2015 International Conference on Management Science & Engineering (22th)
October 19-22, 2015
Dubai, United Arab Emirates
Moderating Effect of Number of Competitors on the Relationship between
Reputation and Sales
XU Min，YE Qiang
School of Management, Harbin Institute of Technology, Harbin 150001, P.R.China
reputable sellers [11]. Many prior studies have shown that
feedback, along with other auction characteristics,
significantly affect price premiums. In addition, online
reputation mechanisms are viable for guaranteeing
quality of product and service and correspondingly affect
the buyer’s willing to pay. A number of studies have
shown that the reputation of the seller can influence the
price paid in an Internet sale.
Some researchers explore the direct relationship
between reputation and sales volume or the probability
of sale. They agree that a good reputation helps establish
the trust necessary for a transaction to take place. They
suggest people are more likely to purchase from the web
if they perceive a higher degree of trust in e-commerce[12].
Reputable sellers receive much larger expected returns
than sellers who have no reputation. Their auctions are
much more likely to result in a sale [13]. Buyers are
willing to transact with reputable sellers. Therefore, high
reputation increased the probability of a sale[14]
Previous studies mainly examine the direct impact
of reputation on sales. However few researches focus on
factors influence the relationship between reputation and
sales[15]. This paper will extend current research on the
relationship between reputation and sales. We will
analyze the moderating effect of number of competitors
on their relationship. We aim to explore the direct impact
of reputation and competition, and additionally validate
the moderating effect of number of competitors. Using
data of Canon digital cameras, we find that reputation
had a significant impact on sales, and that this effect will
be enhanced when there are more sellers in the market.
The paper is organized as follows. In the next
section we review the theoretical and empirical literature
on the value of reputation and the impact of number of
competitors. We also propose our hypotheses in this
section. In Section 3, we represent our log-linear
regression models. Then our analyses are conducted in
Section 4 and conclusive comments are reported in
Section 5.
Abstract: Reputation mechanisms have become an
essential component in online marketplaces, helping to
enforce trust and reduce risk. They play an important role
in buyers’ purchase decisions, therefore generating
favorable economic performance. Many researchers
explore the impact of reputation on price or sales,
however the majority of them focus on the direct
relationship between reputation and price. We in this
paper will look at the impact of reputation on sales
volume and consider whether the number of competitors
will moderate their relationship. Using data of Canon
digital cameras collected on Taobao.com, the largest
online platform in China, we build log-linear regression
models to test our hypotheses. We find that seller’s
reputation has significantly positive impact on sales. The
number of competitors has negative effect on sales, and
meanwhile will enhance the relationship between
reputation and sales.
Keywords: electronic markets, reputation, sales,
number of competitors
1 Introduction
In electronic markets, buyers will experience more
transaction risks and information asymmetries are much
severer due to faceless transactions between buyers and
sellers. To mitigate such information asymmetry, online
reputation mechanisms are built in online platforms.
Reputation has become an essential component in online
marketplaces, helping to build trust and avoid adverse
selection. Comparing trading in a market with online
reputation systems to a market without reputation
systems, researchers find that the reputation mechanisms
improve transaction efficiency and enforce trust [1].
The issue of reputation mechanisms has received
considerable attention in recent years. In general,
researchers believe high reputation will generate
favorable economic performance. Much of the previous
work looks at the impact of a seller’s reputation on prices
[2-10]
. They find a relationship between trust and internet
auction prices and believe trust can mitigate information
asymmetry therefore generating price premiums for
2 Literature review
2.1 Impact of reputation
Online reputation mechanisms are built to enhance
Supported by the NKBRPC (2014CB340506) and the National
Natural Science Foundation of China (71225003, 71490724)
978-1-4673-6513-0/15/$31.00 ©2015 IEEE
- 46 -
trust between buyers and sellers. Previous studies believe
high reputation will generate favorable economic
performance.
The majority of previous research explores how
seller reputation affects auction prices [16,17]. They show
that price premium variations as a consequence of
buyers' lack of information[18], and reputation will
influenced trust predominately as a result reputation
correspondingly significantly affect price premiums.
Many of them find higher reputation will translate into
higher prices [19-22]. While some studies find closing
auction prices are not strongly influenced by the level of
seller reputation but prices are considerably lower when
sellers have no reputation at stake.[23] There are also
some studies drawing different conclusions. They find in
online retailing marketplace of homogeneous goods, high
reputation sellers should charge relatively low price [24].
A number of studies suggest a significant
correlation between seller reputation and the likelihood
of a sale [25,26]. They suggest buyers are more willing to
trade with high-reputation sellers [27]. For example, Using
data from Internet auctions, Livingston (2005) suggest
reputable sellers receive much larger expected returns
than sellers who have no reputation, and their auctions
are much more likely to result in a sale [13]. Eaton (2007)
uses data from electric guitar auctions on eBay to
examine the impact of reputation on the likelihood that
an auction ends in a sale[28]. The results indicated seller
related negative feedback did reduce the likelihood of an
auction ending with a sale. Canals-Cerdá (2012)
conducts an empirical analysis of the value of a seller’s
online reputation, using a unique dataset of art auctions
on eBay. The results point that an additional negative
feedback results in a 9% reduction in the number of
bidders on average, and the effect is highly significant [26].
Cabral and Hortacsu (2010) find that, when a seller first
receives negative feedback, his weekly sales rate drops
from a positive 5% to a negative 8% [29].
A handful of studies empirically focus on the
relationship between sellers’ reputation and sales volume.
Zhang and Zhang (2011) find when seller’s reputation
level is below certain threshold value, the sales are
negatively influenced by reputation. When the seller’s
reputation level exceeds certain critical value, better
reputation certainly help to boost the sales[30]. Li et al.
(2008) find that sellers’ good reputation has a positive
impact on their sales volume, but the marginal effect of
this impact decreases severely[31]. Ye et al. (2009)
suggest a strong correlation between seller reputation and
sales volume, but not sales price [32]. Correspondingly,
we raise the following hypothesis,
Hypothesis 1 Seller’s reputation has positive impact
on sales volume.
2.2 Impact of number of competitors
Number of competitors will influence sales
performance directly and indirectly.
Lindsey-Mullikin and Grewal (2006) find a
negative relationship between the number of competitors
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and price dispersion [33]. Similarly, Lewis (2008) also
suggest the extent of price dispersion is related to the
density of competition [34]. They suggest more
competition led to lower prices and to lower price
dispersion.[35]. Dufwenberg and Gneezy (2000) find that
markets prices do depend on the number of competitors
[36]
.
According to Cournot model, the equilibrium in
1
sales is n + 1 , where n represent the number of sellers. If
there are more sellers in the market, each seller will get a
smaller sales quantity. Correspondingly, we propose the
following hypothesis,
Hypothesis 2a: Number of competitors has a direct
negative effect on sales.
Competition has different effects on sellers with
different reputation levels. the competition among the
sellers, which reduces the high-reputation sellers' prices
but increases the low-reputation sellers' prices.[37]
Venkatesan et al. (2007) provide evidence that when
faced with small number of competitors in
product-market, retailers with high service quality can
easily differentiate their services and charge a higher
price[38]. Bockstedt and Goh (2011) find strong evidence
that the number of reputable sellers in an auction
marketplace moderates the effects of auction attributes
on auction outcomes. Specifically, as auction
environments become more competitive, these attributes
such as reputation scores become less effective on
auction performance[39]. Gatignon (1984) suggest that
more competition leads to a stronger relationship
between advertising level and sales [40]. Similarly we
propose that in a competitive market the relationship
between sales and reputation will also be strong.
Ye et al. (2013) suggest in different competition
level, the relationship between reputation and sales are
different. They argue the number of sellers will influence
their relationship[15]. We will extend this work validate
the impact of number of sellers on retailers’ price
premium and sales volume effects. In other word, we
propose that number of sellers will has an interaction
effect on the correlation between price and sales.
Hypothesis 2b Number of sellers is a moderator of
the effect of reputation on sales volume.
3 Methodology
3.1 Data
We developed a java program to automatically
assemble Canon digital camera data on Taobao.com in
2012. The products sampled include PowerShot A2500,
A2600, G15, S110, SX50, SX160, SX240, IXUS 132,
IXUS140, IXUS 255, EOS 5D Mark III. There are
thousands of items retrieved from the website, however
many of them do not have transaction history. We
exclude those sellers which have not sold a product. We
also refined the price and drop the noisy data, and we
finally get a sample with 684 items. The number of
sellers for each product is listed as follows.











EOS 5D Mark III
XUS 132
IXUS140
IXUS 255
PowerShot A2500
PowerShot A2600
PowerShot G15
PowerShot S110
PowerShot SX50
PowerShot SX160
PowerShot SX240
N represents the number of competitors (sellers) for
each product.
Price_r is defined as average price post by one
distinct seller divided by average price of all
the same products.
Sales_r is measured as one seller’s relative
accumulated sales volume in previous 30 days.
Many studies emphasize the important role of
negative feedback[1], so in this paper we also take
negative feedback into consideration. On account of the
similar role played by neutral feedback ratings as
negative feedbacks, we use the sum of numbers of
negative ratings and neutral rating as the numerator for
Ne_pos. For instance neutral rating has a significant
negative effect on price premium[21].
We conduct logarithmic regression in the models
since coefficient of the regression of sales on price is the
elasticity if we include the logarithms changes in the
regression.
Tab.1 offers summary statistics of our dataset,
including both dependent variables and independent
variables in this study.
67 items
87 items
56 items
35 items
96 items
63 items
23 items
29 items
68 items
55 items
105 items
3.2 Model
Following previous studies, we try to include all
reputation related variables posted in retailer’s feedback
profile. Our analysis focuses on the distribution of list
prices for the products in our samples.
To perform the analysis, we build the following two
logarithmic regression models. Model 1 presents the
direct effect of reputation and number of competitors on
sales volume. Model 2 shows the moderating effect of
number of competitors on sales.
It is obvious that price will decrease sales since a
lower price will attract more buyers. Therefore, we use
price as a control variables in our regression models.
Considering different mean values of price and sales for
each product, we use relative price (Price_r) and relative
sales (Sales_r) in our paper.
Model 1: Direct effects
lnSales_r=β0+ β1 lnRepu_score+ β2 lnPos_rate +β3
lnNe2_pos+β4 lnDetail_IaD +β5 lnPrice_r +β6 lnPrice_r
(1)
+β7N +ε
Model 2: Moderating effects
lnSales_r=β0+β1lnRepu_score+β2 lnPos_rate +β3
lnNe_pos +β4 lnDetail_IaD +β5 N*lnRepu_score+β6
N*lnPos_rate +β7 N*lnNe_pos +β8 N*lnDetail_IaD+β9
lnPrice_r +β10 N*lnPrice_r +β11N +ε
(2)
In above models,
Repu_score represents the overall accumulated
reputation score of a seller.
Pos_rate indicates the percentage of positive ratings
left by members in the last 12 months. It is
calculated by dividing the number of positive
ratings by the total number of ratings (positive
+ neutral+ negative ratings).
Detail_IaD represents the average rating on the
five-point scale detailed seller ratings for “Item
as Described.”
Ne_pos is calculated by dividing the number of
neutral and negative feedback ratings by
positive
feedback
ratings.
( Num_net + Num_neg )
Num_pos
. Num_pos,
Ne_pos=
Tab.1 Summary statistics
Variable
Mean
S.D.
Min.
Max.
Repu_score 14006.8300 38931.38000 1.00000
242288
Pos_ratio
0.99443
0.00758 0.89660
1.00000
Detail_IaD
4.88447
0.08656 4.22222
5
Ne_pos
0.04240
0.10528 0.00069
1.00000
Num_pos
492.7500
971.45636 0.00000
6273
Num_net
1.60965
4.13406 0.00000
28
Num_neg
0.97953
2.53228 0.00000
18
Price_r
1.00000
0.21693 0.36398
1.74711
Sales_r
1.00000
2.93773 0.01802
39.38643
4 Results
We conduct the log-linear regression analysis with
Canon digital camera data collected on Taobao.com.
Fig.1 illustrates the distribution of sellers’ reputation
scores. It gets close to a normal distribution,
with µ = 0，σ = 2.0969 .
The results for the two regression models are shown
in Tab.2 and Tab.3. It should be pointed out that all
independent variables have been centralized in our
regression models. For all the regression analyses, we
computed the variance inflation factor (VIF) and
observed that multicollinearity was not a significant issue;
the maximum observed VIF for all the regressions was
significantly less than the standard cutoff level of 10.
The results for the regression of Model 1 are shown
in Tab.2.
Num_net, Num_neg indicate the number of
positive feedback ratings, neutral feedback
ratings, and negative feedback ratings,
respectively, which the seller received in the
previous month.
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completion environment will obtain large sales volume.
Tab.3 shows the results for test moderating effect.
Comparing above two tables, we find that Adjust R2 in
Tab.3 is larger than that in Tab.2. In the former table
Adj-R2 is 0.1910, while Adj-R2 in Tab.3 is changed to
0.2025. That indicates that the model is more fitted when
introducing moderating variables.
From Tab.3 we can see that the coefficient of
N*lnDetail_IaD is positive (at p<0.05). That is to say the
number of sellers will enhance the effect of lnDetail_IaD
on sales. Similarly to Tab.2, lnRepu_score has a
significant positive correlation with sales (coefficient = 0.
0.1435, p < 0.01), and lnNe_pos has a negative
correlation with sales (coefficient = -0.3183, p < 0.01).
lnDetail_IaD has a weak impact on sales (at p<0.1).
These suggest a significant impact of reputation on sales.
In summary, the results suggest that more sellers in
a market will lead to a relatively strong relationship
between reputation and price. In other word, in more
competitive market, the positive correlation between
reputation and sales volume will be enhanced.
Another implication from our study comes from our
finding that buyers put more weight on recent feedback
than old. The coefficient of lnNe_pos is significant, while
the coefficient of lnPos_rate is insignificant. The
emphasis on recent negative feedback has also been
reported in field studies[8,41].
25
20
Percent
15
10
5
0
-7 -6 -5 -4 -3 -2 -1 0 1
lnrepu_score
Curve
2
3
4
5
Normal(Mu=0 Sigma=2.0969)
Fig.1 Distribution of centralized lnRepu_score
Tab.2 Results for regressions (Model 1)
Variable
Coef.
Std.
p
VIF
Intercept
-1.6871***
0.0585 <.0001 0
lnRepu_score
0.1309***
0.0358 0.0003 1.6659
lnNe_pos
-0.3299***
0.0546 <.0001 1.5559
lnPos_rate
1.7492
9.8688 0.8594 1.6880
lnDetail_IaD
8.2315*
4.7894 0.0861 2.1268
N
-0.0130***
0.0025 <.0001 1.0011
lnPrice_r
-1.0166***
0.2749 0.0002 1.2624
F.
27.87
<.0001
Adj-R2
0.1910
Tab.3 Results for regressions (Model 2)
Variable
Coef.
Std.
p
Intercept
-1.6919*** 0.0581 <.0001
lnRepu_score
0.1435***
0.0360 <.0001
lnNe_pos
-0.3183*** 0.0549 <.0001
lnPos_rate
4.4341
9.9177 0.6550
lnDetail_IaD
8.3539*
4.8829 0.0876
N
-0.0130*** 0.0025 <.0001
lnPrice_r
-1.1974*** 0.2804 <.0001
N*lnRepu_score
0.0018
0.0015 0.2521
N*lnNe2_pos
-0.0024
0.0023 0.2971
N*lnPos_rate
0.1552
0.4511 0.7310
N*lnDetail_IaD
0.4635**
0.2019 0.0220
F.
18.35
<.0001
Adj-R2
0.2025
5 Conclusions
VIF
0
1.7109
1.5950
1.7296
2.2427
1.0011
1.3329
1.6658
1.4800
1.5480
1.8136
We observed that lnRepu_score has a significant
positive correlation with sales (coefficient = 0.1309, p <
0.01), and lnNe_pos has a negative correlation with sales
(coefficient = -0.3299, p < 0.01). lnDetail_IaD has a
weak impact on sales (at p<0.1). These suggest a
significant impact of reputation level on sales. The
coefficient of N is significantly negative (coefficient
=-0.0130, p < 0.01). It supports hypothesis 2b. In general,
the results show that high reputable sellers in less
- 49 -
This paper contributes to extent reputation literature
by revealing the direct impact of reputation on sales and
moderating effect in their correlation.
The empirical results in our paper clearly
demonstrate the positive impact of reputation on sales.
When sellers with higher reputation score, detailed
ratings for “Item as Described” and lower negative, they
will attract more buyers to transact. Similar results have
also been reported in field studies (Livingston, 2005;
Cabral and Hortacsu, 2010; Li et al.).
In addition, we find evidence of a moderating effect
of number of competitors. Ye et al. (2013) suggest in
different competition level, the relationship between
reputation and sales volume is different. However they
did not analyze the quantitative impact of completion.
We in this paper extent their study, and reveal that
number of competitors has a negative effect on sales
volume, and it will enhance the positive correlation
between reputation and sales.
This research has several limitations. First, more
factors can be introduced as control or independent
variables in the future study. For example, some buyers
may post unstructured comments (text or photo). These
feedbacks may have a powerful influence on buyer’s
shopping decisions. Second, we exclude seller with no
transactions within a month. However these data may
also useful to analyze the impact of reputation. They may
be take into account in future research.
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