1st mover
feedback
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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. 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
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22. Reputation
• Repeated play
• Incentive to “cooperate”
• Works in the long-run and rewards the
most patient player
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P2
Cooperate Defect
P1
Cooperate 1, 1 -1, 2
Defect 2, -1 0,0
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”
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Notes de l'éditeur
Who in this room has never bought something on eBay?
99 not reliable!!
Though our dataset is quite old, the habits have not changed much…
Here we consider all possible combination of each trader’s feedback type (positive, negative, neutral + slience) and all possibile temporal orderings of comments (buyer rates first, seller rates first). We found 25 different combinations.
A1: sort of a rationality assumption
A2: they truthfully reports the feedback type that correspond to the outcome observed
Limitation
Second mover reporting probabilities
Receiving positive (negative) feedback increases the propensity to post good (bad) feedback online
when buyer satisfied, 2nd mover will “return the favor”
when buyer mildly dissatisfied, he remains silent
when the seller is dissatisfied, he will “forgive” delinquent buyers in exchange for a positive rating
a trader’s decision to not post feedback
A decision no NOT post online feedback carries important information
silent transactions should become a standard part of a trader’s feedabck profile on eBay