International Journal of Electronic Business Management, Vol. 11, No. 4, pp. 247-257 (2013)
247
THE EVOLUTION OF ONLINE MARKETPLACES:
A ROADMAP TO SUCCESS
Nitin Walia
Dauch College of Business and Economics
Ashland University
Ashland, Ohio, USA
ABSTRACT
Online marketplaces have emerged as one of the most prominent markets on the Internet. In
this paper, we examine the evolution of the salient website elements and strategies as
success factors in the online marketplace. The conceptual framework for this examination is
based on the marketing mix theory, allowing us to formulate a success model for sellers
operating in this market. The conceptual model is empirically tested by the random
collection of nearly 2,500 data observations from the UK eBay Motors division for two
time periods: 2006 and 2012. The results bring to light how the online marketplace has
evolved and provide sellers with a roadmap for formulating their success strategies as a
function of market maturity. This study is the one of first to examine the evolution of the
online marketplace over a period of six years and contrasts the success factors as the market
matures.
Keywords: Online Marketplace, UK, eBay Motors, Market Evolution, Selling Strategies,
Website Design
1
1. INTRODUCTION
Online marketplaces have been widely studied,
with an emphasis on the role of trust, psychological
contract violation, feedback mechanisms, emotions,
fraud, consumer behavior, consumer surplus, mobile
shopping, segmentation of online consumers, reserve
price, supply chain, sellers’ perspective, price
premiums, website quality, and B2B transactions [2,
6, 7, 9, 14, 15, 17, 20, 27, 33, 37, 38, 43, 48, 50].
While the existing research has addressed multiple
facets of the online marketplace, we further extend
the literature by examining the evolution of online
marketplaces over a period of six years. This work
expands on following perspectives of the evolution of
the online marketplace: (1) identification of potential
website elements and strategies as success factors in
the online marketplace and (2) more importantly, how
these success factors evolve over time as the market
mature. Hence, the research questions addressed in
this study are: How do website elements and
strategies evolve over time, especially as contributing
factors to success in online marketplaces?
To answer the research question, we conducted
a longitudinal study of online marketplaces for a
six-year time period from 2006 to 2012. The focus of
this work is on eBay’s UK Motors division's auction
*
Corresponding author: nwalia@ashland.edu
listings as an exemplar of online marketplaces. Data
were collected during the summers of 2006 and 2012
for nearly 2,500 auctions for these two time periods.
A comparative analysis between the two data-sets
allows us to have a unique historical perspective of
the evolution of the online marketplace while keeping
possible intervening factors (such as technology
advances, social changes, and global events) constant
for the most part. For the theoretical foundation of
this work, we rely on the marketing mix theory as
well as on the relevant theories utilized in arguing for
specific links in the model conceptualization. This
work tests the theoretical model in the context of the
UK online marketplace. UK is a global leader in
electronic commerce, with the highest online
spending per capita in the world. The results of this
study, which look at the evolution of an online
marketplace from a young to a relatively more mature
market, could provide retailers with a roadmap and
insight as they enter into emerging online
marketplaces around the globe.
2. THEORETICAL
FRAMEWORK AND RESEARCH
MODEL
In an online marketplace, the heterogeneity of
resources available to sellers leads to variability in
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International Journal of Electronic Business Management, Vol. 11, No. 4 (2013)
market success. The heterogeneity of market-based
resources could best be investigated by using the
marketing mix theory. The origin of this model could
be attributed to Neil Bordon, who in 1953 articulated
12 controllable factors for a successful market
operation placement [46]. McCarthy [28] simplified
the factors into four Ps: product, promotion, price,
and placement, which have become widely popular in
marketing education and practice [12]. Multiple
studies have proposed inclusion of people as a
distinctive element of the marketing mix [10, 20].
Hakansson and Waluszewski [18] argue that the Ps in
a market mix are heterogeneous resources that should
be developed and fostered dynamically in order to
meet customer needs. The marketing mix theory
provides us with a powerful framework for
identifying and categorizing website contents that
could be considered critical factors for succeeding in
the online marketplace.
The promotion mix (P1) reflects the ways a
product is brought to the potential customer's
attention. It also has its own characteristics, such as
methods and frequency of promotion. The placement
mix (P2) refers to how and when the product is made
accessible to the customer. The price mix (P3) reflects
the secondary instrument of exchange (where product
is primary), which has its own characteristics, such as
method and deadline of payment, discounting, and
bundling prices. The people mix (P4) involves the
nature of relationships established with the customer.
The decisions in this mix relate to trust and the
emotional aspects involving customer relationship
management. The product mix (P5) identifies the
instrument of exchange and includes product
characteristics. The conceptual model uses these five
categories as the guiding structure to identify the
salient website elements that influence success in the
online marketplace. The model is presented in Figure
1.
Figure 1: The Online Marketplace Success Model
2.1 Measures of Success as Dependent Variables
Traditionally, the success of a seller is
measured by its profit and market share [32], both of
which are dependent on the completion of the trade.
In a few auction studies, sale above the market fair
value is used as a measure of success [4, 42].
However, in many cases, including motor vehicles,
the market fair value depends on the geographical
location of the market. Furthermore, for a participant
who needs cash right away, the utility of a quick sale
may be the most important attribute, more important
than selling above a given market price. In other
words, participants have internal price references and
the sale reflects the fact that the sale price has
surpassed this internal reference. Hence, a more
concrete measure of success in the online marketplace
is completing the sale of the product. This measure is
robust in that it reflects the satisfaction of both parties
in the voluntary trade. Hence, this research considers
completion of sale as a measure of success.
2.2 Web Design (P1: Promotion)
Due to the virtual nature of the online
marketplace, web design plays an important part in
both informing the online marketplace customers
about the product and providing cues about the nature
and abilities of the seller. Websites constitute store
fronts for the online marketplace participants. The
navigation of the site resembles walking in a store
and examining a product. Hence, the design of the
site plays an important function [35]. Furthermore,
retailers draw on signaling as a means to demonstrate
their ability and intentions. Due to the inherent
asymmetry of information in an online environment,
consumers employ observable signals, such as web
design and information, to form opinions about
unobservable features, such as product quality and
security and privacy [28]. Information can be
presented using multiple media, pictures, audio,
graphics, and animation to create a positive image of
a product and brand [13]. Kwon et al. [25] observed a
differentiated relationship between website design
and consumers' intention to bid. On the other hand,
Melnik and Alm [31] argued that images of
homogeneous products do not offer additional
information, whereas pictures and images of
non-homogeneous products, such as coins, could
increase the willingness to bid. Ottaway et al. [34]
suggested that pictures invoke stronger beliefs in
consumers than textual claims. Thus, sellers will be
more successful in sales with a higher richness of
their website.
H1. A higher level of website richness is positively
associated with sale.
2.3 Timing Strategy (P2: Placement)
In the online marketplace, placement and
availability are related to timing strategies, which
include the weekend/weekday auction ending and
length of the auction (bidding interval). The ending
day is a placement strategy for the seller since the
majority of buyers wait until the end of the bidding
interval to place their bids. There is inadequate
Nitin Walia: The Evolution of Online Marketplaces: A Roadmap to Success
research on the effect of the ending day on success of
selling in the online marketplace. According to the
Pew Internet & American Life Project [39], nearly a
quarter of people go online from places other than
home. Furthermore, 41% of individuals access the
Internet at work [52]. There is more opportunity in
the weekday to access the web from work-related
places. While the weekends may be occupied with
home, family, and social life, the accessibility of the
Internet at the workplace makes it easy to place bids
or monitor the bidding process during the course of
the working day. Hence, we postulate that the ending
day of the bidding interval is a salient seller timing
strategy, and ending the bidding interval over the
weekday may produce a more successful outcome.
Hence,
H2a. Ending the bid on a weekday is positively
associated with sale.
Bidding interval or auction length (3, 5, 7, 10,
21 days) is another strategy that sellers can use in the
online marketplace. Pinker et al. [40] argued that a
longer bidding interval increases the number of bids
since it allows more potential buyers to bid. However,
a lengthy bidding interval increases the waiting time
and is costlier for buyers since the item will not be
available during the bidding interval. Furthermore,
the majority of bidding takes place toward the end of
the bidding interval [44, 47]. Bajari and Hortacsu [5]
reported that 50% of bids are placed during the last
10% of the bidding interval time period, and the
winning bid comes even later. Thus, the majority of
bidding takes place toward the end of an auction [44].
Hence, a long bidding interval is not conducive to
increasing the sale.
H2b. A lengthier bidding interval is negatively
associated with sale.
2.4 Pricing Strategy (P3: Price)
Pricing is one of the key seller strategies in the
online marketplace. Buyers are looking for the lowest
possible price, and sellers want to sell at the highest
possible price through their participation in the
bidding process. The bidding process allows the price
offered from both buyer and seller to converge, with
each side relying on their internal reference price
(see, for example, [9] for a review of internal
reference price). Internal reference price, also
sometimes referred to as fair price, acceptable price
range, average price, and the last price paid, is
normally a price at which the transaction would
become acceptable for either buyer or seller. It has
been observed that a low price lowers a buyer’s
internal reference and is perceived by the buyer as a
bargain [9, 23]. Studies have shown that a low
starting price attracts more interest from potential
buyers and further leads to “auction fever” behavior
[19, 24] whereby the bidders get caught up in the
competitive nature of auction and continue bidding
249
above their valuations. Thus, a relatively low starting
price positively influences sale and the final price
received [5, 42] by the seller.
In this study, we employ "Starting Bid/Last Bid
(%)" (SB/LB) as the manifestation of the seller’s
pricing strategy. It is computed as the starting
minimum bid set by seller as the percentage of last
bid (or sale price) received on that auction. A large
SB/LB indicates that the seller has set the starting
price too close to the seller's internal reserve price,
thus setting the starting bid at a relatively high price,
which leads to a lower chance of sale. Hence, we
posit
H3. Higher Starting Bid/Last Bid (%) is negatively
associated with sale.
2.5 Seller (P4: People)2
Trust is one of the most crucial elements in an
online transaction [14, 37, 47]. In the online
marketplace, we identify two source of trust: (1) the
seller's reputation profile based on the eBay feedback
mechanism and (2) the age of the seller, i.e., time
registered on eBay. These sources induce trust in
potential customers that a seller will complete the
transaction as promised [45]. In the online
marketplace, feedback is a public evaluation of a
seller and establishes the seller's reputation within the
community. Ba and Pavlou [4] considered eBay’s
feedback forum as a reputation system whereby
buyers evaluate sellers based on their experience with
a seller. Feedback mechanism is a key element of
online trust and is widely used in online commerce.
Pavlou and Dimoka [36] showed that buyers’ text
comments have a strong influence in sellers’ trust
building.
H4a. The higher level of seller reputation is positively
associated with sale.
Sellers that have been in operation for a long
time assure potential buyers that they will not
disappear after the transaction is over. The sellers in
the online marketplace are individuals and entities
that could exit the market once a transaction is over.
This lack of perceived permanence could contribute
to the reluctance of buyers to conduct a transaction
with a potential seller. Thus, the age of seller (i.e.,
time registered on eBay) plays an important part in
countering this perception; sellers who have been
present in the online marketplace for a longer period
of time could enhance buyers' trust.
H4b. Seller’s age is positively associated with sale.
Case (1) : Car A Starting Price/Bid = $2000, Last or
Winning Bid = $8000, SB/LB = 25%
Case (2) : Car B Starting Price/Bid = $500, Last or
Winning Bid = $1000, SB/LB = 50%
As the examples above show, SB/LB is a superior
measure of the seller's pricing strategy compared to
utilizing starting price alone.
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Seller type (business or non-business) could
play an important part in the online marketplace.
Business Sellers include dealers, top-rated sellers,
power sellers, or any other seller registered as
"business" with eBay. Examples,
.
Business sellers might have an existing brand name
and, thus, might not need to rely on online feedback
mechanism to establish their reputation [8]. A
business seller by its mere presence is a strong signal
of the seller's legitimacy and ability to deliver on a
promised transaction [49].
H4c. Business Seller as ‘seller type’ is positively
associated with sale.
2.6 Product Characteristics (P5: Product) (Control
Variables)
Product is considered “the basic source
involved in the exchange process” [18] that includes
not only the salient aspects of the product (or service)
in the marketplace but also additional services that
come with the product, such as warranties and
post-sale services. In the context of cars, the salient
product characteristics include condition (used or
new) and warranty. Product characteristics are
controlled for this study.
2.7 Number of Bids (Control Variable)
A high number of bids indicates an explicit
intention to buy and, hence, could be considered as a
pre-cursor of sale [16]. The public nature of the
online marketplace allows potential buyers to see
existing bids, which may impact their intentions, in
other words, “herd behavior bias”. The relationship
between bids and sale has been well established in
literature [5, 16]. Thus, number of bids is a control
variable.
Variables
Dependent Variable
Sale
Promotion (Website Richness)
Pictures
Placement (Timing Strategies)
Weekday ending (Mon-Thur)
Bidding interval
Price (Pricing Strategy)
Starting Bid (£)
Last Bid(£)
Starting Bid/Last Bid (%)
People (Seller Trust)
Feedback rating (seller reputation)
Seller age
Seller type
Number of Bids
Product characteristics
Care age
Engine size
3. RESEARCH METHODOLOGY
Data were collected from eBay Motors UK. We
chose eBay Motors UK since it has multiple types of
participants, the products and prices are
heterogeneous, and sellers use a variety of
presentation styles and tools to promote their
products. For the period-I, data were collected from
April 2006 to June 2006, and for period-II, from May
2012 to June 2012. To collect data randomly and
efficiently, a "spider" was written in Visual Basic.
The spider proceeded as follows. It first gathered the
unique ID of auctions about to end within 10 days.
Unique IDs were collected on different days of the
week and month and during different times of the
day. After the closing time of the auction, the spider
collected the websites’ HTML documents for each
completed auction of the corresponding unique ID,
with or without successful sale. The HTML document
for each auction was then processed through another
VB program (“Cleaner”) to condense the information
about each auction into a single row of data in a
spreadsheet. The program was re-written for 2012
data collection because of changes in eBay website
display format. After the elimination of BuyNow
cases and corrupted listings, the usable website data
set contained 1,255 observations for the 2006 time
period and 1,140 observations for the 2012 time
period. Henceforth in this work, the term UK-I is
used to denote the 2006 time period and UK-II to
denote the 2012 time period for the comparisons.
Variable descriptions and their measurements are
reported in Table 1. Appendix A reports the
descriptive statistics for the data.
Table 1: Variable definitions
Items
Type*
Product was sold or not
B
Number of product and non-product pictures
C
Whether the listing ended on a weekday
Length of auction in days (3, 5, 7, 10, 21)
B
D
Starting bid on the listing
Last or winning bid
Computed (SB/LB)
C
C
The feedback score is the sum of ratings from unique users
Number of months registered on eBay
Business or non-business seller
Number of bids received on a single listing
C
C
B
C
Age of car (in years)
Type of engine (in cc)
C
C
*B=Binary, C=Continuous, D = Discrete values
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Nitin Walia: The Evolution of Online Marketplaces: A Roadmap to Success
3.1 Comparative Analysis of the UK-I and UK-II
Market
To compare the differences, a multiple group
analysis in Mplus was conducted for comparing
differences in coefficients of the corresponding paths
of the two models. A multiple group analysis is a
structural equation modeling that allows the
comparison of structural model differences across
groups (in this study, the UK-I and UK-II time
periods) [41, 10]. When examining differences across
two groups, researchers have suggested comparing
the model’s explained variance (r-square) and the
associated path-coefficients [1]. A comparison of
results (Table 2) suggests that differences exist across
the two groups. In terms of the structural model, a
comparison of the path coefficient suggests weekday
ending, bidding interval, price, reputation, seller type,
and control variables had different influences on the
sale outcome across the two groups. The comparison
of estimated models in the UK-I and UK-II markets
indicates structural changes in the online marketplace
as it matured. R2 values for the dependent variable
(sale) in Model U.K-I was 0.25 (p<0.001), and in
Model UK-II was.37, with p<0.001. Compared to the
UK-I model, the UK-II structural model predicted
12% more variation in sale: ▲R2 Relative to UK-I
=.12.
Table 2: Group analysis of the model
Sale
UK-I
(N=1255)
UK-II
(N=1140)
Promotion(Website Richness)
Number of pictures
.09***
.08**
Placement (Timing Strategies)
Weekday ending
Bidding interval
.06*
.03
.01
-.07*
Price (Pricing Strategy)
SL/LB
-.09*
-.32***
People (Seller Trust)
Seller reputation
Seller age
Seller type
.06**
-.03
.05*
.01
.03
.01
Number of Bids (control variable)
.30***
.32***
Product characteristics
Car age (control variable)
Engine size (control variable)
.12***
-.11***
.07*
.03
.25***
.37***
Variables
R2
In H1, it was hypothesized that website media
richness has a positive association with sale. The
website richness had a significant impact on sale for
both markets (β =0.09, p<0.001 and β =0.08, p<0.01).
Therefore, we may conclude that as the online
marketplace matures, website richness continues to
impact the chance of sale. In H2 (a), it was
hypothesized that ending the auction on a weekday
has a positive influence on sale. We have strong
support for the positive influence of weekday ending
on sale for the UK-I market (β =0.06, p<0.05). We
found no evidence of such influence in the case of the
UK-II market. The result raises the possibility that as
the market matures, weekday ending may not play a
strategic role in closing a sale. As to the second
timing strategy, we hypothesized that bidding interval
(length of auction) has a negative influence on sale
(H2 (b)). The negative influence of bidding interval is
observable in case of the UK-II market (β =-0.07,
p<0.05). In H3, it was hypothesized that the pricing
strategy in the form of a high SB/LB has a negative
impact on sale. The hypothesis was supported in both
the UK-I and UK-II market (β =-0.09, p<0.05 and β
=-0.32, p<0.001. Thus, H3 was strongly supported.
The path coefficients and significance level further
indicated that this relationship was more negative in
case of the more mature market -- UK-II. We
hypothesized that providing seller trust elements
(reputation and age) on the website increases the
chances of sale (H4a and H4b). Seller reputation had
significant influence on sale in the UK-I market (β
=0.06, p<0.01), indicating the reliance of the
customers on these cues, whereas this cue did not
have any influence on sale in the UK-II market. Seller
age had no significant impact on sale in either market.
We also hypothesized that seller type is positively
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International Journal of Electronic Business Management, Vol. 11, No. 4 (2013)
associated with sale (H4c). Seller type had significant
impact on sale for the UK-I market (β =0.05, p<0.01),
indicating that business sellers are more successful,
but there was no such impact in the UK-II market.
4. DISCUSSION
The results of our analysis have uncovered a
complex and evolving structure for eBay as a
prominent online marketplace. The results of
descriptive analysis and structural equation modeling
indicate that online marketplace sellers deploy
distinctive approaches in their web design elements
and market strategies, and the impact of these
strategies on sale varies as the market matures. We
compared website contents and business strategies of
sellers in the UK market for two periods: 2006 and
2012. The overall results provide support for the
market mix model, indicating that the factors play an
important role in the website design and strategies of
eBay online marketplace sellers and influence their
success in both the younger and mature market.
4.1 The Relationship between Media Richness and
Sale
The higher utilization media richness in the
UK-II indicates that as the market matures, its
participants provide richer website contents. The
comparison of estimated models for sellers in the
UK-I market and the UK-II market indicates that the
impact of media richness is equally important in a
younger and mature market and to gain a competitive
advantage, sellers must continue provide media
richness on their sites. Thus, the results are in
conformance with previous literature related to
website design [13, 34, 28, 50].
4.2 The Relationship between Placement and Sale
Ending the auction on a weekday as a timing
strategy works only in case of a younger market but
not in a mature market. The results show that as the
market matures, the customers prefer shorter bidding
intervals. The result from both timing strategies raises
the possibility that the customers in a mature market
are much more sophisticated and familiar with the
online purchase process and consider longer bidding
intervals costlier and wasteful. Furthermore, the
results also show that as the market matures, the
customers are equally comfortable with participating
in online marketplace on either weekday or weekend,
thus negating the impact of weekend/weekday as a
strategy to influence sale.
4.3 The Relationship between Pricing and Sale
The SB/LB was an important strategy for
sellers. Sellers should be careful in setting relatively
high starting prices, as this lowers the chance of a
successful sale. The results indicate that as the market
matures, the negative impact of relatively high SB/LB
is even more pertinent on sale. The results show that
in a mature market setting, a high starting price leads
to a lower likelihood of sale. These results are
different from a study conducted by Hou [19].
Interestingly the data collection for Hou [19] was
done during the year 2006, classified as time
period –I in this this study (year 2006). Thus, the
results could be attributed as the traits of a younger
market.
4.4 The Relationship between People and Sale
Naturally, the average sellers’ market age and
number of ratings received increases as the market
matures. The comparison of estimated models for
sellers in the UK-I market and the UK-II market
indicate that seller reputation is positively associated
with sale in the case of the younger market but not in
case of the more mature market. It seems that as the
online market matures and its participants increase in
number, the online seller's reputation loses its
significance as trust cues for enhancing the seller’s
success. One reason for this phenomenon could be
that in a more mature market, buyers have developed
trust in the structure of the online marketplace (eBay,
in this case) and placed less emphasis on the
individual ratings of the sellers. This result is in
conformance with the literature on online trust [37].
Another reason for this phenomenon could be that the
value placed on seller’s expertise (which impacts
trust in seller) is much higher in a thin market [14]
compared to a mature market which has a high rate of
participation by business sellers. As the participation
rate of business sellers rises, there might be a
reinforcement process between a seller’s online and
offline reputations. Such a phenomenon has been
observed in the case of established banks entering the
online banking market [26].
The low percentage of business sellers in our
random sample of the UK-I market indicates that the
entrance of business sellers takes place at a later stage
of the online marketplace evolution, when its
maturity and number of participants attract business
participation. Furthermore, the higher percentage of
sale combined with the low number of average bids
during the UK-II market shows that the conversion
rate of bids to sale occurs at a higher rate at a later
stage of the market maturity.
4.5 Managerial Implications
One important insight from this study is that it
provides a roadmap to sellers to gain a competitive
advantage in online marketplace. Depending on the
maturity level of online marketplace, business
managers could focus on the critical success factors
in online marketplaces. Sellers operating in emerging
market should focus on media richness, weekday
ending, setting low starting prices and seller
Nitin Walia: The Evolution of Online Marketplaces: A Roadmap to Success
reputation, while sellers operating in relatively
mature online marketplaces should focus on media
richness, shorter bidding intervals and should be
careful about setting high starting prices, as this
lowers the chance of a successful sale. This study not
only provides a comprehensive understanding of the
role of five Ps: product, promotion, price, placement,
and people, it enables sellers to compartmentalize the
impact of each Ps as the function of marketplace
maturity.
In sum, our results show that as the market
matures the diversity of sellers (non-business and
business) in the marketplace increases. This diversity
is in conjunction with the maturity of the market
structure reducing the reliance of customers on only
online trust mechanisms. While in a young market,
sellers may be too focused on getting large number of
bids as a precursor to sale, but as the market matures,
the focus moves from attracting customer (number of
bids) to converting bids to sale. Furthermore, in the
more mature market, customers’ expectations of a
seller's website richness and contents increase.
5. CONCLUSION
This study examined the evolution of salient
website elements and success strategies in the online
marketplace. It is predicted that the economic and
social impact of the online marketplace will continue
to grow. We used UK eBay as the prominent example
of the online marketplace. We provided a comparative
analysis of a younger and a more mature online
marketplace: UK-I (year: 2006) and UK-II (year:
2012). To test our model, we randomly collected data
for the transactions from the motor division of eBay
in the UK during the summers of 2006 and 2012. The
comparison between the two markets provides an
insight into how the online marketplace evolves over
time. The comparison of descriptive statistics in the
two markets revealed differences in sellers’ salient
web elements and strategies for participants in the
two markets and provided insights into the evolution
of the online marketplace over time. The estimations
of the conceptual model for sellers in the UK-I and
UK-II markets showed how website contents, pricing,
and timing strategies impact the success of
participants and the change of the market structure
over time. Business sellers are late entrants in the
online market; as the market matures, sellers are
expected to be more sophisticated in their website
design and to rely less on the market-trust cues to
increase the likelihood of sale.
This study has limitations that could serve as
areas for further extensions. Our work could be
repeated for other types of products and services.
Furthermore, our results are based on data obtained
from eBay as the prominent example of the online
marketplace. Our work could be repeated for other
253
prominent online marketplaces, such as markets
created by Amazon.com and Yahoo.com.
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255
ABOUT THE AUTHOR
Nitin Walia is an Assistant Professor in the
Department of Accounting/Information Systems at
Ashland University, Oh - USA. He received his PhD
degree in Information Systems from the University of
Wisconsin-Milwaukee in 2010. He also holds M.S.
degrees from Oakland University, MI and Pune
University, India. His research interests are healthcare
delivery systems, virtual worlds, online marketplaces,
web-design interface issues, and open source
software adoption and development. He is also
working on issues related to providing medical
services through a virtual world. He has received a
number of research grants and award for teaching.
His work has been published (or forthcoming) in
IEEE Transactions of Engineering Management and
presented in several national and international
conferences.
(Received February 2013, revised February 2013,
accepted August 2013)
256
International Journal of Electronic Business Management, Vol. 11, No. 4 (2013)
APPENDIX – A
Variables
Observations
Dependent Variable
Sale
Promotion (Website Richness)
Pictures
Placement (Timing Strategies)
Weekday ending (Mon-Thur)
Bidding interval
Price (Pricing Strategy)
Starting Bid (£)
Last Bid amount (£)
People (Seller Trust)
Positive feedback (seller reputation)
Seller age (months)
Seller type (business seller)
Number of bids (control variable)
Product characteristics
Car age (years) (control variable)
Engine size (cc) (control variable)
1. Sale
2. Bids (control)
3. Pictures
4. Weekday
5. Bid Interval
6. Starting Price
7. Last Price
8. Market-mediated
9. Seller reputation
10. Seller Age
11. Seller type
12. Car age(control)
13. Engine Size (control)
1
1
.47
.10
.11
-.04
-.20
-.17
.05
.12
-.02
.14
.09
-.11
Table A1: Descriptive statistics
UK-I
Mean
Stdev
or %
1,255
UK-II
Mean
or %
1,140
36%
44%
5.3
4.7
6.9
3.7
82%
6.2
2.7
85%
6.4
2.4
6,363
8,711
12,543
13,221
1,238
1,935
2,714
2,881
138
24
13%
9.9
310.77
15.3
1293.9
390.1
10.4
407.6
140
28%
7.5
7.8
2,322
5.4
1,025
11
1,935.6
1.13
634
Table A2: Correlations matrix (n=2395)
2
3
4
5
6
7
1
.20
.05
-.01
-.22
-.05
.09
.13
-.12
.18
-.06
-.01
1
-.01
.07
-.06
-.02
.01
-.04
.05
.12
-.07
.05
Stdev
8
9
10
9.7
11
12
13
1
-.04
1
-.17 .16
1
-.20 .18 .92
1
-.02 .06 .03 .06
1
.06 -.04 -.06 -.04 -.05
1
-.02 .12 -.10 -.14 -.08 .01
1
.11 -.03 -.10 -.06 -.08 .29 .07
1
.16 -.09 -.33 -.40 .04 -.03 .05 -.09
1
-.16 .14 .40 .46 .02 .01 -.07 -.04 -.06
1
Nitin Walia: The Evolution of Online Marketplaces: A Roadmap to Success
APPENDIX – B
Table B1: Comparison of UK-I and UK-II
UK‐I : Seller Feedback Score
UK‐II : Seller Feedback Score
257