Differences in “Truthiness” across Online Reputation Mechanisms1
Paul Chwelos
[email protected]
Tirtha Dhar
[email protected]
Sauder School of Business, University of British Columbia
2053 Main Mall, Vancouver, BC, V6T 1Z2, Canada
Phone: (604) 822-9956
1
Fax: (604) 822-0045
Principal authorship is not assigned. Authors are thankful for detailed from the seminar participants at the 2005
Marketing Science Annual Conference, Atlanta, and at the ZEW (Centre for European Economic Research)
workshop on ITC and firm strategies 2005, Mannheim, Germany.
Differences in “Truthiness” across Online Reputation Mechanisms
Abstract
Using a unique dataset from a specialty online retailer, we compare the reputation
mechanisms of Amazon and eBay. We develop a theoretical model of reputation mechanisms
based on the feedback processes at Amazon and eBay, and then validate this model by
comparing its predictions to the findings of existing research. We construct and estimate a
dynamic simultaneous equations model of the sales demand, feedback generation, and price
formation processes. Significant differences exist across the Amazon and eBay mechanisms in
that: (a) the propensity to leave feedback is higher on eBay than on Amazon, (b) negative
feedback is much less frequently observed on eBay than on Amazon, (c) feedback on Amazon
has a strong demand-inducing impact, raising prices and total sales, whereas (d) feedback on
eBay has no effect on demand. Overall, these results suggest that the Amazon reputation
mechanism provides more useful information to buyers than does the eBay mechanism, and that
these differences are largely due to the directionality of these mechanisms: eBay’s bilateral
system, which allows buyers to rate sellers and sellers to rate buyers gives way to strategic
considerations – reciprocity and retaliation – that do not plague Amazon’s unilateral system in
which only buyers rate sellers.
Keywords: E-commerce, Game Theory, Reputation, Online Marketplaces, eBay, Amazon,
Feedback
2
1.0 Introduction
Business-to-consumer (B2C) e-commerce in the United States hit $108.7 billion in 2006
and is growing at 23.5% per year.1 Online marketplaces such as eBay, which foster transactions
from consumer to consumer (C2C), continue to grow at an even faster pace: eBay alone
processed 2.4 billion auctions in 2006, which accounted for $52.5 billion changing hands and
earning eBay $5.97 billion in commissions, representing approximately 28% annual growth.2
While all purchases contain some risk, the level of risk is much higher in the online
context, where goods cannot be physically inspected and payments are not made at the site of the
transaction (Wolf and Muhanna 2005).
In such markets, the information asymmetry is
particularly intense, in that the seller knows much more about the quality of the goods and her
intent to fulfill the transaction than does the buyer (Bajari and Hortaçsu 2004). The seller would
like to be able to commit to behave in a trustworthy manner and thereby maximize the buyer’s
willingness to pay, but, having no credible or cost-effective way to do so in the online context,
the seller suffers the consequences of the resulting moral hazard (Dellarocas 2005). If these
problems are severe enough, total market failure can ensue through adverse selection as only
“lemons” (low quality goods/sellers) are available (Akerlof 1970).
In order to mitigate the buyer’s perception of risk, online retailers such as Amazon.com or
Priceline.com have invested heavily in building their reputations for safe, secure, and predictable
transactions. Meanwhile, electronic marketplace providers such as eBay, Yahoo, and Amazon
have emerged to facilitate transactions among independent buyers and sellers online; however,
these marketplace providers cannot directly guarantee the trustworthiness of the participants in
their markets. In order to overcome the information asymmetry and moral hazard problems, these
marketplaces use a variety of “reputation mechanisms” to allow buyers and sellers to leave
publicly visible feedback on each other’s performance in transactions. These feedback
mechanisms act as both a sanctioning device to curb moral hazard and a signalling device to
reduce information asymmetry (Dellarocas, 2005). For example, eBay allows each participant to
leave feedback for the other party in a transaction that is either positive (+1), neutral (0), or
negative (-1). The individual feedback items are aggregated into an index of the past behaviour
1
2
U.S. Census Bureau, Feb 16, 2007, http://www.census.gov/mrts/www/data/html/06Q4.html
EBay Earnings Release, Jan 27, 2007, http://investor.ebay.com/index.cfm
3
of market participants, forming, in effect, an online “brand equity” for each participant. An eBay
“score” is simply the sum of feedback received from unique users.
Despite the billions of dollars changing hands in these online marketplaces, surprisingly
little is known about the efficacy of online reputation mechanisms in countering the problems
described above; indeed, the design of reputation mechanisms was highlighted in a recent survey
of online auction research as the most important outstanding research question (Bajari and
Hortaçsu, 2004). The rapid growth of online marketplaces such as eBay suggests that these
mechanisms must “work” in some sense as there does not appear to be a strong case of market
failure; however, it is impossible to know how many consumers adopted and then exited these
online marketplaces – perhaps due to a bad experience or simply due to learning about the
marketplace – or simply elected not to play the “transaction game” as it is structured in these
marketplaces (Shugan 2005). Thus, it is possible that these marketplaces might have grown even
faster with “better” reputation mechanisms that had encouraged more buyers (and sellers) to play
the transaction game. Neither do the existing reputation mechanisms “work” in the sense of
removing all risks; indeed, online auctions have consistently been the most common source of
internet fraud reported to the National Consumer League, accounting for more complaints than all
other categories of internet fraud combined.3 Studies of the eBay marketplace consistently find a
very high proportion of positive feedback, suggesting that consumers appear to be highly
satisfied. However, recent research has shown that the eBay mechanism is biased toward positive
feedback (Dellarocas and Wood, 2006; Klein et al., 2006), and eBay itself continues to
experiment with and enhance its reputation mechanism.
During 2007, eBay rolled out its
“Feedback 2.0” system, which incorporates a number of significant changes, across international
markets, with deployment in the U.S. anticipated by the end of the year. Thus, the “design space”
of reputation mechanisms is a current issue of central concern to both researchers and
practitioners (Dellarocas, 2005).
The goals of this study are: (i) to provide an economic rationale for the observed
differences in the proportions of positive and negative feedback at Amazon and eBay, (ii) to
develop a better understanding of the feedback generation process, and (iii) to assess the impact
of feedback on market outcomes in these marketplaces. To address these, we first develop a
3
National Consumers League, “2005 Fraud Trend Report,” http://www.fraud.org/2005_fraud_trend_report.pdf;
Internet Crime Complaint Center, “IC3 2005 Internet Crime Report,”
http://www.ic3.gov/media/annualreport/2005_IC3Report.pdf.
4
theoretical model of the feedback generation processes on eBay and Amazon platforms. We then
compare insights from our theoretical model to the findings of existing research. Last, we
estimate the impact of feedback on market outcomes (in terms of sales and pricing) using a
unique database on the same seller on both the Amazon and eBay platforms after taking into
account the dynamics of these markets.4
Based on our game-theoretic model, we predict that, due to bilateral nature of the
feedback generation process at eBay, rational market participants will behave strategically,
providing either positive feedback or no feedback, regardless of the outcome of the transaction
that generated the feedback. Thus, the eBay feedback mechanism is biased against providing
negative feedback because forward-looking market participants leave feedback based on future
benefits and not on the (sunk) costs and benefits of the completed transaction.
We find that one of the implications of the bilateral eBay feedback mechanism being
strategic and not reflective of the underlying transaction is that the eBay reputation mechanism
will be less informative to market participants than a unilateral reputation mechanism. If this is in
fact the case, then we would expect feedback on eBay to have less impact on market outcomes
than feedback on the unilateral Amazon platform. We test this prediction regarding the
“truthiness" of the platforms using proprietary data for the same seller under both the eBay and
Amazon reputation mechanisms, and ask whether the feedback provided on these platforms
actually provides useful information for buyers in making purchasing decisions, or whether they
are simply a rosy “window dressing” provided to further the interests of marketplace providers
and sellers.5
Our findings regarding the effectiveness of feedback across platforms suggest that the
directionality of reputation mechanisms plays a critical role in determining the usefulness of these
mechanisms to market participants. Estimates of our structural econometric model show that
positive feedback on eBay has no effect on sales or prices whereas positive feedback on Amazon
has a significant effect on both sales and prices; that is, the reputation mechanism of Amazon
appears to “work” in the sense that it increases the trust of buyers, as evidenced by increasing
4
Our data on eBay feedback came from Half.com, the posted-price subsidiary of eBay.com. Half and eBay share the
identical reputation mechanism, and participants (buyers and sellers) can register at one site and transact at both sites.
Because Half, as the retail marketplace of eBay, uses the eBay reputation mechanism, we use the name of the parent
site (eBay) throughout this paper in order to and keep our discussion tractable and to avoid any confusion.
5
Stephen Colbert popularized the word “truthiness” on the debut episode of the Colbert Report; he adapts the word
truthiness – which entered the language at the beginning of the 19th century – to refer to claims made on the basis of
intuition or gut instinct without recourse to evidence, logic, or intellectual examination (Wikipedia, 2006).
5
quantities demanded, whereas the reputation mechanism of eBay simply has no effect on buyer
behaviour.
We attribute this difference to the fact that eBay uses a bilateral reputation
mechanism, in which strategic considerations (reciprocity and retaliation) introduce a reporting
bias.
Therefore, we conclude that Amazon’s system provides an unbiased signal of seller
trustworthiness that is more useful to buyers.
This study makes timely and important empirical contributions, as it is one of the first, if
not the first, to compare the effectiveness of reputation mechanisms across two online retail
marketplaces. It is also one of the first to examine the impact of directionality in the design
dimensions of reputation mechanisms, and provides empirical and analytical evidence that
unilateral reputation mechanisms are superior.6 Finally, we generalize earlier findings regarding
the eBay reputation mechanism from the auction context to the posted-price retail context,
verifying that feedback is strategic under both market conditions.
The remainder of the paper is structured as follows. In Section 2, we review the existing
research relating to the economic impact of online reputation mechanisms. We develop a gametheoretic model of the feedback generation process in Section 3 and derive four hypotheses. We
first explore the patterns in feedback observed in existing studies to support of our theoretical
model in Section 4. We then use a proprietary database from one of the largest online seller of
second-hand products to test our hypotheses; section 5 describes the empirical context and this
database. We develop a dynamic simultaneous equations model in Section 6 to capture the
relationship between feedback, sales, and pricing. We estimate this model in Section 7 and
conclude in Section 8 with a discussion of the design of reputation mechanisms and proposals for
future research.
2.0 Reputation Mechanisms in Online Marketplaces
In order to mitigate the transaction risks in electronic marketplaces, providers have
devised a number of mechanisms like escrow (e.g., escrow.com), secured online payment (e.g.,
PayPal, BidPay), participant screening (e.g., Bid4Assets, PropertyRoom), transaction insurance
or bonding (e.g., Amazon’s A-to-Z guarantee, Overstock.com’s Trusted Merchant program),
monitoring of listings (e.g., uBid), and user feedback and reputation mechanisms. Of these
approaches, reputation mechanisms remain, by far, the most commonly used and play a number
6
We examine differences in the granularity of the rating systems in Appendix 1.
6
of crucial roles in online marketplaces including building trust between traders, helping buyers
and sellers generate goodwill and any associated price premium, and helping to evolve online
trading norms of behaviour. As a result, reputation mechanisms help to avoid market failure in
online marketplaces due to adverse selection as well as moral hazard problems (Bajari and
Hortaçsu, 2004). Unlike other mechanisms, user feedback is voluntary and is usually provided by
buyers and sellers after the trades are completed with minimal intervention of the market
facilitator. As a result, current feedback and ratings have the potential to affect both buyers and
sellers in all of their future transactions.
More than a dozen papers have explored the economic impact of feedback mechanisms in
online marketplaces from an empirical perspective (for a recent literature review, see Bajari and
Hortaçsu, 2004 or Dellarocas 2005).7 Most of these studies were based on publicly available
feedback data “scraped” from eBay’s website, although some utilized field experiments at eBay.
Excepting Cabral and Hortaçsu (2006), most of the studies use reduced-form econometric
methodology to test the impact of a seller’s reputation on auction outcomes (e.g., the number of
bids, likelihood of sale, and closing prices).
Although the results of these studies are mixed, most studies find a significant relationship
between positive feedback and closing price (Dewan and Hsu, 2001; Kalyanam and McIntyre
2001; Ba and Pavlou 2002; Livingston 2002; McDonald and Slawson, 2002; Melnik and Alm,
2002; Lei 2005; Lucking-Reiley et al., 2005; Cabral and Hortaçsu, 2006;Houser and Wooders,
2006; Resnick et al., 2006) or the number of bids received (McDonald and Slawson, 2002).
Some studies also find an impact of negative feedback in lowering the closing price (Ba and
Pavlou 2002; Cabral and Hortaçsu, 2003; Lucking-Reiley et al, 2005), whereas most do not find
any effect of negative feedback. However, others find that positive feedback increases the
probability of a sale, but has no impact on closing price (Jin and Kato, 2002; Resnick and
Zeckhauser, 2002), and some find no effect of seller reputation at all (Eaton 2002; Schamel
2004). More recent research has documented the under-reporting of negative outcomes on eBay,
demonstrating that unhappy buyers are more likely to simply leave no feedback at all than
negative feedback (Dellarocas and Wood 2006).
7
There also exists another stream of research that explores reputation mechanisms in terms of their algorithmic
components, largely from the perspective of computer science. We do not explore this research in this study, but an
online bibliography is available at: http:\\databases.si.umich.edu\reputations\bib\bib.html.
7
While these studies contribute greatly to our understanding of online marketplaces, taken
as a whole, the existing literature suffers from two major shortcomings. First, all of the studies
reviewed use only data from eBay. However, the eBay reputation mechanism is just one of many
possible designs, and it differs from the mechanisms used at other sites, including Amazon and
Yahoo. As a result, little is known about the empirical effects of reputation mechanisms outside
of eBay’s particular design. Importantly, the eBay mechanism is a bilateral mechanism that
allows both buyers to rate sellers and sellers to rate buyers, whereas Amazon is unilateral in that
only buyers can rate sellers. As a result, incentives to leave feedback (and the consequences of
leaving feedback) are very different at these two sites. There are other differences in the rating
mechanisms across different sites, such as the granularity of the rating scales used (3-point for
eBay and 5-point for Amazon), the timeframe participants have to leave feedback, whether
negative feedback can be mutually withdrawn, and the particulars of the metrics (e.g., total score,
average score or percent positive, breakdowns of feedback by time, role, and type) presented to
participants. Confining research to only one reputation mechanism creates a major limitation in
that the impacts of the various dimensions of reputation mechanisms cannot be disentangled. Our
discussions with buyers and sellers on different platforms indicate that significant differences in
feedback behaviour exist across the eBay and Amazon sites; our research will address this
limitation by comparing the eBay and Amazon reputation mechanisms.
This existing research focus solely on the eBay reputation mechanism is especially
problematic given that the extant literature highlights a disproportionate quantity of positive
versus negative feedback on eBay. This apparent bias toward positive feedback raises concern as
to the accuracy and effectiveness of eBay feedback mechanism. For example, Resnick and
Zeckhauser (2001) found that only 0.6% of the feedback provided by buyers was negative;
similarly, Bajari and Hortatscu (2004) found only 0.43% negative feedback. It has been suggested
that the unusually high rate of positive feedback observed on eBay is a result of the bilateral
design of eBay’s reputation mechanism. Particularly, under the bilateral system, a reporting bias
may arise due to reciprocity and retaliation effects (Dellarocas and Wood, 2006; Klein et al.,
2006). For example, Cabral and Hortatcsu (2006) find that a buyer leaving negative feedback has
a 40% probability of also receiving negative feedback by the seller; this apparent “tit-for-tat”
strategy used by sellers dramatically undermines the incentive of buyers to leave negative
feedback. A recent survey of eBay participants finds that this practice is very prevalent: 68% of
8
eBay users surveyed had experienced retaliatory feedback (Steiner, 2007). As a result, there is
strong evidence that negative feedback is under-provided on eBay, and that the number of
“silent” transactions (i.e., transactions where no feedback is posted) can potentially be interpreted
as an indirect measure of negative experiences (Dellarocas and Wood, 2006). We therefore aim to
increase the breadth of the literature by comparing reputation mechanisms that vary in terms of
directionality to assess whether and how the directionality of feedback mechanisms impacts
market outcomes.
The second limitation of existing studies is that they explored the role of online reputation
mechanisms only in the context of auctions. However, we know that consumer behaviour tends to
be different in the traditional fixed-price retail context versus auctions. For example, Ku,
Malhotra, and Murninghan (2003) suggest that consumers’ valuation of products can be
influenced by the online auction process, particularly the timing and increment of bids from other
sellers. Confining research solely to the auction context again creates a shortfall of knowledge,
especially considering that auctions account for less than 30% of total e-commerce sales to
consumers (Department of Commerce, 2005).
Our research will address this shortfall by
empirically examining the impact of reputation mechanisms in the retail context.
Because of these shortcomings in the current literature, we believe that a number of the
“general” findings regarding online reputation mechanisms are, in fact, idiosyncratic to the eBay
mechanism and the auction context.
Our research will begin exploration of the impact of
different dimensions of reputation mechanism design of the efficacy of these mechanisms. In this
study, we focus on directionality, although we present our findings on scale granularity in
Appendix 1.
3.0 Theoretical Model and Pattern of Feedback in Empirical Studies
The bidirectional nature of eBay feedback implies that the feedback generation process on
eBay can be modeled using game theory because it meets the conditions of a strategic game: it
has two or more players/agents (a buyer and a seller), strategies (leaving or not leaving feedback),
and associated payoffs. On the other hand, the unidirectional nature of the Amazon feedback
process cannot be modeled as a game due to the existence of only one player/agent (the buyer) in
the process. We first develop a model to explore the eBay feedback generation processes.
9
Previous studies of feedback in the eBay marketplace have documented both a reciprocity
and retaliation effect (Resnick and Zeckhauser, 2002; Dellarocas, Fan, and Wood, 2003; Bolton,
Katok, and Ockenfels, 2004; Cabral and Hortaçsu, 2006; Dellarocas and Wood, 2006; Klein et
al., 2006). A reciprocity effect exists when, after a transaction, player 1 has an incentive to leave
positive feedback for player 2 – perhaps even regardless of 1’s actual satisfaction with the
transaction – in the hopes that 2 will reciprocate with positive feedback for 1. In this case, both
parties “win” in that they build their overall reputation score and percentage of feedback that is
positive, making both 1 and 2 look like more trustworthy eBay participants thereby increasing
returns from future transactions. Interestingly, this implies that the “feedback game” on eBay can
be separated from the “transaction game” in that the feedback provided need not be associated
with the outcome of the transaction game, as is often assumed in other research. We expand on
this assertion below.
The “transaction game” on eBay can be thought of as a one-shot prisoner’s dilemma
game. Sellers in a particular transaction choose to either “cooperate” (provide high-quality goods
or service at a higher cost to the seller) or “defect” (provide low-quality goods or service, perhaps
including outright fraud or failure to deliver, at lower cost to the seller). Winning bidders
(buyers) simply choose to pay or not pay; if they don’t pay, the seller will not ship the product.
Since defecting is always a dominant strategy in a one-shot prisoner’s dilemma, eBay introduced
its feedback system to overcome this moral hazard problem. The feedback system provides a
mechanism whereby participants who have not had dealings with a particular participant can
learn from the past experiences of others with that participant. This history of interactions
provides an incentive for participants to cooperate even in one-shot games because their
behaviour will be revealed to all future participants (Berg, Dickhaut, and McCabe, 1995; Fan,
Tan, and Whinston, 2005; Dellarocas, 2005). Since repeated interaction between the same
participants is rare on eBay due to the sheer volume of participants, this amounts to most
participants cooperating most of the time, although a binary reputation mechanism may not
completely eliminate the incentive to defect with some positive frequency (Bajari and Hortaçsu,
2004; Fan, Tan, and Whinston, 2005).
Stated differently, once the transaction is completed but before the feedback is provided,
all the costs and benefits of the transaction are internalized or “sunk” since eBay sellers do not
typically provide a mechanism for returns or refunds. As a result, a rational, forward-looking
10
participant would treat the decision of whether and what type of feedback to leave on the basis of
the future outcomes of leaving feedback, rather than on the past events (the “transaction game”).
We term the decision of what (if any) feedback to leave after a transaction the “feedback game,”
and analyze it independently of the transaction game. Because the focus of this paper is on the
antecedents and consequences of feedback under the eBay and Amazon reputation mechanisms,
we leave aside the transaction game to concentrate on the feedback game.
By design, the eBay mechanism only ever uses one unique piece of feedback by a market
participant on another participant in calculating reputation score (or percent positive). Once a
transaction is complete, either participant may leave feedback for the other and neither participant
may change her feedback after it is left. For now, assume that both players leave feedback. Since
reputation on eBay has positive value, participants want to receive positive feedback and avoid
negative feedback (Dewan and Hsu, 2001; Kalyanam and McIntyre 2001; Ba and Pavlou 2002;
Livingston 2002; McDonald and Slawson, 2002; Melnik and Alm, 2002; Lei 2005; LuckingReiley et al., 2005; Cabral and Hortaçsu, 2006; Houser and Wooders, 2006; Resnick et al., 2006).
We label player B the buyer and player S the seller. We first model the game as singleshot simultaneous move 2 × 2 game.8 Based on the empirical and theoretical literature to date, we
assume that positive feedback will improve the seller’s reputation and generate positive returns
either in the form of higher prices due to an increased willingness of buyers to pay to buy from
this seller or in the form of higher unit sales due to an increased willingness of buyers to transact
with this seller. In the case of the buyer, positive feedback and a better reputation can lead to an
increasing willingness of sellers to transact with this buyer (some sellers restrict their auctions to
bidders who have more than a minimum threshold of positive feedback).9
Let ± β S be the seller’s per-period net future benefit from the positive/negative feedback
received from the buyer. Similarly, let ± β B be the buyer’s per-period net future benefit from the
(positive/negative) feedback received from the seller. Depending on the valence (positive or
negative) of the feedback the net future benefit can either be positive or negative. In addition to
8
Here, we follow the convention of previous research (e.g., Resnick and Zeckhauser, 2001; Bhattacharjee and Goel,
2005; Cabral and Hortaçsu, 2005; Dellarocas 2005) of treating eBay feedback as binary, either positive or negative.
While eBay allows “neutral” feedback, the empirical evidence is that neutral and negative feedback are both treated
by participants as unfavorable, and (Dellarocas 2005) shows that in the case of pure moral hazard, a reputation
mechanism of any granularity can be treated as a binary mechanism consisting of a “good” and “bad” set of ratings.
9
In addition, many buyers on eBay are also sellers, and since the eBay reputation mechanism does not differentiate
between feedback earned as a buyer from feedback earned as a seller, the reputation earned as a buyer also provides
benefit as a seller.
11
feedback received affecting future payoffs, it appears that feedback provided can also influence
future payoffs. Trade journals and anecdotal evidence suggest that the eBay community also
assesses participants’ decisions to provide feedback; in particular, those leaving positive feedback
are judged to be “good eBayers” and those leaving negative feedback are considered
“uncooperative” or risky to do business with. Thus, the decision whether to leave feedback (and
of what type), in addition to feedback received, will influence the net future utility generated from
the transaction. We assume that positive/negative feedback provided will make a player more/less
attractive to transact with. Without any loss of generality, let γ j be the percentage gain/loss from
providing positive/negative feedback provided to the other player.
Let p, c, V , E j be the price of the product, the acquisition cost to the seller, the valuation
of the product by the consumer, and the cost of effort needed to provide feedback by the buyer or
seller. Here we assume that the process of leaving feedback after a transaction imposes at least a
small effort cost on the participants.10 Now, when both the buyer and the seller provide positive
feedback (and are planning to stay in the market for the foreseeable future) then total payoffs can
be specified as:
For the buyer: U +B,+ = (1 + γ B )
For the seller: U +S,+ = (1 + γ S )
βB
− p + V − EB
1 − rB
βS
1 − rS
+ p − c − ES
Where, rj (j=B, S) is the discount rate of the players.
Similarly, when the buyer and the seller both provide negative feedback then the payoff will be:
For the buyer: U −B,− = − (1 + γ B )
For the seller: U −S,− = − (1 + γ S )
βB
1 − rB
βS
1 − rS
− p + V − EB
+ p − c − ES
When the buyer provides positive feedback and the seller provides negative feedback then the
payoffs will be:
10
Since the feedback processes on eBay and Amazon are roughly equivalent in terms of effort (in both cases, the
participant must log into the website, select the transaction, choose their feedback type, and leave a short comment),
we treat the effort cost the same on both platforms.
12
For the buyer: U +B,− = − (1 − γ B )
For the seller: U +S,− = (1 − γ S )
βB
1 − rB
βS
− p + V − EB
+ p − c − ES
1 − rS
When buyer provides negative feedback and the seller provides positive feedback then the payoff
will be:
For the buyer: U −B,+ = (1 − γ B )
βB
− p + V − EB
1 − rB
For the seller: U −S,+ = − (1 − γ S )
βS
1 − rS
+ p − c − ES
In a 2 × 2 normal form this game can be expressed as:
Player S (Seller)
The Feedback Game
Positive Feedback
(1 + γ B )
Positive
Feedback
Player B
(Buyer)
1 − rS
(1 + γ s )
(1 − γ B )
Negative
Feedback
βB
− (1 − γ B )
+ p − c − ES
(1 − γ B )
− p + V − EB ,
− (1 + γ B )
+ p − c − ES
− (1 + γ S )
1 − rS
1 − rB
− (1 − γ S )
βB
− p + V − EB ,
βS
βB
Negative Feedback
βS
1 − rS
+ V − p − EB
1 − rB
βB
1 − rB
− p + V − EB
βB
1 − rB
βS
1 − rS
− p + V − EB ,
+ p − c − ES
Proposition 1: (Positive feedback, Positive feedback) is the unique dominant strategy equilibrium
of the feedback game.
Proof: The proof is straightforward; the (Positive, Positive) strategy generates the highest payoffs
for the both buyer and seller.
The best outcome for a player is receiving positive feedback while also leaving positive
feedback, since this not only enhances the player’s reputation directly, but also signals to the
eBay community (i.e., other eBay participants) that the player is cooperative and likely to leave
positive feedback in the future. The unique dominant strategy equilibrium also implies that this is
also a sub-game perfect equilibrium irrespective of who initiates the feedback generation process.
13
Corollary 1: The solution to the 2 × 2 feedback game does not depend on the price paid, cost of
acquisition of the good for the seller, valuation by the consumer, or effort level.
Proof: In case of seller, p − c − ES is the common element under both strategies (positive or
negative). Similarly for the buyer, − p + V − EB influences payoffs under both strategies.
So far we have only considered the situation in which both players leave feedback. It is
possible to extend the feedback game to include “silence,” i.e., the option to not leave feedback.
Indeed, recent research has highlighted the use of silence as an alternative to negative feedback
(Dellarocas and Wood, 2006).
To simplify the exposition we modify the payoff notations such that the future payoffs
from feedback received are
are γ S
βS
1 − rS
= RS and γ B
βS
1 − rS
βB
1 − rB
= PS and
βB
1 − rB
= PB , the future payoffs from feedback provided
= RB , and the net profits/benefits from the transaction are
p − c = π and V − p = υ . In this 3 × 3 game the payoff matrix can be expressed as:
Player S (Seller)
The Feedback Game
with Silence
Positive
Feedback
Player B
(Buyer)
Negative
Feedback
Silence
(No Feedback)
Positive Feedback
Negative Feedback
Silence
(No Feedback)
PB + RB + υ − EB ,
− PB + RB + υ − EB ,
RB + υ − EB ,
PS + RS + π − ES
PS − RS + π − ES
PS + π
PB − RB + υ − EB ,
− PB − RB + υ − EB ,
− RB + υ − EB ,
− PS + RS + π − ES
− PS − RS + π − ES
− PS + π
PB + υ ,
− PB + υ ,
RS + π − ES
− RS + π − ES
υ ,π
If RB > EB and RS > ES then there are three pure strategy Nash equilibria in this game:
(Positive feedback, Positive feedback), (Positive feedback, Silence) and (Silence, Positive
feedback). If RB < EB and RS < ES then there are two pure strategy Nash equilibria: (Positive
feedback, Positive feedback) and (Silence, Silence). If RB = EB and RS = ES then (Positive
feedback, Positive feedback) is the only Nash equilibrium based on concept of iterative
14
dominance. We can also consider two other scenarios. If RB > EB and RS < ES then there will be
two Nash equilibria: (Positive feedback, Positive feedback), (Positive feedback, Silence). On the
other hand, if RB < EB and RS > ES then the pure strategy Nash equilibria will be (Positive
feedback, Positive feedback) and (Silence, Positive feedback). Note that even in this game
(Negative feedback, negative feedback) is not a Nash equilibrium.
We again note that, as a policy, eBay only uses one feedback between each pair of
participants in calculating a reputation score. This policy is a way for eBay to prevent “ballot
stuffing,” in which a participant builds a high reputation by accruing many positive feedback
from a small number of transaction partners, possibly confederates. As a result of this policy,
players in the feedback game do not interact repeatedly, and so the Folk Theorem cannot be
applied and it is, therefore, not possible to sustain any specific pure strategy Nash equilibrium
using a grim-trigger strategy. For the same reason, we do not explore mixed strategy equilibrium
as the existence of such equilibrium rely on repeated interactions.
As mentioned earlier, the unilateral feedback system on Amazon causes the game
described above for the eBay platform to degenerate since only the buyer can provide feedback.
As a result, Amazon should suffer neither the reciprocity nor retaliation biases inherent in eBay’s
bilateral system. Using the same notation as in the eBay game, we can express the payoffs of a
typical Amazon buyer as υ (in the case of silence) and υ − EB (in the case of positive or negative
feedback by the buyer).11 On Amazon, silence dominates feedback unless EB ≅ 0 , in which case
the buyer will be indifferent between providing feedback or remain silent. Given that in empirical
data we observe significant numbers of feedback on the Amazon platform, we believe that the
cost of providing feedback may be close to zero.
The differences in directionality of the Amazon and eBay mechanisms, as reflected in the
feedback games described above leads us to make the following hypotheses about feedback on
the two platforms:
H1 (Reciprocity Effect): A bilateral rating system (eBay) will result in much lower proportion of
negative feedback than a unilateral rating system (Amazon) due to strategic considerations and
the possibility of negative feedback in retaliation.
11
Note that Amazon provides no mechanism by which sellers can examine the history of feedback provided by a
buyer, so it is not possible for a buyer to curry favor with the sellers by providing positive feedback.
15
H2 (Directionality Effect): A bilateral rating system (eBay) will result in a higher propensity to
leave feedback than in a unilateral rating system (Amazon) due incentives to build reputation (i.e.,
to elicit feedback in return).
One of the implications of our game theory model of eBay feedback is that consumers
will come to recognize the strategic incentives (reciprocity and retaliation) inherent in the
bilateral eBay reputation mechanism. As a result, we predict that positive feedback will have a
differing impact on demand across the eBay and Amazon platforms. In particular, since the vast
majority of feedback observed under the eBay reputation mechanism is positive, we expect a
“ceiling effect” to arise, in which feedback on eBay affects demand less than feedback on
Amazon. This prediction is one of the implications of our model: once eBay participants
recognize that feedback is a strategic result of the feedback game, rather that a true reflection of
the outcome of the transaction game, then participants will discount positive feedback observed
on eBay in making their decisions. Likewise, since nearly all feedback observed on eBay is
positive (i.e., approaching the ceiling of 100% positive), there is very little information provided
by the eBay reputation mechanism. Therefore:
H3 (Ceiling Effect): Buyers using the eBay mechanism will be less sensitive to positive
feedback than buyers using the Amazon mechanism.
As discussed, feedback on the eBay platform will not necessarily relate to the underlying
transactions; rather, participants will provide feedback based on expectations of future returns to
leaving feedback. In particular, eBay participants will be unlikely to leave negative feedback
even after a negative experience in the transaction game since they will be strategically looking
forward to the results of their actions in the feedback game. On the other hand, Amazon
consumers will have no incentive to be strategic, and, as a result, any feedback given on Amazon
will reflect the underlying experiences with the transaction, be they positive or negative.
H4 (Strategic Feedback Effect): Buyers on the Amazon platform will be more likely to leave
negative feedback as a result of an error or return than buyers on the eBay platform.
Note that the theoretical models we presented assumed fully rational economic agents. As a
result, these models do not take into account emotional or other behavioural aspects of the
16
feedback decision processes. It is probable that at least some consumers are not perfectly rational,
and fail to separate the transaction game from the feedback game. We would expect these
consumers to be driven by a notions or feelings of fairness, anger, gratitude, or frustration with
the other party in the transaction, and perhaps to leave feedback that is not optimal in the sense of
maximizing future payoff but may provide the consumer an immediate intrinsic payoff from
sharing information about her experiences, possibly due to the satisfaction of rewarding a good
seller or the thrill of punishing a bad seller, or even due to the “warm glow” of altruistically
sharing information with other buyers. This “emotional gratification” of leaving feedback is
another possible explanation for the observed feedback on Amazon. Our objective in this paper
is to build a model of the feedback processes between rational market participants that requires
the minimum set of assumptions, but this does not imply that we completely discount the role of
non-economic factors such as emotional involvement in the feedback processes. We will examine
whether the price of the good has any impact on the propensity to leave feedback, even though
the cost (price) of the good is sunk and therefore irrelevant to the feedback game.
4.0 Feedback Patterns in Existing Empirical Studies
We examine the data published in three previous studies of feedback on eBay: Resnick
and Zeckhauser (2002, hereafter RZ), Dellarocas and Wood (2006, hereafter DW), and Klein et
al. (2006, hereafter KLSS). RZ find in their sample of 15,228 eBay transactions where both
participants leave feedback that positive first feedback is reciprocated with positive feedback
99.87% of the time. Similarly, DW found that 99.80% of positive feedback is reciprocated in
their sample of 29,139 transactions. Using a larger and more recent sample of eBay transactions
(1,744,850 observations with feedback from both participants), KLSS find that 99.44% of
positive feedback is reciprocated with positive feedback. In all studies, it is extremely unlikely for
positive first feedback to receive negative feedback in return (0.13% in RZ, 0.20% in DW, and
0.56% in KLSS). The conditional probabilities (conditional on player B’s action) in these three
studies are given by:
17
The Feedback Game:
Observed Conditional Probabilities12
Player S (Seller)
Positive Feedback
Negative Feedback
Positive Feedback
99.87% (RZ)
99.80% (DW)
99.44% (KLSS)
0.13% (RZ)
0.20% (DW)
0.56% (KLSS)
Negative Feedback
37.93% (RZ)
33.33% (DW)
6.22% (KLSS)
62.07% (RZ)
66.67% (DW)
93.78% (KLSS)
Player B
(Buyer)
On the other hand, if for whatever reason, player B leaves negative feedback first, it is
likely that player S will “retaliate” with negative feedback both as a “punishment” of player B
and as an attempt to preserve S’s reputation disputing the veracity of B’s negative feedback.
Again, the empirical results support this supposition: RZ find negative first feedback is retaliated
against 62.07% of the time, with DW and KLSS finding 66.67% and 93.78% respectively. It is
unlikely for negative first feedback to be responded to with positive feedback: the figures are only
37.93%, 33.33%, and 6.22% for RZ, DW, and KLSS.
Looking at the overall observed pattern of feedback (the unconditional probabilities of the
outcomes), the (positive, positive) equilibrium occurs in more than 99% of all transactions where
both parties leave feedback. These empirical outcomes are consistent with the predictions of the
feedback game developed above.
The Feedback Game:
Observed Unconditional Probabilities13
Player S (Seller)
Positive Feedback
Negative Feedback
Positive Feedback
99.30% (RZ)
99.09% (DW)
98.96% (KLSS)
0.12% (RZ)
0.20% (DW)
0.56% (KLSS)
Negative Feedback
0.22% (RZ)
0.23% (DW)
0.03% (KLSS)
0.35% (RZ)
0.47% (DW)
0.45% (KLSS)
Player B
(Buyer)
12
Conditional probabilities are conditional on Player A’s move; as such, probabilities sum to 1.0 in each row
(excepting rounding error).
13
Unconditional probabilities measure the overall observed behavior in the marketplace; as such, probabilities sum to
1.0 across the entire matrix for each study (excepting rounding error).
18
Only two empirical studies report data on transactions where neither party leaves
feedback: RZ and DW. The observed probabilities in these studies, conditional on the buyer’s
action, are given below.
The Feedback Game with Silence:
Observed Probabilities Conditional
on Buyer Rating First or Silent
Player B
(Buyer)
Player S (Seller)
Positive
Feedback
Negative
Feedback
Silence
(No Feedback)
Positive
Feedback
74.46% (RZ)
63.55% (DW)
0.02% (RZ)
0.13% (DW)
25.46% (RZ)
36.33 % (DW)
Negative
Feedback
19.57% (RZ)
9.87% (DW)
19.57% (RZ)
19.73% (DW)
60.87% (RZ)
70.40% (DW)
Silence
(No Feedback)
36.61% (RZ)
62.14% (DW)
1.96% (RZ)
1.23% (DW)
61.44% (RZ)
36.64% (DW)
The conditional probabilities suggest that a buyer’s positive feedback will most likely be
met with positive feedback or silence, with negative feedback being very unlikely. Likewise,
negative feedback is most likely to be met with silence, followed by negative feedback with
positive feedback being least likely. Finally, silence from the buyer is most likely to be met by
silence or positive feedback, with negative feedback being very unlikely.
The pattern of results is very similar for the unconditional probabilities presented below.
Overall, the four equilibria predicted by the feedback game with silence correspond to the four
most frequently observed outcomes; in the unconditional model, these accounts for 98.58% out
outcomes in DW and 98.42% of outcomes in RZ. The incidence of negative feedback is
extremely low; buyers leave negative feedback only 0.44% (RZ) to 0.71% (DW) of the time, and
receive negative feedback only 0.85% (DW) to 1.20% (RZ) of the time. The extremely low
incidence of negative feedback on eBay is consistent with the feedback game as we describe it.
This finding is also consistent with participants choosing to use silence as a signal of
dissatisfaction with the transaction that avoids the possibility of retaliation (Dellarocas and Wood,
2006).
19
The Feedback Game with Silence:
Observed Unconditional Probabilities
Player B
(Buyer)
Player S (Seller)
Positive
Feedback
Negative
Feedback
Silence
(No Feedback)
Positive
Feedback
32.25% (RZ)
29.71% (DW)
0.01% (RZ)
0.06% (DW)
11.03% (RZ)
16.98% (DW)
Negative
Feedback
0.09% (RZ)
0.07% (DW)
0.09% (RZ)
0.14% (DW)
0.27% (RZ)
0.50% (DW)
Silence
(No Feedback)
20.59% (RZ)
32.64% (DW)
1.10% (RZ)
0.65% (DW)
34.55% (RZ)
19.25% (DW)
Considering effort effects, we see that buyers leave feedback 43% (RZ) to 47% (DW) of
the time, electing not to leave feedback the majority of the time (56% RZ, 53% DW). This
finding suggests that, for the majority of eBay buyers, RB < EB . On the other hand, 53% (RZ) to
62% (DW) of sellers leave feedback, indicating that, on average RS > ES for eBay sellers. From
this, we conclude that sellers have either lower effort costs or higher payoffs from leaving
positive feedback than do buyers.
Overall then, we conclude that existing research is supportive of our model, in that
approximately 98.5% of observed outcomes correspond to the Nash equilibria predicted by the
feedback game.
We interpret the other 1.5% of feedback as representing the actions of
boundedly rational participants, participants who didn’t understand the rules of the game, or
participants who simply made an error in leaving feedback (i.e., meant to leave positive feedback
but left negative feedback by mistake). That our relatively simple model of the feedback process
is able to correctly predict 98.5% of observed behaviour is, in our view, strong validation of our
model. We now turn to describing our data sources and compare the efficacy of the eBay and
Amazon reputation mechanisms by testing hypotheses H3 and H4.
5.0 Data Sources
We use a proprietary database from one of the largest online sellers of used books in order
to explore the differing effects of unilateral vs. bilateral feedback mechanisms. Books, along with
CDs, DVDs, and consumer electronics are among the leading categories of goods purchased over
the Internet, and thus provide a representative perspective from which to study online purchase
behaviour. Used goods have the added advantage of providing more variability in quality and
20
value than do new items, which makes information asymmetry, risk, and therefore reputation
even more salient.
We utilize this context because a fruitful collaboration has been established with one of
the largest online second-hand booksellers, UsedBooks.com, an internet-only marketplace for
rare and used books located in North America.14 UsedBooks.com has been operating since the
late 1990s and maintains its own website at which individual sellers can list their own
inventories; in addition, UsedBooks.com re-lists its sellers’ inventories under the UsedBooks.com
ID on a number of other electronic marketplaces, including Amazon.com and Half.com, the
posted-price retail subsidiary of eBay.com.15 In 2004, UsedBooks.com listed more than 2,500
sellers as customers, and UsedBooks.com’s revenues were more than US$ 10 million.
UsedBooks.com competes with other online book marketplaces such as Alibris, as well as with
book retailers, both online and offline. As part of an ongoing collaborative project
UsedBooks.com has provided access to proprietary transaction data, which provides us weekly
aggregate nominal and unit sales and data on returns related to transactions.
We augmented this database by collecting data from two other sources. Feedback data
were captured by the existing reputation mechanisms of eBay and Amazon.com for the
UsedBooks.com profile. These transaction-level feedback data are publicly available on the eBay
and Amazon.com websites, and were “harvested” over time using web robots. In total, 134,549
pieces of unique feedback were captured. Web traffic data were obtained from Alexa.com, which
tracks web visits the top 100,000 websites at any point in time. Alexa publishes the ranks of these
websites in terms of total web traffic, which we convert into estimates of page views for the
platforms by extrapolating the current views and ranks to earlier periods.
To create a unified database for further analysis, we first aggregate the transaction-level
feedback data by week and then match that data with the transaction data. This transformation
provides us with 82 weekly data points across the two platforms (with 41 data points for eBay
and 41 data points for Amazon). The average prices and distribution of feedback across
transactions is described in Table 1, and other characteristics of the two platforms are summarized
in Table 2. In terms of the volume of sales, the Amazon platform sells significantly more books
14
The name of the company has been changed in order to preserve the confidentiality of financial information and
internal operational details.
15
Following the norm we have used until now to identify source of the feedback we will refer to transactions on
Half.com by its parent eBay.
21
than the eBay platform. This statistic is not surprising as in retailing Amazon dominates the rest
of the players in the book market. In terms of average price, the average price on Amazon is
higher than on eBay, although the difference is not significant at the 5% level.
In terms of feedback, as mentioned above, the key difference between these mechanisms
is the directionality of feedback.16 Under the eBay mechanism, buyers rate sellers, but sellers
also rate buyers, and buyers know this; our theoretical model and literature review suggest that
strategic considerations will emerge under such a bilateral mechanism. We begin by exploring
how these differences affect the feedback generation process in these marketplaces. As can be
seen from Table 1, feedback on the eBay platform is significantly more positive and less negative
than that on the Amazon platform. Indeed, the incidence of negative feedback on Amazon is
approximately 2.5 times as high as on eBay, even though the data are for the same seller selling
the same products to similar consumers; we interpret this difference as strong evidence of the
positive reporting bias previously noted with the eBay mechanism. Figure 1 plots the total
feedback observed on the two platforms in our dataset, and Figure 2 plots this feedback over
time. Both figures show that feedback on eBay is consistently more positive (and less negative)
than on Amazon.
To test this difference in feedback across platforms, we use weekly measures of feedback
proportions on the two platforms and test whether they are statistically different using a pairedsample t-test. For this purpose, we treat Amazon feedback of 1 and 2 as negative feedback and
Amazon feedback of 4 and 5 as positive feedback. Whether we use the percentage of feedback
that is positive or the percentage negative as the measure, the difference between the Amazon and
eBay is strongly significant: t39 = 11.398, p < 0.0001 for percent positive, t39 = 22.564, p <
0.0001 for percent negative. This finding verifies that bilateral mechanisms lead to a smaller
proportion of negative feedback than do unilateral mechanisms, as argued in H1.
Another basic comparison between the Amazon and eBay mechanisms relates to the
propensity of users to leave feedback. The differences in the likelihood of receiving feedback
across the two platforms is striking: on average, only 11.2% of transactions on the Amazon
platform received feedback, whereas 45.4% of transactions on the eBay platform received
feedback, making buyers on eBay more than 4 times as likely to leave feedback as buyers on
16
There is also a difference is the scale of the feedback mechanisms; Amazon uses a 5-point rating scale (1 is the
lowest/worst, 5 is the highest/best) while eBay uses a 3-point rating scale (negative, neutral, and positive). In
Appendix 1 we explore the scale effect and demonstrate why scale differences do not affect our analyses.
22
Amazon. Once again, we test the significance of this difference by comparing the weekly
measures across the two platforms, which is highly significant: t39 = 22.076, p < 0.0001. This
difference strongly supports H2.
We conclude our exploration of the feedback data by comparing the prices of transactions
for which buyers leave and do not leave feedback. As can be seen in Table 1, transactions in
which the buyers leave feedback are, on average, for higher-value items than transactions in
which buyers remain silent; this pattern holds across both platforms (t39 = 7.499, p < 0.0001 on
Amazon, t39 = 5.629, p < 0.0001 on eBay). This finding is consistent with the idea that there is a
relationship between emotional involvement in the purchasing process and the feedback
processes; in particular, higher-price transactions appear lead to higher involvement and hence
higher propensity to leave feedback. Unfortunately, we do not have measures of involvement or
any other behaviour constructs, and thus we must leave aside these issues in this study. We now
turn to modelling the impact of feedback across these two platforms on prices and demand.
6.0 Empirical Model Specification
In this section, we develop a dynamic simultaneous equations model to quantify the
impact of feedback and test the hypotheses outlined below. These hypotheses follow directly
from the theoretical model developed in Section 3.
6.1 Feedback Index
Our analysis of scale effects (see Appendix 1) suggests that differences in granularity do
not lead to differences in the patterns of feedback elicited across platforms. This finding allows us
to develop a parsimonious econometric model using a single feedback index for both platforms.
Our preliminary analysis showed that counts of raw feedback by type (i.e. positive, neutral, and
negative) were highly correlated over time (i.e. 0.95 and above). One implication of such
correlation is that models using raw feedback counts suffer from all the usual problems arising
from highly collinear data and an initial attempt to estimate such a model failed to converge
under iterated three-stage least squares. One approach to overcoming these problems is to use an
index that combines the raw counts into a metric of average feedback. Both platforms in our
study use such a metric: eBay posts the percentage of feedback for a participant that is positive,
while Amazon posts the average feedback score (from 1.0 to 5.0) for the seller. Earlier research
found that eBay buyers tend to make decisions based on the percentage of feedback that is
23
positive, rather than the absolute numbers of feedback received (Cabral and Hortaçsu, 2006), and
we thus define our feedback index as an average of total positive and negative feedback:
NPFit =
TPFit − TNFit
*100
TPFit + TNFit
(1)
Where, NPFit (net positive feedback) is our index of feedback; TPFit is the total number of
positive feedback received and TNFit the total number of negative feedback received. Note that
our estimates of the effect of NPF are, therefore, quasi-elasticities because they measure the
effect of the percentage change in NPF on unit sales. We use this feedback index to develop our
econometric model of how reputation affects sales, how transaction characteristics generate
feedback, and how prices are formed.
6.2 Structural Model Description
Our structural model takes into account the decision processes of both buyers and sellers:
The consumer purchase decision process: Following the literature on the effects of
reputation mechanism on auction markets, we postulate that a consumer’s decision whether to
purchase is influenced by the feedback that they observe at the time of purchase, the prices of
the products, and other (observable and unobservable) product characteristics.
The feedback generation process: Our game theory model predicts that, on the eBay
platform, feedback do not necessarily reflect outcome of the associated transactions. On
Amazon, by contrast, feedback will be related to the outcome of the transactions; in
particular, transactions in which the buyer is so dissatisfied as to return the book and demand
a refund will lead to negative feedback.
Finally, as mentioned earlier, higher-price
transactions may have higher emotional involvement and therefore may lead to a higher
incidence of feedback.
The seller decision process: The primary role of seller in our model is to strategically set
prices after taking into account its own pricing history, customer feedback history, and
inventory level.
Diagram 1 presents the critical interrelationships between the key variables. The processes
mentioned above can be specified in the following manner1:
1
Our model cannot take into account any differences between the buyers across the two websites since the
anonymous nature of online transactions makes it difficult to obtain demographic data on purchasers across websites.
However, a recent Forrester Research survey found that Amazon and eBay consumers were nearly identical in terms
of the demographic characteristics measured in the study (Johnston et al, 2004).
24
Vit* = υ + υ p pit + υ F Fit + υTS TSit + υWT WTit
(2)
Fit* = π + π p pit + π TS TSit + π R Rit
(3)
pit* = ϕ + ϕiF Fit + ϕTS TSit
(4)
Here, Vit* is the potential demand/sales level for platform i at period t. Fit* is the potential
feedback level the market should generate and pit* is the desired price level by the firm. On the
right-hand side, pit is the observed price level, Fit is the observed level of feedback, TSit is a
binary variable for the textbook season (the beginning of the fall and winter semesters), Rit is the
measure of returned books, and WTit is web traffic to capture any secular trend in growth.
In this system, the left-hand side variables are not expressed at the level they are observed
but at the potential or desired level. In each equation, we express the current period’s observed
level of the left-hand side variables as a function of the previous period’s level using a partial
adjustment process to capture the dynamics of online marketplaces.
This implies, in the
demand/sales equation, that the market will not clear for every book in every period; thus, the
observed sales will be a fraction of the potential sales and unmet demand or oversupply will be
adjusted in the next periods. This structure implies that the current period’s observed sales will be
correlated with the sales in the previous period. We express this relationship as partial adjustment
process (Greene, 2003):
Vit − Vit −1 = (1 − λV ) (Vit* − Vit −1 )
(5)
For the feedback equation, the same structure will hold because not all price- and returnrelated consumer concerns will generate feedback in the current period and some of the feedback
in the current period will be a result of transactions in earlier periods. Therefore, observed
feedback in the current period will similarly be a fraction of potential feedback, and the
difference between the current period feedback and one period lagged feedback can be expressed
as fraction of the difference between the current period potential feedback and feedback in the
previous period:
Fit − Fit −1 = (1 − λF ) ( Fit* − Fit −1 )
(6)
25
Our detailed discussions with firm managers suggest that current prices are not set as part
of an optimization process, rather prices are adjusted heuristically based on past prices and sales.
Again, we capture this iterative process through a partial adjustment model of price formation:
pit − pit −1 = (1 − λ p ) ( pit* − pit −1 )
(7)
Finally, exploratory data analysis indicated that prices moved differently during the
textbook season (the beginning of the fall and winter college semesters), so we introduce an
interaction term of price and textbook season binary. Using these partial adjustment processes
and a platform-specific intercept, we can re-specify our baseline model as:
Vit = ∑ υibin bini + υ p pit + υV V
+ υ F Fit + υ TSit + υ
TS * pit + υWT WTit
it −1
TS
TSp it
i
(8)
Fit = ∑ π
bin + π F Fit −1 + π p pit + π TS TSit + π R Rit
ibin i
i
(9)
pit = ∑ ϕ
bin + ϕ p pit −1 + ϕiF Fit + ϕTS TSit
ibin i
i
(10)
Here, i=Amazon or eBay
The partial adjustment process also implies that there exists a geometric lag effect of right
hand side (RHS) variables on the left hand side (LHS) variables; thus, the effect of the RHS
variables on the LHS variables decays over time. Such a decay process captures the important
dynamics of online marketplaces, for example, in the case of the sales equation (8), recent
feedback influences current period sales more than feedback from earlier periods. Similarly,
recent pricing influences current period sales more than earlier pricing (10), perhaps due to
consumers forming reference prices based on the prices observed in the current and previous
periods. Likewise, the firm’s current-period pricing decisions are influenced more by the
feedback in the recent period than in the earlier periods (10). Similarly, in the feedback equation
(9), recent prices and returns generate more of the current feedback than prices and returns from
earlier periods.
Our present specification is relatively parsimonious and helps us to avoid some of the
pitfalls of estimating models with large numbers of lags. (Standard geometric lag models, for
example, run into problems with degrees of freedom with limited numbers of observations.)
Another important advantage of our specification is that the unique lagged variables on the right
hand side help us to have a fully identified model. To capture the platform-specific unobserved
26
effects, we augment our baseline model using platform-specific binary variables and respecify
key parameters by the platforms. Thus, our platform-specific model is:
Vit = ∑ υ
bin + υip d k pit + ∑ υiV d kV
+ υiF d k Fit + υ TSit + υ
TS * pit
it −1 ∑
TS
TSp it
ibin i ∑
i
i
i
i
(11)
+ ∑ υiWT d kWTit
i
Fit = ∑ π ibinbini + ∑ π iF Fit −1 + ∑ π i d k pit + π TS TSit + ∑ π iR d k Rit
i
i
i
(12)
pit = ∑ ϕibinbini + ∑ ϕip d k pit −1 + ∑ ϕiF d k Fit + ϕTS TSit
i
i
i
(13)
Here, i,k=Amazon or eBay; and d k = 1 if k=i.
7.0 Estimates of the Structural Model
In this section, we discuss results from the two estimated models: the baseline model
(equations 8-10) and the platform specific model (equations 11-13). Both of these models can
potentially give rise to endogeneity problems because the left hand side dependent variables also
appear as right hand side explanatory variables in other equations. To control for endogeneity, we
use iterated three stage least squares to estimate the models, with the presence of lagged
endogenous variables helping to identify the estimated models.
In Table 3, we present the estimates of our baseline model (8-10).
Under this
specification, interestingly, the feedback measure does not significantly impact sales; instead,
sales are driven by lagged sales, pricing during the textbook season, the textbook season, and web
traffic. The feedback measure is significantly impacted by the unobserved platform-specific
effects, lagged feedback, and, as expected, by the returns per order. On the other hand, pricing is
only significantly impacted by the lagged price and textbook season. Overall, this model only
provides aggregate effects and suggests that the overall effects of feedback on sales or prices
across both platforms are insignificant, so we turn our attention to the platform-specific
disaggregate model (11-13), the results of which are presented in Table 4.
Under the platform-specific specification, feedback on Amazon significantly impacts
sales whereas feedback on eBay has no effect on sales. This difference may explain why we
failed to find a significant effect of feedback on sales in the baseline model, which pools both
platforms. In terms of price effects, higher prices on Amazon marginally reduce sales (at the
10% level) but the price on eBay does not affect sales. These marginally significant or
27
insignificant effects of price may be a result of the naïve price-setting rule used by the seller
where past prices are used as a rule-of-thumb by which to set current prices.
Interestingly, web traffic on eBay positively and significantly impacts sales but web
traffic at Amazon does not have any significant effect. This result could be driven by the fact that
the business models of Amazon and eBay are very different for used books: while eBay’s primary
objective is to increase sales for all its sellers, Amazon’s principal goal is sell new books and it
provides access to used books from affiliated sellers only as a secondary alternative to avoid
losing sales of out-of-print books or sales to highly price-sensitive consumers who will not buy
new books. Our results suggest that both platforms are succeeding in implementing their
marketing model. We explore the implications of these differences across platforms in more
detail below.
To chose between the two models, we test for model fit. Because our baseline model is
nested in the platform-specific model, a simple likelihood ratio test will suffice to find the betterfitting model. Test statistics reject the baseline model in favor of the platform-specific model at
the 5% level of significance. Thus we will use the results from the platform-specific model in
order to estimate the short- and long-term effects of feedback.
Under our dynamic partial adjustment model, short- and long-term effects can be
measured in the following way. Suppose yt = α + β xt + δ yt −1 is our final model specification, then
β is the short term effect of xt and the long term effect of xt is given by β
(1 − δ )
(Greene,
2003). Table 5 presents our estimates of the short-run and long-run effects.
The estimated effect of feedback suggests that a 1% increase in the percentage of Amazon
feedback that is positive will lead to 905 extra units of sales in the short-term 1153 units in the
long-term effect. eBay feedback, on the other hand, doesn’t have a significant effect on unit
sales, implying a short- and long-term effect of zero units. In the case of web traffic, eBay traffic
significantly impacts sales; an increase of 100,000 visits will lead to 858 units of increased sales
in the short term and 1387 units in the long term. There is no significant effect of web traffic on
sales on the Amazon platform. This can be due to the fact that only a small proportion of
Amazon traffic still relates to books given their now highly diversified business model.
In terms of the effect of feedback on pricing, we find contrasting effects on the two
platforms: Amazon feedback positively affects pricing, whereas eBay feedback has a marginally
28
significant but negative effect on pricing. That is, more positive feedback allows UsedBooks.com
to raise unit prices on Amazon but actually results in marginally lower prices on eBay. On
Amazon, raising prices in response to positive feedback can be an optimum strategy as more
positive feedback also increases sales, so the net effect of a 1% increase in positive feedback will
be a $776 increase in revenue in the short term and a $2,951 increase in the long term. Results in
the case of eBay are puzzling. Perhaps UsedBooks.com faces more intense price competition on
the eBay platform as there are more sellers mainly for lower-quality and less-expensive books at
eBay platform; this competitive pressure may drive prices lower even with more positive
feedback. Table 5 also presents estimates of price elasticities, which are of the right sign but are
significant only on the Amazon platform.
With regard to our hypotheses, we find that positive feedback on eBay has no effect on
demand, whereas positive feedback on Amazon has a positive and significant relationship to
demand, providing support for H3. We also find that buyers on Amazon are more likely to leave
negative feedback when problems requiring returns arise than are buyers on eBay: returns per
order is significant on the Amazon platform but insignificant on the eBay platform. This finding
supports H4.
8.0 Concluding Remarks
Our ultimate objective is to derive a richer understanding of the dimensions of reputation
mechanisms and their impacts on online transactions in both the retail and auction contexts. In
this study, we have taken first steps to exploring these differences, and, to some extent, have
generalized earlier findings about online reputation effects that were derived solely from the eBay
platform and auction context.
Using a theoretical model of the “feedback game,” we find very different incentives to
leave feedback on eBay and Amazon due to differences in the directionality of their reputation
mechanisms. We find empirically that the propensity to leave feedback and the proportion of
positive and negative feedback observed on the eBay and Amazon platforms differs markedly,
even though we examine the same seller and products across the two platforms. In addition, a
review of previous research shows that more than 98% of outcomes observed on eBay are
consistent with the predictions of our parsimonious game-theory model. Overall, these empirical
findings strongly support our theoretical model.
29
Our analysis of the effects of feedback on market outcomes suggests that the Amazon
mechanism appears to elicit much more truthful feedback than does the eBay mechanism,
resulting in reputations that are more useful to buyers in assessing the trustworthiness of sellers,
and thereby enable “good” sellers to increase both sales and prices. On the other hand, the eBay
platform creates strategic incentives for buyers and sellers to provide overwhelmingly positive
feedback, making reputation on eBay less useful for overcoming moral hazard. Our findings
show buyers are aware that, as a result of this strategic feedback, the eBay reputation mechanism
provides “truthiness” rather than truth, that is, nearly all sellers can appear trustworthy regardless
of their actual performance. It is not surprising, then, that we find that buyers largely ignore
positive feedback on eBay.
We also show that many of the characteristics of the eBay reputation mechanism observed
in auctions also hold in the retail context. In particular, we find that the eBay reputation
mechanism is strategic and suffers from a positive reporting bias in the retail context just as in the
auction context.
Our findings also explain why customer satisfaction, as measured by the University of
Michigan American Customer Satisfaction Index (ACSI), is consistently higher for Amazon than
for eBay despite customer feedback on the eBay reputation mechanism being much more positive
than the feedback on the Amazon reputation mechanism.2 If feedback on the two platforms were
equally reflective of customer satisfaction, then eBay’s ACSI score should be higher than
Amazon’s, but the positive bias in eBay feedback explains why eBay can be rated much higher
by its own feedback mechanism yet have lower actual levels of customer satisfaction; again, eBay
provides the appearance of satisfaction rather than actual satisfaction, or truthiness over truth.
We also find that Amazon feedback is more effective than eBay feedback for generating
revenue since increasingly positive feedback at Amazon leads to higher sales and prices. It is not
surprising that eBay recently announced that it will unveil a new “Feedback 2.0” reputation
mechanism; the central departure of Feedback 2.0 from the existing eBay mechanism is it allows
four types of unilateral feedback from buyers about sellers regarding the accuracy of item
description, communication, delivery time, and postage and packaging charges. Our results show
that unilateral feedback, which does not suffer from the strategic considerations of bilateral
2
See, for example, “E-Commerce Nears All-Time High in Latest American Customer Satisfaction Index,” 2007.
http://www.foreseeresults.com/Press_ACSI_AllTimeHigh_Feb07.html
30
feedback, will be more useful to decision-makers, and thus Feedback 2.0 may allow eBay to build
higher demand for good sellers. However, Feedback 2.0 still allows sellers to post one overall
rating of buyers, and this remaining bilateral element may prevent buyers from leaving negative
feedback – even on the unilateral feedback dimensions – for fear of retaliation from the sellers.
Thus, while the new eBay feedback mechanism is moving toward becoming more unilateral than
bilateral, it may not have moved far enough to completely remove the strategic elements of
feedback on eBay. Of course, our future research will no doubt explore the impact of eBay’s
change of reputation mechanism.
Our present study also has a few shortcomings. Given the aggregate nature of our data we
were not able to measure the impact of low-priced and high-priced books separately. Currently,
we are in the process of collecting and archiving data on individual transactions with the help of
our corporate partner in order to conduct an analysis of transaction-level feedback effects.
Another shortcoming of this study is that although it allows us to measure the effects of
feedback across the two platforms, it does not allow us measure the competitive effect of
feedback on sales. Conducting such research would require that competing firms share sensitive
marketing data with us, which we have so far been unable to achieve. Overall, our results clearly
show that differences in the design of reputation mechanisms across platforms have large impacts
market outcomes. Investigating how to design better reputation mechanisms will continue to be
our primary research objective.
References
Axelrod, Robert. (1981). "The Evolution of Cooperation." Science, 211(4489):1390-6.
Ba, S. and P. Pavlou. 2002. “Evidence of the Effect of Trust Building Technology in Electronic
Markets: Price Premiums and Buyer Behavior,” MIS Quarterly. 26:3, pp. 243–68.
Bajari, P. and A. Hortaçsu, “Economic Insights from Internet Auctions,” Journal of Economic
Literature, June 2004, v. 17, pp. 457-86.
Bhattacharjee, R. and A. Goel, “Avoiding Ballot Stuffing in eBay-like Reputation Mechanisms,”
SIGCOMM’05 Workshops, August 22–26, 2005, Philadelphia, PA.
Berg J., J. Dickhaut, and K. McCabe, 1995, “Trust, reciprocity, and social history,” Games and
Economic Behaviour 10, 122-142.
31
Bikhchandani, S., “Reputation in Repeated Second-Price Auctions”, Journal of Economic
Theory, October 1988, v. 46(1), pp. 97-119.
Bolton, G.E., E. Katok, A. Ockenfels, "How Effective Are Electronic Reputation Mechanisms?
An Experimental Investigation," Management Science 50(11) 2004 1587-1602.
Cabral, L. and A. Hortaçsu, “The Dynamics of Seller Reputation: Theory and Evidence from
eBay,” New York University Working Paper, 2006
Dellarocas. C. Analyzing the economic efficiency of eBay-like online reputation reporting
mechanisms. Proceedings of the 3rd ACM Conference on Electronic Commerce, Tampa,
FL, October 14-16, 2001.
Dellarocas C. “Reputation Mechanism Design in Online Trading Environments with Pure Moral
Hazard,” Information Systems Research 16 (2), 2005, 209-230.
Dellarocas, C. “How Often Should Reputation Mechanisms Update a Trader’s Reputation
Profile?” Information Systems Research 17 (3), September 2006, 271-285.
Dellarocas, C., Fan, M., and Wood, C., “Self-Interest, Reciprocity, and Participation in Online
Reputation Systems,” Workshop in Information Systems and Economics (WISE), Seattle,
WA, Dec 2003.
Dellarocas, C. & Wood, C. “The Sound of Silence in Online Feedback: Estimating Trading Risks
in the Presence of Reporting Bias,” Robert H. Smith School Research Paper No. RHS 06041, 2006.
Department of Commerce, “Quarterly Retail E-Commerce Sales 4th Quarter 2004” Feb. 24, 2005
Dewan, S. and V. Hsu. 2001. “Trust in Electronic Markets: Price Discovery in Generalist Versus
Specialty Online Auctions,” University of Washington Working Paper.
Eaton, D.H. 2002. “Valuing Information: Evidence from Guitar Auction on eBay,” Murray State
University Working Paper.
Fan, M., Y. Tan, and A. Whinston, “Evaluation and Design of Online Cooperative Feedback
Mechanisms for Reputation Management,” IEEE Transactions on Knowledge and Data
Engineering, 17(2) 2005.
Greene, W. H. (2003). Econometric Analysis (Fifth Edition). Upper Saddle River: Prentice Hall.
Houser, D. and J. Wooders (2006) “Reputation in Auctions: Theory, and Evidence from eBay,”
Journal of Economics & Management Strategy 15 (2), 353–369.
32
Jin, G.Z. and A.Kato. 2002. “Blind Trust Online: Experimental Evidence from Baseball Cards,”
University of Maryland working paper.
Johnston, C.A., C.P. Wilson, S.Leaver, E.H. Yuen. 2004. Amazon.com and eBay Shoppers'
Loyalty. Forrester Research, Inc., accessible at:
http://www.forrester.com/Research/Document/Excerpt/0,7211,33897,00.html
Kalyanam, Kirthi and Shelby McIntyre. 2001. “Returns to Reputation in Online Auction
Markets,” Santa Clara University working paper.
Klein, T.J., C. Lambertz, G. Spagnolo, K.O. Stahl, "Last Minute Feedback" University of
Mannheim Working Paper, 2006.
Ku, Gillian, D. Malhotra and J.K. Murninghan. 2003. “Competitive Arousal in Live and Internet
Auctions,” working paper. Kellogg Graduate School of Management, Northwestern
University.
Lei, Q. “The Financial Value of Reputation,” University of Michigan Working Paper, 2005.
Livingston, J. 2003. “How Valuable is a Good Reputation? A Sample Selection Model of Internet
Auctions,” University of Maryland working paper.
Latcovich, S. and H. Smith, “Pricing, Sunk Costs, and Market Structure Online: Evidence from
Book Retailing.” Oxford Review of Economic Policy, Summer 2001(2) pp. 217-34.
Lucking-Reiley, D.; D. Bryan; N. Prasad and D. Reeves. 2000. “Pennies from eBay: the
Determinants of Price in Online Auctions,” working paper, University of Arizona.
McCabe, K.A., S.J. Rassenti, and V.L. Smith, “Designing Call Auction Institutions: Is Double
Dutch the Best?”, Economic Journal, January 1992, v. 102(410), pp. 9-23.
McDonald, C.G. and V.C. Slawson, Jr., “Reputation in an Internet Auction Market”, Economic
Inquiry, October 2002, v. 40(4), pp. 633-50.
Melnik, M.I. and J. Alm, “Does a Seller's Ecommerce Reputation Matter? Evidence from eBay
Auctions,” Journal of Industrial Economics, September 2002, v. 50(3), pp. 337-49.
Montano, B.R., D.C. Porter, R.A. Malaga, and J.K. Ord “Enhanced Reputation Scoring for
Online Auctions” proceedings of the Twenty-Sixth International Conference on
Information Systems, 2005.
National White Collar Crime Center. 2005. “IC3 2004 Internet Fraud - Crime Report”1-30.
http://www.ifccfbi.gov/strategy/2004_IC3Report.pdf
33
Resnick, P. and R. Zeckhauser. “Trust Among Strangers in Internet Transactions: Empirical
Analysis of eBay's Reputation System.” in The Economics of the Internet and ECommerce, Michael R. Baye, editor. Volume 11 of Advances in Applied
Microeconomics. Amsterdam, Elsevier Science, (2002).
Resnick, P., R. Zeckhauser; J. Swanson and K. Lockwood, “Value of Reputation on eBay: A
Controlled Experiment.” Experimental Economics 9(2) 2006.
Schamel, G. “Online Auctions: Pricing Bordeaux Wine on eBay,” Humboldt University Berlin
Working Paper, 2004.
Shugan, S.M. 2005. Marketing and Designing Transaction Games. Marketing Science 24(4) 525530.
Steiner, I. “AuctionBytes Publishes Survey Results on eBay Feedback 2.0,” Auction Bytes,
March 9, 2007, http://www.auctionbytes.com/cab/abn/y07/m03/i09/s02.
Wikipedia, “Truthiness”, http://en.wikipedia.org/wiki/Truthiness, retrieved 2006-08-04.
Wolf, J.R. and W.A. Muhanna, “Adverse Selection and Reputation Systems in Online Auctions:
Evidence from Ebay Motors,” Proceedings of the Twenty-Sixth International Conference
on Information Systems, 2005.
34
Figure 1: Total Feedback by Platform
Amz FB = 1
Amz FB = 2
Amz FB = 3
Amz FB = 4
0
0
200
200
400
400
600
600
800
Amazon and Half Feedback Average
Half FB = -1
Half FB = 0
Half FB = 1
Amz FB = 5
Figure 2: Feedback by Platform over Time
% Positive Feedback
80 85 90 95
% Negative Feedback
0
5
10 15
Feedback by Platform
0
10
20
Week
30
0
10
Half
20
Week
30
Amazon
40
Half
70
% NPFM
75 80
85
% Neutral Feedback
2 4 6 8 10
Amazon
40
0
10
20
Week
Amazon
30
40
0
Half
10
20
Week
Amazon
30
40
Half
Note: Weeks between vertical dashed lines indicate textbook season.
35
Diagram 1: Flowchart of the Structural Model
Consumer searches for products
Consumer finds products
Feedback generated
Consumer forms expectations
of quality and service based on
price and feedback
Expectations realized
Consumer orders products
36
Table 1: Transaction Prices and Feedback
Platform
Variables
Positive
Percent of Feedback
Amazon
Negative
Silent
85.2%
3.3%
11.5%
9.6%
0.4%
1.3%
88.8%
$ 18.06
$ 19.65
$ 20.69
$ 15.21
Percent of Feedback
89.8%
5.7%
4.6%
Percent of Total
40.8%
2.6%
2.1%
54.6%
$ 11.74
$ 9.62
$ 14.66
$ 7.09
Percent of Total
Average Price
eBay
Neutral
Average Price
Table 2: Platform Characteristics
Platform
Variables
Mean
Std. Error
$ 15.78
3.62
14.59
3.12
Web Traffic ('00000)
4.10
0.50
% Returned
1.57
0.61
$ 10.34
3.06
Orders per Week ('000)
3.62
1.48
Web Traffic ('00000)
5.77
1.16
% Returned
0.82
0.26
Price
Orders per Week ('000)
Amazon
Price
eBay
37
Table 3: Baseline Model
Sales Equation
Feedback
Equation
Price Equation
Binary Amazon
-16.0384
(19.8095)
12.3331
(3.4424)***
-12.1945
(17.2195)
Binary eBay
-26.9325
(20.9501)
13.5608
(3.7740)***
-16.5449
(18.9536)
Cumulative Net Positive Feedback
0.2965
(0.2361)
Lagged Sales
Unit Price
0.2340
(0.2285)
0.2265
(0.0911)**
-0.0921
(0.1440)
Textbook Season * Unit Price
0.5198
(0.1138)***
Textbook Season Binary
-4.2730
(1.2003)***
Web Traffic
0.6118
(0.1267)***
Lagged Cumulative Net Positive
Feedback
-0.0238
(0.0288)
-0.1113
(0.2110)
0.8382
(0.0455)***
Errors and Returns Per Order
-0.2327
(0.0972)**
Lagged Unit Price
Observations
2.3246
(0.7024)***
0.6634
(0.0931)***
80
80
80
Note:
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
38
Table 4: Platform-Specific Model
Sales
Equation
Feedback
Equation
Price
Equation
Binary: Amazon
-52.1058
(28.1920)*
32.7483
(4.9253)***
-57.4645
(27.5125)**
Binary: eBay
-11.4860
(30.3792)
6.1310
(5.2168)
44.1295
(24.2138)*
Cumulative Net Positive Feedback: Amazon
0.9045
(0.4015)**
0.8584
(0.3778)**
Cumulative Net Positive Feedback: eBay
0.1518
(0.3328)
-0.4809
(0.2884)*
Lagged Unit Sales: Amazon
0.2152
(0.1231)*
Lagged Unit Sales: eBay
0.2932
(0.1575)*
Unit Price: Amazon
-0.4205
(0.2329)*
0.0388
(0.0273)
Unit price: eBay
-0.2010
(0.2552)
-0.0508
(0.0448)
Textbook Season * Unit Price
0.6820
(0.1649)***
Textbook Season Binary
-6.1703
(1.8527)***
Web Traffic Amazon
Web Traffic eBay
-0.2995
(0.2118)
2.9937
(0.6632)***
-0.6416
(1.0549)
0.9803
(0.3851)**
Lagged Cumulative Net Positive Feedback: Amazon
0.5478
(0.0686)***
Lagged Cumulative Net Positive Feedback: eBay
0.9339
(0.0609)***
Errors and Returns Per Order: Amazon
-43.0959
(9.8810)***
Errors and Returns per Order: eBay
-32.6892
(21.9252)
Lagged Unit Price Amazon
0.6648
(0.0916)***
Lagged Unit Price eBay
0.3507
(0.1320)***
Observations
80
80
80
Note:
Standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
39
Table 5: Short- and Long-Term Effects
Estimates
Std. Err.
z-value
Short term Amazon feedback effects on sales
0.905
0.401
2.250
Long term Amazon feedback effects on sales
1.153
0.594
1.940
Short term eBay feedback effects on sales
0.152
0.333
0.460
Long term eBay feedback effects on sales
0.215
0.487
0.440
Short term Web traffic: Amazon
-0.817
1.327
-0.620
Long term Web traffic: Amazon
-0.642
1.055
-0.610
Short term Web traffic: eBay
0.980
0.385
2.550
Long term Web traffic: eBay
1.387
0.532
2.610
Short term Amazon Feedback effect on price
0.858
0.378
2.270
Long term Amazon Feedback effect on price
2.561
1.403
1.830
Short term eBay feedback effect on price
-0.481
0.288
-1.670
Long term eBay feedback effect on price
-0.741
0.355
-2.090
Short and long term price elasticities of demand
Short term price elasticities Amazon
-0.437
0.242
-1.810
Long term price elasticities: Amazon
-0.556
0.295
-1.890
short term price elasticities eBay
-0.482
0.612
-0.790
Long term price elasticities: eBay
-0.614
0.739
-0.830
40
Appendix 1: Differences in Granularity/Scale
As mentioned above, there are two key differences between the eBay and Amazon
reputation mechanisms: the granularity of the rating scale and the directionality of rating. In this
appendix, we explore the impact of granularity. Amazon uses a 5-point rating scale (1 is the
lowest/worst, 5 is the highest/best) while eBay uses a 3-point rating scale (negative, neutral, and
positive).
To explore the effect of the granularity of the rating scale, we first plot the feedback by
type across platform in Figures A1 and A2. The distribution of feedback on the two platforms is
similar, but not identical (ignoring scale differences). Both distributions are negatively skewed,
but the eBay distribution is unimodal whereas the Amazon distribution is bimodal. These
differences in modality are not a function of the scale; in Figure A1 and Figure A2 we project the
eBay feedback onto the Amazon scale and vice-versa (assuming uniform distribution within
categories). In both cases, the modality of the distribution is preserved even as the scale
changes. Thus, while there appear to be differences in the underlying distribution of feedback
across Amazon and eBay, they do not appear to be caused by the granularity of the rating scale.
As a result, we focus our analysis in this paper on the differences in directionality. For
this paper we collapse Amazon’s five-point scale into the three-point scale of eBay using
Amazon’s posted categorization of scores of 4 or 5 as “positive,” scores of 1 or 2 as “negative,”
and a score of 3 as “neutral.”
41
Figure A1: Amazon Feedback and Half Feedback Projected on the
Figure A2: EBay Feedback and Amazon Feedback Projected on the
Amazon Scale
EBay Scale
100
80
90
70
80
60
70
50
60
40
50
30
4
2
40
20
30
10
20
0
10
5
0
3
1
1
0
-1
Half Feeback on Amazon Scale
Actual Amazon Feedback
Actual Half Feedback
Amazon Feedback on Half Scale
42