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Success of E-commerce Management System: A Case Study of eBay

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Abstract and Figures

Internet auctions have become a major aspect of E-Commerce, and most importantly eBay has become the world’s largest C2C auction site. This research investigates eBay business transaction features that influence auction success: ability to attract bidders and seller’s net earnings. Specifically, the study examines fifteen controlled auction factors about sellers; nine of the investigated factors are available to sellers to enhance revenue, while remaining six are listed to attract bidders to the auctions. Using four datasets of online auctions, one dataset for auctions of Apple Ipods (F = 119) , Dell Laptops (F = 134), Olympus Cameras (F = 120) and Motorola Cell phones (F = 118), multiple regression and logistic regression analyses are conducted to determine the impact of auction features on sellers’ net revenue and ability to attract bidders respectively. Findings indicate that the application of certain auction features such as initial bid price, and number of bids influence seller’s net revenue for all four products. Other features impacting seller’s revenue are shipping cost for product one, pictures and buy-now-option for products two. There was no evidence of auction duration impact and payment methods upon successful auctions. Additional auction factors influencing the ability to attract bidders were option of credit cards, presence of picture, lower starting price and reserve price for items one, three, four and four respectively. Further, no connection on new product and length of time of seller’s business establishment is found. In addition, findings indicate that one auction feature across each of the two models has an inverse impact on auction success for only one product. Research is beneficial for new online business ventures designing similar business tactics. It establishes and verifies certain strengths regarding inclusion of certain features while cautioning about practices having pessimistic effect.
Content may be subject to copyright.
P
ro
c
ee
d
i
ng
s
ICME
2012
230
230
230
Success of
E
-
c
o
mm
e
r
c
e
Management
System:
A
C
a
s
e
Study of
e
B
a
y
S
h
e
h
la
Z
a
m
a
n
1 a, Prof
Syed
A
m
j
a
d
Farid
Hasnu 2
b
1
COMSATS
University,
Quaid
A
v
e
nu
e
,
T
h
e
Mall,
Wah
C
a
n
tt
,
P
a
k
is
t
a
n
2
COMSATS
University,
T
ob
e
C
a
m
p
,
University
R
o
a
d
,
A
bbo
tt
a
b
a
d
,
P
a
k
is
t
a
n
a shehlazaman@hotmail.com, b h
a
s
nu
@ciit
.
n
e
t
.
p
k
K
e
y
words: internet auctions,
E
-
C
o
mm
e
r
c
e
,
eBay,
on
l
in
e
business
f
a
c
t
or
s.
A
b
st
r
a
c
t
:
I
n
t
e
rn
e
t
a
u
c
t
i
on
s have become a major aspect of
E
-
C
o
mm
e
r
c
e
,
and most importantly
eBay
has
b
e
c
o
m
e
the
w
or
ld
s largest
C2C
auction site.
This
research investigates
eBay
business transaction features
t
h
a
t
influence auction success: ability to attract bidders and s
e
lle
r
s net earnings.
Specifically,
the s
t
ud
y
examines fifteen controlled auction factors about s
e
ll
e
r
s
;
nine of the
i
n
v
e
s
t
ig
a
t
e
d
factors are available
t
o
sellers to enhance revenue, while
r
e
m
a
i
n
in
g
six are
li
s
t
e
d
to attract
b
i
dd
e
r
s to the auctions.
Using
f
our
datasets of online auctions, one dataset for auctions of
A
pp
le
I
pod
s (F = 119) ,
Dell
L
a
p
t
op
s (F = 134),
Olympus
C
a
m
e
r
a
s (F = 120) and Motorola
C
e
ll
phones (F = 118), m
u
l
t
i
p
le regression and
lo
gis
t
i
c
regression analyses are conducted to
d
e
t
e
r
m
i
n
e
the impact of auction features on s
e
lle
r
s
net
r
e
v
e
nu
e
and
ability
to attract bidders
r
e
s
p
e
c
t
iv
e
l
y
.
Findings
i
nd
i
c
a
t
e
that the application of certain auction features such as
initial
bid price, and number of
bids
in
f
l
u
e
n
c
e se
lle
r
s net revenue for
all
four products. Other features
i
m
p
a
c
t
in
g s
e
ll
e
r
s revenue
are
shipping cost for product one, pictures and buy-now-option for products two.
T
h
e
r
e
was no evidence of
auction duration impact and payment methods upon s
u
cc
e
ss
f
u
l
a
u
c
t
io
n
s.
A
dd
i
t
io
n
a
l
auction factors
influencing
the
ability
to attract
b
i
dd
e
r
s were option of credit cards,
pr
e
s
e
n
c
e
of picture,
l
o
w
e
r
starting
pr
i
c
e
and reserve
pr
i
c
e
for
i
t
e
ms one, three, four and four
r
e
s
p
e
c
t
iv
e
ly
.
F
ur
t
h
e
r
,
no connection on new product and length of time of s
e
lle
r
s business establishment is found.
I
n
addition,
findings in
d
i
c
a
t
e
that one auction feature across each of the two models has an inverse
im
p
a
c
t
on auction success for only one product.
R
e
s
e
a
r
c
h
is
b
e
n
e
f
i
c
ia
l
for new online business
v
e
n
t
ur
e
s
designing
similar
business
t
a
c
t
i
c
s.
I
t
establishes and verifies certain strengths regarding inclusion of
certain features while
c
a
u
t
i
on
in
g
about
pr
a
c
t
i
c
e
s
having
p
e
ss
im
is
t
i
c
e
ff
e
c
t
.
I
n
t
rodu
c
t
i
on
T
h
e
tremendous usage of internet is drastically
a
l
t
e
r
in
g
the buying and the
selling
of merchandise. On
hand
rich
information about products and price over the internet has made the
c
on
s
u
m
e
r
s
po
sit
io
n
much stronger and at the same time has undermined supremacy of fixed price model. One of
t
h
e
indications about the
fall
of fixed price marketing and sales is the emergence of the e-auction
(Song
&
Baker,
2007). Online
a
u
c
t
i
on
market has turned out to be one of the most remarkable advances in
E
-
C
o
mm
e
r
c
e
that is undoubtedly growing rapidly
(Chiu
et al. 2009). Online auctions have become s
o
popular in
l
a
s
t
few years.
T
r
e
m
e
ndou
s rise in users and product categories have been
ob
s
e
r
v
e
d
(
B
e
c
h
e
r
e
r
et
al
2008).
I
ndu
s
t
r
y
leader,
EBays
remarkable and continued growth
h
e
l
p
s in the
pro
g
r
e
ss
io
n
of this mode of pricing and service
(Song &Baker,
2007).
I
n
2007,
eBay
reported $7.67
billion
sales,
w
it
h
212
million
registered users
(eBay,
2007
a
)
.
A
study on
eBay
reports that buyers have a preference for
eBay
because of price
c
on
t
ro
l
while, s
e
lle
r
s
prefer it is
p
l
a
t
f
or
m for a
l
a
r
g
e
number of customers
(
V
r
a
g
o
v
,
2005).
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231
Given the continuous growth of ecommerce, a thorough understanding of online auctions has
b
e
c
o
m
e
the main focus of research scholars.
V
a
r
io
u
s economic,
psychological
and
sociological
aspects that
a
ff
e
c
t
sellers,
a
u
c
t
i
on
ee
r
s and bidders and that eventually determine the auction outcome has not yet
b
ee
n
explained clearly
(Dholakia,
2005).
F
a
c
t
or
s
d
e
t
e
r
m
i
n
in
g
E
-
C
o
mm
e
r
c
e
Success
in terms of seller
r
e
v
e
nu
e
(Song
&
Baker,
2007) and
ability
to attract bidders
(Bland
et
al.
2005) have been
e
x
a
m
i
n
e
d
in
d
e
p
e
nd
e
n
t
l
y
in early researches.
T
h
e
intent of the study is to evaluate factors of
EBay
M
a
n
a
g
e
m
e
n
t
M
od
e
l
that
i
n
f
lu
e
n
c
e
E
-
c
o
mm
e
r
c
e
(online auction)
Success,
by integrating these two perspectives
f
or
providing a better comprehension of the management
S
y
s
t
e
m. Here,
Success
is defined as ability
t
o
maximize s
e
ll
e
r
net revenue and
ability
to attract bidders.
T
h
e
r
e
f
or
e
,
the
pr
i
m
e
motive of the
r
e
s
e
a
r
c
h
enables the paper to make
c
on
t
r
ib
u
t
i
on
to literature by determining, analyzing and comparing
t
h
e
results of underlying success parameters based on an integrated m
od
e
l
;
M
od
e
l
1
(
A
b
ili
t
y
to
a
tt
r
a
c
t
Bidders),
would analyze the
i
m
p
a
c
t
of auction factors(starting price, reserve
pr
i
c
e
,
credit card,
n
e
w,
picture, s
e
ll
e
r
active) on the ability of auction to attract
b
i
dd
e
r
s and
M
od
e
l
2
(Seller
R
e
v
e
nu
e
)
would
analyze the parameters
(initial
b
i
d pr
i
c
e
,
buy-now-option,
a
u
c
t
i
on
duration, payment modes, s
e
lle
r
feedback, number of bids, number of pictures and shipping cost) of seller net revenue.
T
h
e
study
a
ls
o
aims to establish
credibility
of the m
od
e
l
for implementation into
local
firms. Many of these factors
a
r
e
under the s
e
ll
e
r
s
control,
identifying
them
will
h
e
l
p
them
improving
the outcome of their
a
u
c
t
io
n
s.
T
h
e
remainder of this
a
r
t
i
c
le is structured as
follows.
F
ir
s
t
,
a
li
t
e
r
a
t
ur
e
review is provided to discuss
t
h
e
factors that may
in
f
l
u
e
n
c
e
the seller revenue and ability to attract bidders, and research hypotheses
are
then developed.
S
e
c
ond
,
methodology
including
data
c
o
ll
e
c
t
io
n
and
d
e
s
c
r
ip
t
i
on
are presented.
T
h
ir
d
,
hypotheses are
empirically
tested and a discussion of the
r
e
s
u
l
t
s is also provided.
Finally,
the s
t
ud
y
closes with conclusions and implications for future
r
e
s
e
a
r
c
h
.
T
h
e
or
e
t
i
c
a
l
Development and Research
H
y
po
t
h
e
s
e
s
This
section
will
review a number of
Success
factors that may
i
n
f
lu
e
n
c
e
ability
of auctions to
a
tt
r
a
c
t
bidders and the seller net revenue,
including,
the starting
pr
i
c
e
,
the reserve price, use of credit
c
a
rd
,
presence of picture, use of new items, length of
t
i
m
e
seller has been registered with
eBay, initial
b
i
d
price,
B
u
y-
I
t
-
N
o
w
option, product
p
i
c
t
ur
e
s
,
auction duration, seller reputation, number of
b
i
d
s
,
shipping
cost and payment modes.
R
e
s
e
a
r
c
h
hypotheses are then developed
a
cc
ord
in
g
l
y
.
2.1 Factors for
A
b
ili
t
y
to attract Bidders (Model 1):
1) Starting Price
I
n
it
ia
l
bid price or s
t
a
r
t
i
n
g
price is the minimum bid a seller s
p
e
c
i
f
ies
(
T
h
e
default is $0.01) (Wan &
T
e
o
2001;
Bland
&
B
a
rr
e
tt
2004).
Bajari
& Hortacsu (2003) are of the view that
initial
b
i
d
pr
i
c
e
is the m
o
s
t
vital
measure of auction entry
d
e
c
is
io
n
.
A
number of studies report negative impact of s
t
a
r
t
i
n
g
pr
i
c
e
on the number of bidders
(
L
u
c
k
in
g
-
R
e
ile
y
,
2000;
Bajari
& Hortacsu 2003;
V
a
k
r
a
t
&
S
e
id
m
a
nn
2000;
Bland
and
B
a
rr
e
tt
,
2004;
Bland
et al. 2005
,
2007).
A
cc
ord
in
g
to
L
e
v
in
&
Smith
(1994) the reason for such
b
e
h
a
v
i
or
is that auction members
a
n
t
ic
ip
a
t
e
minimum profit in advance and by raising the
initial
bid price not only decreases the
b
id
d
e
r
s
e
x
p
e
c
t
e
d
profit but they also believe that
bidding
is something expensive to
und
e
r
t
a
k
e
.
H1a:
A
b
ili
t
y
to attract bidders in online auction is increased by a lower starting
pr
i
c
e
.
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2) Reserve Price
On “listing agent sites”, sellers select reserve price in addition to other
a
u
c
t
i
on
parameters
(
L
u
c
k
in
g
-
Reiley,
1999). Hence, the auction
a
tt
r
i
bu
t
e
d
e
t
e
r
m
i
n
e
d
by the seller, s
ig
n
if
ic
a
n
t
ly
affecting bidding
a
nd
auction outcome is reserve price
(Bland
&
B
a
rr
e
tt
2004; Pinker et al. 2003;
Y
e
t
o
et al. 2007).
R
e
s
e
r
v
e
price is the minimum
pr
i
c
e
below which the seller is not ready to accept any price offer to
sell
t
h
e
product
(Bajari &
Hortacsu, 2003).
A
number of studies report negative
i
m
p
a
c
t
of reserve
pr
i
c
e
on the number of bidders
(Budish
a
nd
T
a
k
e
y
a
m
a
2001;
Bajari
and Hortacsu , 2003;
Li
et
al.
2008;
Standifird,
2001;
Bland
and
B
a
rr
e
tt
2004
;
Bland
et al. 2007).
I
t
is because
b
i
dd
e
r
s try to circumvent the possibility of failure by
practicing
“price-
aversion s
t
r
a
t
e
g
y
(
K
a
hn
e
m
a
n
&
Tversky,
1979). While,
V
inc
e
n
t
(1995) argues that for these bidders
t
h
e
only chance of
winning
is to win at the reserve price, and it happens when the
i
t
e
m is not of much
v
a
lu
e
for bidders, which
proh
i
b
i
t
s the entry of bidders
in
this case.
C
on
s
e
qu
e
n
t
ly
,
the investigation
a
n
t
ic
i
p
a
t
e
s
negative impact of reserve price on the number of
b
i
dd
e
r
s.
H2:
Presence of reserve price increases the number of
b
i
dd
e
r
s.
3)
C
r
e
d
i
t
C
a
rd
C
r
e
d
it cards are in
g
e
n
e
r
a
l
equally convenient to both buyers and sellers
(
Y
a
n
g
et al. 2007).
T
h
e se
lle
r
accepting payment via credit card has the advantage of
quick
money transfer
which
bye passes
t
h
e
lengthy process of money exchange s
i
n
c
e
the
automated
exchange is immediately accessible
(Bland
&
B
a
rr
e
tt
,
2004). Payments made by the buyer are
c
o
ll
e
c
t
e
d
w
i
t
hou
t
human intervention and then sent
t
o
the s
e
lle
r
s bank promptly by means of
e
le
c
t
ron
i
c
transmission.
Bland
and
B
a
rr
e
tt
(2004) have
f
ound
that the payments via credit card
r
e
s
u
l
t
in
high
final
pr
i
c
e
s and also more
p
a
r
t
i
cip
a
t
io
n
of
b
i
dd
e
r
s.
B
la
nd
et al. (2005) also reported the positive
i
m
p
a
c
t
of credit card payments on the number of
b
id
d
e
r
s.
H3:
A
b
ili
t
y
to pay with a credit card increases the number of
b
i
dd
e
r
s.
4)
P
i
c
t
ur
e
I
n
order to minimize the
risk
related to the product condition and quality, sellers can upload pictures of
the auctioned item
(
E
a
t
on
,
2002). Picture posting in online auctions is
op
t
io
n
a
l
and incurs certain
c
o
s
t
s.
I
n
clu
d
in
g
a product picture with auctions gives bidders an opportunity to “window shop” prior to m
a
k
e
offers, therefore, this
a
tt
r
i
bu
t
e
is deemed to reduce the element of risk attached with
buying
items
on
eBay
which ultimately increases the auction success
(Bland
et al. 2005).
A
number of studies
in
li
t
e
r
a
t
ur
e
provide support for the positive
i
m
p
a
c
t
of pictures on the bidding
performance in online auctions.
A
cc
ord
in
g
to
E
a
t
on
(2002) the
likelihood
of
bidding activity
in
a
u
c
t
io
n
s
offering
p
i
c
t
ur
e
s is more than those
which
do not provide
p
i
c
t
ur
e
s.
A
u
c
t
io
n
s
including
a picture are
l
ik
e
ly
to serve as the focus of attention for bidders
(Bland
and
B
a
rr
e
tt
,
2004).
H4:
Presence of a picture shall result in higher number of
b
i
dd
e
r
s.
5) Use of New
P
rodu
c
t
s
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COMSATS Institute of Information Tecnology, Sahiwal
T
h
e
i
n
f
or
m
a
t
io
n
about the condition of products particularly
u
s
e
d
/
n
e
w
is directly related to
produ
c
t
quality
(Li
et al. 2008).
T
h
e
items that are scratched appear to be less
a
pp
e
a
li
n
g
to the buyers and
a
ls
o
result
in
a reduced price
(Gilkeson
&
Reynolds,
2003).
A
nd
e
r
s
on
et al. (2004) associate superior
f
e
a
t
ur
e
with new” or “undamaged” m
e
r
c
h
a
nd
i
s
e
.
A
number of
empirical
studies address the impact of product condition on the auction outcome. Using
eBay c
a
l
c
u
la
t
or
s
,
Bland
and
B
a
rr
e
tt
(2004) propose that signs of wear reduce the number of
b
id
d
e
r
s
,
likewise presence of words “new” or new in box”
in
the
d
e
s
c
r
ip
t
i
on
of
c
a
l
c
u
la
t
or
attracts more
b
id
d
e
r
s
and increases
final
price.
Similar s
t
ud
i
e
s indicate that the number of bidders and the auction s
u
cc
e
ss
(Bland
et al. 2005), occurrence of transaction and the
winning
b
i
d pr
i
c
e
(Bland
et al. 2007) is
likely
t
o
decrease if the seller mentions the word “wear” in the description. While positive phrases like new
i
n
box” or “never opened, increase the chance of transaction and the
final
bid price
(Bland
et al. 2007),
reduce the perceived
risk
by the bidders and
r
e
s
u
l
t
s
in
high revenue
(Song & Baker,
2007).
H5:
Presence of new products increases the number of
b
i
dd
e
r
s.
6) Seller
A
c
t
i
v
e
L
on
g
e
v
it
y
of seller indicates how long a seller has been active, thus,
specifying
the time period in m
on
t
h
s
since a s
e
ll
e
r
is registered with
eBay. This
parameter helps buyers to find honest s
e
ll
e
r
s and also m
on
it
or
their business activities on
eBay.
Hence, a seller who has been active for long on
eBay,
is a
signal
of
h
i
s
good repute according to
e
B
a
y
s
policy
and also that the seller has not been involved in any
t
r
a
n
s
a
c
t
io
n
due to
which
he could be banned by
eBay (Bland
et al. 2005).
A
few resent researches
(Bland
et al. 2005
;
Bland
and
B
a
rr
e
tt
,
2004) postulated positive
i
m
p
a
c
t
of the auction
a
tt
r
i
bu
t
e
on the number of
b
id
d
e
r
s.
T
hu
s
,
in light
of above discussion this research proposes that the length of
t
i
m
e
a particular seller is
active on
eBay
is
positively
related to the number of
b
id
d
e
r
s.
H6:
Number of bidders
attracted
to the auction is increased by the longevity of seller
a
c
t
i
v
i
t
y
.
2.2 Factors for Seller Revenue (Model 2) :
1)
I
n
i
t
i
a
l
bid
pr
i
c
e
I
n
it
ia
l
bid
pr
i
c
e
is positively related to auction
final
price
(Bajari
and Hortacsu 2003;
B
r
in
t
2003;
H
a
ub
l
and
L
e
s
z
c
z
y
c
2003;
Reynolds
et al. 2008;
K
im
,
2007;
Ariely
and
Simonson
2003).
This
po
sit
i
v
e
in
f
lu
e
n
c
e
of
initial
bid price on auction
final
bid is
e
x
p
la
i
n
e
d
as a consequence of “value
c
on
s
t
ru
c
t
io
n
phenomenon.
(Haubl
and
L
e
s
z
c
z
y
c
2003;
K
a
m
in
s et al. 2004), where s
t
a
r
t
i
n
g
pr
i
c
e
serve as a cue of
product
qu
a
li
t
y
and thus helping bidders construct their
v
a
lu
a
t
i
on
about such products that drives
up
the price
(Li
et al. 2004).
H1
a: Seller revenue is increased by high initial bid
pr
i
c
e
.
2)
B
u
y
-
no
w
-
op
t
i
on
A
u
c
t
io
n
s
in
essence offer a variable pricing mechanism.
But
in resent
t
i
m
e
s (2001) the major
on
lin
e
auction site
like eBay
has launched a
fixed
pr
i
c
e
mechanism called “buy it now” (Melnik &
Alm,
2004
;
Wang et al. 2004).
A
buy-now price is a kind of m
a
x
i
m
u
m bid that is specified by the seller (Pinker et
a
l
.
P
ro
c
ee
d
i
ng
s
ICME
2012
234
234
234
1st International Conference on Management and Ecomomics 2012
COMSATS Institute of Information Tecnology, Sahiwal
2003) which acts as an upper
limit
to bid .
T
h
e
concept of “buyout price” incorporates that the
bu
y
e
r
submits
sufficiently
high bid
(
L
u
c
k
in
g
-
R
e
ile
y
,
1999).
A
t
this
high
pr
i
c
e
the seller becomes ready
t
o
dispatch the product in order to end the auction soon
(Budish &
T
a
k
e
y
a
m
a
2001).
T
h
e
r
e
are s
e
v
e
r
a
l
notable studies in this area.
Song
&
Baker
(2007) indicate that the presence of buy-
now option has a negative impact on the s
e
ll
e
r
e
a
rn
i
n
g
s.
T
h
e
reason for this is that
w
i
t
h
the
bu
y-
ou
t
option the seller is
d
e
pr
i
v
e
d
of
po
ss
i
b
le higher
b
i
d
s than the buy
pr
i
c
e
(Hian,
2004). Wang (1993)
postulates that
po
s
t
e
d
-
pr
i
c
e
tends to fetch lower revenue than auctions and this variation increases
a
s
more customers become ready to pay.
Li
et al. (2008) exclaim that buy-now-option
which is
the one of
the components of “indirect quality
in
d
i
c
a
t
or
s
that does not encourage
b
i
dd
e
r
s to
p
a
r
t
i
cip
a
t
e
.
T
h
e
r
e
f
or
e
,
the above
analysis
leads to the formation of
following
h
y
po
t
h
e
s
is
:
H2:
Presence of Buy-now-option increases the seller
r
e
v
e
nu
e
.
3)
A
u
c
t
i
on
dur
a
t
i
on
L
u
c
k
i
n
g
-
R
e
ile
y
(1999)
d
e
f
i
n
e
s duration as the time span at which the
nor
m
a
l
auction remains active.
I
n
contrast to the
i
n
-
p
e
r
s
on
auction where auctioneer specifies the auction duration, the internet
a
u
c
t
io
n
gives the s
e
ll
e
r
an opportunity to set the start and the end
t
i
m
e
of auction
in
days.
For
i
n
s
t
a
n
c
e
at
e
B
a
y
auctions, s
e
ll
e
r
s opt a duration of 3, 5, 7, or 10 days
(Bland
&
B
a
rr
e
tt
,
2004). Previous studies
h
a
v
e
empirically
shown that longer auctions are
likely
to result
in high
seller revenue
(Song
&
Baker,
2007)
and high number of
b
i
d
s and the winning price
(Reynolds
et al. 2008).
C
on
c
e
iv
a
b
ly
,
auctions with
lo
n
g
e
r
durations provide more bidders the chance to
view
items (Wan and
T
e
o
,
2001) and also
g
r
e
a
t
e
r
f
le
x
ib
ili
t
y
in bidding the products (Wan et al. 2003) that
u
lt
i
m
a
t
e
l
y
raises
a
u
c
t
i
on
selling
pr
i
c
e
(Wan
a
nd
T
e
o
,
2001).
B
a
s
e
d
on the above analyses, the
following
h
y
po
t
h
e
s
i
s could be s
t
a
t
e
d
:
H3:
L
on
g
e
r
the auction duration, higher is the seller
r
e
v
e
nu
e
.
4) Payment
op
t
i
on
s
With the advent of
i
n
t
e
rn
e
t
auction markets, as one of the most important channels for
e
le
c
t
ron
ic
commerce, payment methods have become a
vital
concern
(Li &
Z
h
a
n
g
,
2006).
Several
methods have been devised and employed in
C2C
a
u
c
t
i
on
markets to enhance trust,
r
e
du
c
e
hazards and to ensure
i
n
s
t
a
n
t
internet transactions
(
Y
a
n
g
et al. 2007).
T
hu
s payment modes
e
nh
a
n
c
e
the convenience of
c
o
m
p
l
e
t
in
g
online auction transactions
(Song
&
Baker,
2007), which is preferred by
online buyers
(Burke,
1997;
Syzmanski
& Hise, 2000).
Being
able to choose freely from a number of
payment
op
t
i
on
s like checks, money orders, credit cards, or
c
r
e
d
i
t
cards, makes the transaction m
or
e
convenient for a buyer. With more payment methods buyers pay less for transactions, which
u
lt
im
a
t
e
ly
raise the s
e
ll
e
r
revenue
(Song & Baker,
2007).
H4:
More payment options result in higher net
r
e
v
e
nu
e
.
5) Seller
Fe
e
db
a
c
k
P
ro
c
ee
d
i
ng
s
ICME
2012
235
235
235
1st International Conference on Management and Ecomomics 2012
COMSATS Institute of Information Tecnology, Sahiwal
Seller
feedback system (or
r
e
pu
t
a
t
i
on
system) established
in
internet auctions have become
a
foundation
in
e
-
C
o
mm
e
r
c
e
(
B
e
c
h
e
r
e
r
et al. 2008).
T
h
e
feedback system
f
a
c
ili
t
a
t
e
s trust, from
in
t
e
rn
e
t
perspective, trust is
vital
because of the dependence of online buyers for buying and other
i
n
f
or
m
a
t
io
n
on internet
(
B
e
c
h
e
r
e
r
et
al
2008;
B
a
r
t
et al., 2005).
By
establishing this feedback rating system,
e
B
a
y
has been
a
b
l
e
to earn a good
will. EBay
provides the opportunity to both s
e
ll
e
r
s and
winning
bidders
t
o
post publicly positive, negative or
n
e
u
t
r
a
l
r
a
t
i
n
g
s on completion of each transaction
(
B
e
c
h
e
r
e
r
et
a
l
2008).
A
number of
empirical
researches have examined the impact of seller feedback on auction success,
w
i
t
h
mixed
findings
(e.g.,
Ba &
Pavlou, 2002;
McDonald & Slawson,
2002;
Resnick &
Z
e
c
k
h
a
u
s
e
r
,
2002).
A
f
e
w
studies found the
i
n
f
lu
e
n
c
e
of positive feedback only (e.g.,
Bajari &
Hortacsu, 2003). While, some
r
e
por
t
the impact of negative feedback (e.g.,
L
ee
et al. 2006).
Melnik
and
Alm
(2002) and
Standifird
(2001)
bo
t
h
found a
po
sit
i
v
e
effect of seller
r
e
pu
t
a
t
i
on
on
pr
i
c
e
s.
Signal
T
h
e
or
y (
S
p
e
n
c
e
,
1973)
explains
the
t
h
e
or
e
t
i
c
a
l
basis for the positive
i
m
p
a
c
t
of s
e
l
le
r
feedback
on
auction
final
prices.
I
n
an online framework,
signaling
becomes
vital
when
i
n
f
or
m
a
t
io
n
related to quality
is usually not clear to buyers before purchase
(Schlosser,
et al. 2006).
I
n
such a case,
Seller
f
ee
db
a
c
k
ratings
c
ou
l
d
serve up as an
in
d
i
c
a
t
or
to product
qu
a
l
it
y (
B
e
c
h
e
r
e
r
et
al
2008).
T
h
e
r
e
f
or
e
,
this
r
e
s
e
a
r
c
h
addresses the
following
h
y
po
t
h
e
s
e
s.
H5: High
positive feedback rating leads to higher net
r
e
v
e
nu
e
.
H6:
L
o
w
negative feedback rating leads to higher net
r
e
v
e
nu
e
.
6) Product
P
i
c
t
ur
e
s
With the advent of internet, e-auction enterprises are
coming
up with such advancements that allow
sellers to
unveil
more details to their customers
r
e
l
a
t
e
d
to their
in
t
e
g
r
i
t
y
and product
qu
a
li
t
y
(Li
et al.
2008).
A
cc
ord
in
g
to
Heijst
et al.(2008) certain aspects of information are hard to interpret by simply
providing
statements.
For
instance a
s
lig
h
t
ly
worn s
ho
e
could be badly damaged or be in
n
e
a
r
-
m
in
t
c
ond
it
i
on
. Under such circumstances photographs give bidders
h
e
lp
f
u
l
and precise
i
d
e
a
about
produ
c
t
s.
Since
2001,
eBay
has provided
i
t
s s
e
ll
e
r
s the opportunity to
mail
m
u
l
t
ip
le photographs at the cost of
certain fees.
A
cc
ord
in
g
to
Song & Baker
(2007) product information is obtained by noting the number of
product pictures.
An empirical
study has explained that pictures of a an online auctioned product may
influence information processing and eventually the success of the sale (Ottaway et
al.
2003).
A
number of studies from
li
t
e
r
a
t
ur
e
indicate a positive impact of
p
i
c
t
ur
e
on the
a
u
c
t
i
on ou
t
c
o
m
e
.
Dewally
and
E
d
e
r
in
g
t
on
s (2006) s
a
m
p
l
e
has 3,664 auctions, 96.5% of which have a picture, while
Li
e
t
al. (2004) report 98% of their auctions have a picture.
B
o
t
h
studies find that auctions with
produ
c
t
pictures
r
e
c
e
i
v
e
a s
ig
n
if
i
c
a
n
t
ly
higher price than those without any
p
ic
t
ur
e
.
T
h
e
s
e
researches use a dummy
v
a
r
i
a
b
le to
e
s
t
i
m
a
t
e
the impact of
p
i
c
t
ur
e
use. Which
l
e
a
d
s
t
o
information
loss
as
all
the auctions are treated
alike
regardless of the number of pictures they
inc
l
ud
e
.
So
one way to overcome this
prob
l
e
m is to use continuous measure of picture, that is number of
pictures posted for the same auction, with the thought that more
p
i
c
t
ur
e
s mean
i
m
pro
v
e
d produ
c
t
information.
T
hu
s
,
the research extends the previous literature by postulating that more pictures m
e
a
n
more seller
r
e
v
e
nu
e
.
P
ro
c
ee
d
i
ng
s
ICME
2012
236
236
236
1st International Conference on Management and Ecomomics 2012
COMSATS Institute of Information Tecnology, Sahiwal
H7:
Greater numbers of pictures are associated with higher net
r
e
v
e
nu
e
.
7) Number of
b
i
d
s
I
n
auctions the number of bids is related to high auction
pr
i
c
e
(
L
u
c
k
in
g
-
R
e
i
le
y
et al. 2000;
McDonald
a
nd
Slawson,
2002;
V
inc
e
n
t
,
1995;
Reynolds
et al. 2008).
A
cc
ord
in
g
to Wilcox (2000) abnormally high number of
b
i
d
s attract large number of
b
i
dd
e
r
s
w
h
i
c
h
signify important
i
n
f
or
m
a
t
io
n
to the bidders. When more
i
n
f
or
m
a
t
io
n
about the product
qu
a
li
t
y
is
available to bidders, more value is perceived by
b
i
dd
e
r
s
,
which
results in
high
seller revenue
(
V
i
n
c
e
n
t
,
1995).
Gilkeson, &
Reynolds
(2003)
in
d
i
c
a
t
e
that
“bidding
momentum” or
a
u
c
t
i
on
fever” is s
o
m
e
t
h
in
g
observed in auctions where a large number of
b
i
dd
e
r
s
p
a
r
t
ic
i
p
a
t
e
.
I
t
involves bidding to start at a low
price;
which
allows bidders to get “caught up” in the
bidding
activity and eventually leads to high
f
in
a
l
prices and auction s
u
cc
e
ss.
H8:
Higher numbers of bids are associated with higher net
r
e
v
e
nu
e
.
8) Shipping
c
o
s
t
Sellers usually
specify
shipping
choices and charges for buyers.
Since
buyers are charged for shipping
their
i
t
e
ms
(Bland
&
B
a
rr
e
tt
,
2004), therefore, paying shipping prices by buyers is something
u
s
u
a
l
at
eBay.
L
o
w
e
r
shipping costs are related to higher net revenue
(Song
&
Baker,
2007) and also high
f
in
a
l
prices
(Bland
et al. 2007;
McDonald & Slawson
2002).
An
i
m
por
t
a
n
t
rationale for this is that with lower shipping costs the net amount paid by
b
i
dd
e
r
s for
t
h
e
item purchased is reduced.
A
p
a
r
t
from the apparent s
t
a
t
is
t
ic
a
l
effect that lower shipping cost is
likely
t
o
have on seller net revenue, it also may contribute to attract a large number of bidders who perceive
a
“better deal” on an
i
t
e
m with a low shipping cost.
Swinyard
and
Smith
(2003) point out that
on
li
n
e
buyers are sensitive to
high
shipping costs and according to (Gilkeson &
Reynolds,
2003) they
v
a
lu
e
convenient transaction and low
c
o
s
t
.
H9:
L
o
w
e
r
shipping costs are associated with higher net
r
e
v
e
nu
e
.
M
e
t
hodo
l
o
gy
3.1 Sample
&
D
a
t
a
eBay
is selected as the data source because of its dominant role in the internet auction
in
du
s
t
r
y
.
Secondly, eBay
has been commonly used as the
po
i
n
t
of data
c
o
l
le
c
t
io
n
in the realm of
on
li
n
e
a
u
c
t
io
n
s
by numerous studies
(Song
&
Baker
2007;
Bland
and
B
a
rr
e
tt
,
2004;
Bland
et al. 2005;
Bland
et al. 2007).
Four
consumer electronics products:
A
pp
le
I
pod
,
Dell
L
a
p
t
op
,
Motorola
C
e
ll
Phone, and Olympus
D
ig
i
t
a
l
C
a
m
e
r
a
are chosen as the study object for the
following
reasons.
F
ir
s
t
,
e
l
e
c
t
ron
ic
appliances are
a
m
on
g
the
c
o
mm
on
l
y
traded consumer products in online auctions.
Secondly,
their
high
sales volume
f
a
c
ili
t
a
t
e
s
data
c
o
llec
t
i
on
,
by ensuring data availability for completed auctions on daily basis.
Finally,
early
researches on
similar
models have been carried out on products
like
DVD
and
MP3
Player
(Song &
b
a
k
e
r
,
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2007) and
financial
calculators
(Bland
et al. 2005). Hence, the study intends to
fill
the gap in
li
t
e
r
a
t
ur
e
by
conducting the
similar
research on these four
aforementioned
e
l
e
c
t
ron
i
c
produ
c
t
s.
Data for completed is
c
o
ll
e
c
t
e
d
at
eBay US
during three
w
ee
k
s
period (11 Dec., 2008- 31 Dec., 2008),
on
daily
basis.
T
h
e
span of data collection
is
limited to three weeks
in
order to circumvent the affects on
t
h
e
item prices due to change in market
pr
i
c
e
s.
Overall,
the data set includes 491 single item completed auctions.
Of
these auctions, 134 are for the
d
e
ll
laptop, (119) apple
I
pod
,
(118) Motorola cell, (120)
Olympus
C
a
m
e
r
a
.
3.2
V
a
r
i
a
b
l
e
s
For
each auction
in
f
or
m
a
t
i
on
collected related to variables are
l
a
b
e
le
d
and defined
in
T
a
b
le 1 and
t
h
e
summary statistics are given in
T
a
b
le 2 and
T
a
b
le 3.
T
a
b
l
e
1:
V
a
r
i
a
b
l
e
s
D
e
f
i
n
i
t
i
on
V
a
r
i
a
b
l
e
s
D
e
f
i
n
i
t
i
on
Model 1
:
D
e
p
e
nd
e
n
t
V
a
r
i
a
b
l
e
:
Number of
b
i
dd
e
r
s
(
A
b
ili
t
y
to attract
b
i
dd
e
r
s)
Number of
d
i
s
t
inc
t
b
i
dd
e
r
s who had placed a bid
dur
i
n
g
the
a
u
c
t
io
n
I
nd
e
p
e
nd
e
n
t
V
a
r
i
a
b
l
e
s
:
C
u
s
t
o
m
e
r
s Perceived
R
is
k
S
t
a
r
t
in
g
Price
An
it
e
m
s starting bid plus shipping
f
ee
.
R
e
s
e
r
v
e
Price
P
r
e
s
e
n
c
e
/
a
b
s
e
n
c
e
of a reserve
pr
ic
e
.
C
r
e
d
it
C
a
rd
Presence or absence of option paying
w
i
t
h
a
c
r
e
d
i
t
c
a
rd
.
P
ic
t
ur
e
Presence or absence of auctioned product
p
ic
t
ur
e
.
n
e
w
P
r
e
s
e
n
c
e
/
a
b
s
e
n
c
e
of new
produ
c
t
.
Seller
A
c
t
iv
e
Number of months
p
a
r
t
i
c
u
la
r
seller has been active on
eBay.
Model 2 ( Seller
R
e
v
e
nu
e
)
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COMSATS Institute of Information Tecnology, Sahiwal
Dependent
v
a
r
i
a
b
l
e
Seller
Net
R
e
v
e
nu
e
Highest bid s
ub
m
i
tt
e
d
(final
bid price)
l
e
ss the
shipping
c
o
s
t
I
nd
e
p
e
nd
e
n
t
V
a
r
i
a
b
l
e
s
:
I
n
it
ia
l
bid
pr
ic
e
T
h
e
price above which the first bidder to enter an auction must bid.
Buy-now
op
t
i
on
Presence or absence of the option for a bidder to end an auction early
by purchasing at a seller
d
e
t
e
r
m
i
n
e
d
fixed
pr
i
c
e
(
e
B
a
y
s buy-it-now
op
t
io
n
)
.
Number of
payment m
e
t
hod
s
Number of payment methods available to
b
id
d
e
r
s.
A
u
c
t
io
n
dur
a
t
io
n
L
e
n
g
t
h
of auction in
d
a
y
s.
Number of
po
sit
iv
e
f
ee
db
a
c
k
T
o
t
a
l
number of positive feedback ratings for s
e
lle
r
.
Number of
n
e
g
a
t
iv
e
f
ee
db
a
c
k
T
o
t
a
l
number of negative feedback
r
a
t
i
n
g
s for s
e
lle
r
.
Number of
p
ic
t
ur
e
s
Number of
p
i
c
t
ur
e
s.
Number of
b
i
d
s
T
o
t
a
l
number of bids submitted for a given it
e
m.
Shipping c
o
s
t
A
m
oun
t
of shipping and handling
c
h
a
r
g
e
s.
T
a
b
l
e
2: Summary Statistics (Model 1):
A
b
ili
t
y
to
A
tt
r
a
c
t
B
i
dd
e
r
s
P
rodu
c
t
M
i
n
i
m
u
m
M
a
x
i
m
u
m
M
ea
n
Std.
D
e
v
i
a
t
i
on
Dell
L
a
p
t
op
0.01
900
68.05
134.8
Apple
I
pod
0.01
500
46.26
72.98
Motorola
C
e
l
0.01
180
28.25
33.98
Olympus
C
a
m
e
r
a
0.01
599
66.08
125.37
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Dell
L
a
p
t
op
0
1
0.11
0.31
Apple
I
pod
0
1
0.06
0.25
Motorola
C
e
l
0
1
0.05
0.22
Olympus
C
a
m
e
r
a
0
1
0.066
0.24
Dell
L
a
p
t
op
0
1
0.29
0.45
Apple
I
pod
0
1
0.04
0.20
Motorola
C
e
l
0
1
0.10
0.30
Olympus
C
a
m
e
r
a
0
1
0.05
0.21
Dell
L
a
p
t
op
0
1
0.97
0.14
Apple
I
pod
0
1
0.90
0.29
Motorola
C
e
l
0
1
0.95
0.20
Olympus
C
a
m
e
r
a
0
1
0.91
0.27
Dell
L
a
p
t
op
0
1
0.10
0.30
Apple
I
pod
0
1
0.44
0.49
Motorola
C
e
l
0
1
0.22
0.41
Olympus
C
a
m
e
r
a
0
1
0.21
0.41
Dell
L
a
p
t
op
0
135
58.50
34.14
Apple
I
pod
0
131
58.35
41.48
Motorola
C
e
l
0
138
59.23
33.73
Olympus
C
a
m
e
r
a
0
134
67.61
38.25
T
a
b
l
e
3: Summary Statistics (Model 2): Seller
R
e
v
e
nu
e
V
a
r
i
a
b
l
e
P
rodu
c
t
M
i
n
i
m
u
m
M
a
x
i
m
u
m
M
ea
n
S
t
d.
D
e
v
i
a
t
i
on
I
n
i
t
i
a
l
Bid
Price
Dell
L
a
p
t
op
.01
1800.00
81.47
201.65
Apple
I
pod
.01
500.00
46.26
73.29
Motorola
C
e
l
.01
180.00
28.26
34.139
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Olympus
C
a
m
e
r
a
.01
599.00
67.13
126.69
P
a
y
m
e
n
t
O
p
t
i
on
s
Dell
L
a
p
t
op
1
5
1.81
1.32
Apple
I
pod
1
4
1.08
.42
Motorola
C
e
l
1
4
1.27
.82
Olympus
C
a
m
e
r
a
1
4
1.14
.61
A
u
c
t
i
on
D
ur
a
t
i
on
Dell
L
a
p
t
op
1
10
3.95
2.78
Apple
I
pod
1
10
4.09
2.50
Motorola
C
e
l
1
10
3.68
2.39
Olympus
C
a
m
e
r
a
1
10
4.97
2.38
P
o
si
t
v
e
Fe
e
db
a
c
k
Dell
L
a
p
t
op
0
1408
232.43
385.69
Apple
I
pod
0
27252
642.56
2647.50
Motorola
C
e
l
0
6642
699.31
1198.53
Olympus
C
a
m
e
r
a
0
10874
851.21
1931.63
Negative
Fe
e
db
a
c
k
Dell
L
a
p
t
op
0
10
1.62
2.64
Apple
I
pod
0
74
3.27
9.97
Motorola
C
e
l
0
103
7.20
17.15
Olympus
C
a
m
e
r
a
0
146
5.55
19.62
N
u
m
b
e
r
Of
P
i
c
t
ur
e
s
Dell
L
a
p
t
op
0
11
2.13
1.99
Apple
I
pod
0
7
1.19
0.91
Motorola
C
e
l
0
11
1.24
1.08
Olympus
C
a
m
e
r
a
0
10
1.82
1.64
N
u
m
b
e
r
Of Bids
Dell
L
a
p
t
op
0
44
16.45
10.76
Apple
I
pod
0
62
13.48
10.89
Motorola
C
e
l
0
45
6.44
7.80
Olympus
C
a
m
e
r
a
0
54
9.22
9.50
Shipping
Dell
L
a
p
t
op
.00
100.00
14.28
14.8
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C
o
st
Apple
I
pod
0
25.00
6.97
6.39
Motorola
C
e
l
.00
20.00
4.67
4.63
Olympus
C
a
m
e
r
a
.00
35.00
6.96
6.43
I
nd
e
p
e
nd
e
n
t
c
a
t
e
g
or
ic
a
l
variable
B
u
y-
no
w
-
op
t
io
n
(Y:
offered,
N:
not
o
ff
e
r
e
d
)
Olympus
C
a
m
e
r
a
N=93,
Y
=
27
Motorola
C
e
ll
=
N=66,
Y
=
51
Dell
L
a
p
t
op
=
N=112,
Y
=
22
A
pp
le
I
pod
=
N=100,
Y
=
18
3.3 Data
A
n
a
l
y
si
s:
Since
the
dependent
variable for the hypotheses entailed
in
m
od
e
l
1 is a dummy variable for whether
or
not a
p
a
r
t
i
c
u
la
r
auction attracts
b
i
dd
e
r
s , i.e. the auction has the ability to attract
b
i
dd
e
r
s
,
logistic
regression analysis is used to determine the
i
n
f
lu
e
n
c
e
of factors as a predictor of number of
b
id
d
e
r
s.
Whereas, for m
od
e
l
2, Ordinary
L
e
a
s
t
S
qu
a
r
e
s
(O
L
S
)
regression
is
used to test the
significance
of
e
a
c
h
independent variable as a predictor of seller net revenue.
T
a
b
les 4 and 5 provide the regression
r
e
s
u
l
t
s
for the two sets of hypotheses
t
e
s
t
s.
R
e
su
l
t
s
4.1 Results of
L
o
g
i
st
i
c
Regression (Model 1):
T
a
b
le 4 provides the
r
e
s
u
l
t
s of
lo
gis
t
ic
regressions of
ability
to attract bidders
T
h
e
dependent
variable is if the auction ends
w
i
t
h
bidders count>0 (coded 1) or failed to attract
a
n
y
bidder (coded
0).on
the auction variables listed in
T
a
b
le 4 for each of the four product
t
y
p
e
s.
T
h
e
results indicate that starting price has a
significant
negative
i
m
p
a
c
t
on the number of
b
i
dd
e
r
s for
a
ll
four products.
This
shows that number of bidders decrease with an increase in the starting price.
H
e
n
c
e
,
accepting the Hypothesis (H1).
R
e
s
e
r
v
e
pr
i
c
e
a
u
c
t
i
on
s are found to have a significant positive
i
m
p
a
c
t
across
all
the four products,
which
show that the reserve option
i
n
c
r
e
a
s
e
s the number of
c
u
s
t
o
m
e
r
s
,
thereby
providing full
support for the hypothesis
(H2).
I
n
a
dd
i
t
io
n
,
having a picture is found to
inc
r
e
a
s
e
the number of bidders for three of the four products
(Olympus
C
a
m
e
r
a
,
Motorola
C
e
ll
and
dell
L
a
p
t
op
)
but not for the
A
pp
le
I
pod
auctions.
This
finding shows
p
a
r
t
ia
l
support for the hypothesis (H4). Having
an option to pay with a credit card has a
significant
positive impact on the number of bidders for
d
e
ll
laptop, but not for the Olympus
C
a
m
e
r
a
and Motorola
cell
auctions.
Surprisingly,
its impact on
t
h
e
number of
b
i
dd
e
r
s is found to be s
ig
n
if
i
c
a
n
t
ly
n
e
g
a
t
i
v
e
for the
A
pp
le
I
pod
a
u
c
t
i
on
s.
This finding
p
a
r
t
ia
ll
y
supports the hypothesis (H3).
However, presence of new products and length of
t
i
m
e
the seller had been active on
eBay
auction
a
r
e
not found to be
significant
pr
e
d
i
c
t
or
s
,
therefore,
r
e
j
e
c
t
i
n
g
the hypotheses
(H5,
and
H6,
respectively).
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TABLE
4: Results of Hypothesis
T
est
i
n
g
:
A
b
ili
t
y
to
A
tt
r
a
c
t
Bidders (M1)
P
r
odu
c
t
s
D
V
Apple
I
pod
(N
= 119)
Olympus
C
a
m
e
r
a
(N
= 120)
Motorola
P
hon
e
(N
= 118)
Dell
L
a
p
t
op
(N
= 134)
B
i
dd
e
r
s
B
i
dd
e
r
s
B
i
dd
e
r
s
B
i
dd
e
r
s
H1
Starting Price
-0.01(-2.51) ***
-0.01(-3.88)***
-0.02(-2.95)***
-0.004 (-1.86 )*
H2
Reserve Price
36.30(1.43) ***
37.72 (1.14) ***
34.11(6.42 )***
26.55(3.47) ***
H3
C
r
e
d
i
t
C
a
rd
-1.71 ( 0
.
94
)
*
-1.27( -1.36)
0.51(0.642 )
26.30 (2.51)***
H4
P
i
c
t
ur
e
2.68 (4.18)
3.39( 4.86) ***
2.59 (3.86) ***
3.89( 3.55) ***
H5
N
e
w
-0.49 ( -0.66)
-0.42 ( -0.50)
0.04 (0.06)
-1.51 ( -1.27)
H6
Seller
A
c
t
i
v
e
-0.00 (0.83 )
-0.00 (-0.09)
0.00 (0.06)
0.0074 ( 0
.
62
)
H7
Store
Fr
on
t
-0.759 ( -0.83)
-0.78 (-0.54)
-1.5 (-2.51)
-1.98 (-2.03)
C
h
i
-
S
qu
a
r
e
-
T
est
28
.
90
[
0
.
000
]
***
65
.
35
[
0
.
000
]
***
41
.
05
[
0
.
000
]
***
21
.
51
[
0
.
0031
]
***
Successful
P
r
e
d
i
c
t
i
on
89
%
77
%
73
%
94
%
-
2
(
LL
)
52
.
95
65
.
03
96
.
23
39
.
09
* p < .01
** p < .05
*** p < .10
Note:
T
a
b
l
e
4 provides coefficients for the variables along with the t-values in the
p
a
r
e
n
t
h
e
si
s.
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1st International Conference on Management and Ecomomics 2012
COMSATS Institute of Information Tecnology, Sahiwal
TABLE
5: Results of Hypothesis
T
est
i
n
g
:
Seller Revenue (M2)
P
r
odu
c
t
s
D
.
V
.
Apple
I
pod
(N
= 119)
Olympus
C
a
m
e
r
a
(N
= 120)
Motorola
P
ho
(N
= 118)
Dell
L
a
p
t
op
(N
= 134)
Seller
R
e
v
e
nu
e
Seller
R
e
v
e
nu
e
Seller
R
e
v
e
nu
e
Seller
R
e
v
e
nu
e
H1
I
n
i
t
i
a
l
Bid
Price
.
90
***
.
97
***
1.05 ***
.
91
***
H2
B
u
y
-
no
w
-
op
t
i
on
20
.
35
*
23.95 **
-.011
73
.
47
***
H3
No of Payment
M
e
t
hod
s
-3.74
-8.36
-1.99
-2.76
H4
A
u
c
t
i
on
D
ur
a
t
i
on
.83
-
.
78
-
.
74
1
.
21
H5
No of Positive
Fe
e
db
a
c
k
.
002
-.002
-.001
.04
H6
No of Negative
Fe
e
db
a
c
k
-
.
54
.
126
.
052
-1.57
H7
No of Pics
-2.03
-5.08 *
2
.
44
**
8
.
55
*
H8
No of Bids
4
.
21
***
5.89 ***
2
.
94
***
8
.
42
***
H9
Shipping
C
o
st
-
.
64
.49
-.70**
-
.
79
F-value
21
.
61
***
103
.
15
***
85
.
94
***
39
.
90
***
Adj-R2
(&)
61
.
3
88
.
5
86
.
8
72
.
5
* p < .01
** p < .05
*** p < .10
Note:
T
a
b
l
e
5: provides
un
sta
nd
a
rd
iz
e
d
coefficients for the variables for Model 2
.
4.2 Results of Multiple Regression (Model 2):
T
a
b
le 5
pro
v
i
d
e
s the results of m
u
lt
ip
l
e
l
in
e
a
r
r
e
g
r
e
ss
i
on
estimates of seller revenue on auction
f
e
a
t
ur
e
s.
T
h
e
study provides
full
support for the two hypotheses (H1, H8) as the
findings
report that across
a
ll
four products,
initial
b
i
d
price and number of
b
i
d
s is uniformly s
ig
n
if
i
c
a
n
t
ly
related to seller
r
e
v
e
nu
e
.
This
means that
h
i
g
h
e
r
initial
b
i
d
and the number of bids, leads to high seller net
r
e
v
e
nu
e
.
I
n
a
dd
i
t
io
n
,
number of
p
i
c
t
ur
e
s has a significant positive impact on seller revenue for two of the
f
our
product types (Motorola
cell
and
dell
laptop).
I
n
t
e
r
e
s
t
in
g
ly
this variable has a
significant
negative
im
p
a
c
t
on the seller revenue for Olympus
C
a
m
e
r
a
,
whereas, in case of
A
pp
le
I
pod
no evidence is found.
T
h
is
finding
partially supports the
h
y
po
t
h
e
s
i
s (H7).
P
ro
c
ee
d
i
ng
s
ICME
2012
244
244
244
1st International Conference on Management and Ecomomics 2012
COMSATS Institute of Information Tecnology, Sahiwal
Shipping
cost has a
significant
negative association with seller revenue for one of the four
produ
c
t
s
(Motorola
cell)
but is not s
ig
n
if
ic
a
n
t
ly
related to seller revenue for any of the other three products.
T
h
i
s
finding
partially supports the
h
y
po
t
h
e
s
i
s (H9).
No support is found for the hypothesis
(H2),
as the results
r
e
v
e
a
l
that
B
u
y-
no
w
-
op
t
io
n
has a s
ig
n
if
i
c
a
n
t
positive impact on seller revenue in three of four product cases
(Dell
L
a
p
t
op
,
A
pp
le
I
pod
and Olympus
C
a
m
e
r
a
)
,
and no impact on the Motorola
cell
a
u
c
t
io
n
s.
However, Number of payment options, auction duration, number of positive feedback and number of
negative feedback (H3, H4, H5, and H6,
r
e
s
p
e
c
t
iv
e
l
y)
are not found to be significant
pr
e
d
i
c
t
or
s.
T
hu
s
,
r
e
j
e
c
t
i
n
g
the
aforementioned h
y
po
t
h
e
s
e
s.
C
on
c
l
u
si
on
This
study presents s
e
v
e
r
a
l
important factors, some that help attract
b
i
dd
e
r
s and others that raise s
e
lle
r
revenue, in the form of two models.
A
f
t
e
r
summarizing these factors the study has been able to
i
d
e
n
t
if
y
the worth of certain factors that could be of
vital
concern for s
e
ll
e
r
s to find success on
eBay.
For
example,
I
n
case of m
od
e
l
1, a lower starting price appeared to be the most important factor in
a
tt
r
a
c
t
i
n
g
the number of bidders for
all
four instances.
Similarly,
including pictures in auctions
f
or
majority of the auctions
(Dell
laptop, Motorola
C
e
ll
and Olympus
C
a
m
e
r
a
)
also found to be
vital
f
or
success for
d
e
t
e
r
m
i
n
in
g
the number of buyers on
eBay.
I
n
case of
M
od
e
l
2,
high
starting price and number of
b
i
d
s turned to be the most
im
por
t
a
n
t
determinants of seller revenue.
T
hu
s
,
eBay
sellers should keep this in m
i
nd
as they search for s
u
cc
e
ss.
Whereas, according to the study, number of payment options, auction duration ,number of
po
sit
iv
e
feedback and number of negative feedback are not
i
m
por
t
a
n
t
features
in
d
e
t
e
r
m
in
i
n
g
s
e
l
le
r
r
e
v
e
nu
e
and importantly auction success.
Similarly,
for m
od
e
l
1, new and the
t
i
m
e
span of seller on
eBay
did
no
t
contribute towards auction success in terms of attracting bidders.
This
has an
i
m
por
t
a
n
t
implication
f
or
sellers to design their auctions, as they need not to be concerned about these auction attributes
w
h
i
c
h
do not seem to matter.
And
certain factors like buy-now-option and reserve price which appeared
t
o
have profound impact on the auction outcome ought to be
c
on
s
i
d
e
r
e
d
by the sellers to increase
t
h
e
ir
r
e
v
e
nu
e
.
Given the results
r
e
g
a
rd
i
n
g
certain factors,
a
dd
it
io
n
a
l
questions are raised as to why some
f
a
c
t
or
s
assumed to
i
m
p
a
c
t
auction success both bidders and s
e
ll
e
r
revenue have no significance, thus,
a
n
examination of insignificant predictors bear further study in other settings,
L
ik
e
w
is
e
,
similar
r
e
s
e
a
r
c
h
could be conducted using products of different nature from online auctions and even with
o
t
h
e
r
internet
a
u
c
t
i
on
ee
r
which
would
d
e
f
i
n
it
e
ly
produce different
im
p
a
c
t
s.
T
h
e
paper concludes that the research has a standing for new
on
l
in
e
bu
s
i
n
e
ss ventures designing similar
business
t
a
c
t
i
c
s.
I
t
establishes and
verifies
certain strengths regarding inclusion of certain features while
c
a
u
t
io
n
i
n
g
about practices having
n
e
g
a
t
i
v
e
and pessimistic
e
ff
e
c
t
.
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COMSATS Institute of Information Tecnology, Sahiwal
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c
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a
y
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h
tt
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//
c
a
m
pu
s.m
urr
a
y
s
t
a
t
e
.
e
du
/
a
c
a
d
e
m
ic
/
f
a
c