The impact of product representation online - results of an empirical analysis of image auctioning of flowers.

1. ARTICLE IN PRESS
Decision Support Systems xx (2003) xxx – xxx
www.elsevier.com/locate/dsw
The importance of product representation online: empirical results
and implications for electronic markets
Otto R. Koppius a,*, Eric van Heck a, Matthijs J.J. Wolters b
a
Department of Decision and Information Sciences (F1-31), Rotterdam School of Management, Erasmus University Rotterdam, P.O. Box 1738,
3000 DR, Rotterdam, The Netherlands
b
Free University Amsterdam, Amsterdam, The Netherlands
Received 1 May 2001; accepted 1 September 2002
Abstract
We investigate the effects of online product representation at a large Dutch flower auction that implemented screen
auctioning. In screen auctioning, flowers are not physically shown to the buyers anymore; instead, an image is presented on a
screen to buyers in the auction hall. This online product representation entailed a decrease in information about flower quality
compared to the physical product representation. Analysis of the transaction data before and after screen auctioning revealed
lower prices after the introduction of screen auctioning. We conclude that deficient product representation may be a partial
explanation for reduced prices in electronic markets.
D 2003 Elsevier B.V. All rights reserved.
Keywords: Electronic commerce; Electronic markets; Auctions; Product representation; Flower auction
1. Introduction auction is the matching of demand and supply at the
‘best price’ at one specific point in time. The advan-
The rapid developments in information technology tages, however, must be weighed up against lower
and its applications in business have resulted in switching costs for auction participants. Are auctions
electronic markets being increasingly popular. These always beneficial to the companies involved? For
markets can result in significant savings for both instance, what is the impact of electronic auctions
sellers and buyers. Savings are made by reducing on the prices of traded goods? A lot of attention has
transaction costs, increasing the circle of potential been paid to potential benefits for participants in
customers as well as by improving the search-and- electronic markets, but not much is known about the
find capabilities for all parties concerned [12]. Elec- actual consequences of the introduction of electronic
tronic auctioning in particular is a rapidly expanding markets for the various participants. These consequen-
application [7]. The additional benefit of an electronic ces are to some extent dependent on the design of the
electronic market and one of these design factors is
the product representation online. Possible represen-
* Corresponding author. Tel.: +31-10-408-2032; fax: +31-10-
tation methods vary from listing the product character-
408-9010. istics to providing a picture to a full audio/video
E-mail address: O.Koppius@fbk.eur.nl (O.R. Koppius). presentation of the product in question or combina-
0167-9236/03/$ - see front matter D 2003 Elsevier B.V. All rights reserved.
doi:10.1016/S0167-9236(03)00097-6
DECSUP-01095

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2 O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx
tions of these. The central research question here is marketplaces could reduce the search costs that buyers
how a different product representation online impacts must incur to acquire information about seller prices
the prices paid for those products. and product offerings. The lowered search costs allow
In this article we present the first part of a large buyers to look at more product offerings and make it
research project dealing with empirical research on difficult for sellers to sustain high prices. Bakos
electronic auctions in the Dutch flower industry. We therefore formulated a reduced price hypothesis: pri-
investigate a new method of auctioning at an anony- ces would be lower in electronic markets. Empirical
mous Dutch flower auction called screen auctioning, research related to electronic markets, however,
where flowers are not physically shown to the buyers showed mixed results. For example, in [2], satellite
anymore, but instead an image is presented on a video cattle auctions were compared with regional
screen to the buyers in the auction hall. Note that market prices. Prices for both the regional and video
the screen auction itself is not an electronic market, auctions were adjusted for quality differences, trans-
because buyers still physically assemble in the auction portation costs, commissions, and days to delivery.
hall. However, it can be considered an intermediate Net prices paid by buyers and received by sellers in
step towards an electronic market, because in an video auctions exceeded the prices for the three major
electronic market the products are not physically regional auction markets. In [8], it was showed that
presented either. Moving to a completely electronic the prices of secondhand cars traded through an
market could entail many more changes such as electronic market place (Aucnet) could actually be
buyers not physically meeting anymore or lower higher than those products sold in traditional markets.
search costs that could have varying effects on the Potential explanations are that Aucnet focused on
different stakeholders in the market. The investigation relatively newer secondhand cars and also buyers
of screen auctioning provides an isolation of the are willing to pay the premium (i.e. a slightly higher
product representation effects from these other effects. price) because they not only avoid a large waste of
The remainder of this article is organized as fol- time spent on attending physical auctions but also
lows: Section 2 summarizes previous research on easily locate a vehicle that best matches their prefer-
electronic markets and Section 3 explains the charac- ences. In both these studies, multiple factors could
teristics of the Dutch flower industry and its auctions. have accounted for the price differences, making
In Section 4, we analyze screen auctioning from a unambiguous interpretation of the results difficult. In
process – stakeholder perspective. In Section 5, the this study however, we are able to isolate the effects of
model is presented and validated using transaction one of these factors, namely the effects of online
data obtained at an anonymous Dutch flower auction. product representation.
Section 6 discusses the results and Section 7 ends with
conclusions and implications for research and practice.
3. The flower industry and auctions
2. Previous research on electronic markets The Netherlands is the world’s leading producer
and distributor of cut flowers. The Dutch dominated
Prior research on the effects of Information and the world export market for cut flowers in 1996 with
Communication Technology (ICT) on exchange a 59% share and for potted plants with a 48% share.
organizations and processes typically applied transac- The world’s two biggest flower auctions are in Aals-
tion cost theory and agency theory to predict shifts meer (Flower Auction Aalsmeer) and Naaldwijk/
from hierarchies towards market or other intermediate Bleiswijk (Flora Holland); every day on average 30
forms of organization [4,5,9]. A central argument of million flowers—originating not only from the Neth-
these articles was that ICT would improve communi- erlands but also from countries such as Israel, Kenya
cation searches, monitoring and information-sorting and Zimbabwe—are traded in 100,000 transactions.
capabilities, to reduce transaction costs and allow In total there are seven Dutch flower auctions,
purchasers to take advantage of production economics namely in the villages of Aalsmeer, Naaldwijk/Bleis-
available in markets. Bakos [3] argued that electronic wijk, Rijnsburg, Grubbenvorst, Eelde, Bemmel, and

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O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx 3
Vleuten. The Dutch flower auctions play a vital role price determined by the auctioneer, and drops until a
in Holland’s leadership of this industry, by providing buyer stops the clock by pushing a button. The
efficient centers for price discovery and transactions auctioneer asks the buyer by intercom how many
of flowers between buyers and sellers. These auctions units of the lot he or she will buy. The buyer provides
traditionally use the ‘Dutch auction’ as the mecha- the number of units. The clock is then reset, and the
nism for price discovery. They are established as process begins for the remaining flowers, sometimes
cooperatives by the Dutch growers. introducing a new minimum purchase quantity, until
We will describe the auction rules of the Dutch all units of the lot are sold. Table 1 illustrates the
flower auction concept using some empirical data to auction process by an example with some actual
illustrate its characteristics and results [11]. There are auction data. The first rows deal with producer 1234
approximately 3500 varieties of cut flowers. These (column 2), who is responsible for transactions 408 to
varieties are classified into 120 auction groups, 420 (column 1). On January 4, 1996 this producer
according to the variety, size of the lot, and quality delivered roses (product group 52), or more specifi-
of the flowers. Dutch flower auctions use a clock for cally the brown rose ‘Leonidas’ (product number
price discovery as follows. The computerized auction 10288). He delivered four lots of that type of rose
clock in the room provides the buyers with product (column 4). These lots had the same type of quality
characteristics such as stemlength or diameter or (A1), but were different in length (70, 60, 50, and 80
number of leaves (dependent on the particular flower cm, respectively) and in amounts of 9, 5, 3, and 12
type), as well as information on the producer, unit of units, respectively. The first lot was auctioned, and
currency, quality and minimum purchase quantity. buyer 3782 took 1 unit (out of 9) for a price of 93
The flowers are transported through the front of the cents per stem. The rest of the lot was auctioned again,
auction room, where there is a person (the ‘raiser’) and buyer 1854 bought 2 units for 95 cents. The
who shows the flower to the more than hundred remainder of the lot (6 units) was auctioned, and
buyers in the stand. The clock hand starts at a high buyer 727 bought 3 units for 96 cents. Finally, buyer
Table 1
Auction data illustrating the Dutch flower auction concept (source: [11])
Transaction Producer Product Product Quality Length Total no. Stems Buyer No. of Price in
no. group in cm of units per unit units cts./stem
408 1234 52 10288 A1 70 9 100 3782 1 93
409 1854 2 95
410 727 3 96
411 42 3 97
412 1234 52 10288 A1 60 5 100 727 4 89
413 1824 1 91
414 1234 52 10288 A1 50 3 100 3090 1 67
415 2528 2 68
416 1234 52 10288 A1 80 12 100 3282 4 109
417 4157 1 115
418 134 3 115
419 3462 2 116
420 3042 2 117
727 12 52 11087 A1 80 3 100 2893 2 91
728 752 1 87
729 12 52 11087 A1 70 6 100 727 1 79
730 1768 2 77
731 3004 3 77
732 12 52 11087 A1 60 8 100 3219 1 56
733 2669 3 56
734 727 4 54
735 12 52 11087 A1 50 3 100 727 3 46

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42 bought the rest of the lot for 97 cents. The table hall. In February 1998, screen auctioning was expand-
shows that the price may increase during the auction- ed to cover the flower type Gerbera as well. The
ing of a lot (see, for example, transactions 408 research in this article deals with Anthuriums only.
through 411) or may decrease within a lot (see, for
example, transactions 729 through 731). So the price
is very volatile, considering different lots of the same 4. Screen auctioning: process– stakeholder analysis
producer or different lots of different producers.
Buyers must be physically present in the auction Previous research dealing with reengineering the
room. In practice, it turns out that the Dutch flower Dutch flower auctions is published in [6,10,11].
auction is an extremely time-efficient auction mecha- Kambil and van Heck [6] specify a generalizable
nism: it can handle a transaction every 4 seconds. It model of exchange processes and develop a pro-
also reduces the amount of time that growers must cess –stakeholder analysis framework to evaluate al-
spend on price discovery and bidding; hence they can ternative market designs. They identify five basic
focus on production. The auction provides a central trade processes: search, valuation, logistics, payments
location for the meeting of buyers, creating efficient and settlements, and authentication. The basic trade
quality control and logistics of product redistribution. processes are distinct processes required in all trans-
This auction has ‘‘backtracking’’ possibilities: al- actions of goods and services. The trade context
though the price movements are decreasing per sub- processes facilitate and enable or reduce the costs or
lot, the price can be multidirectional (up or down) ‘‘frictions’’ in the basic processes. The five trade
within the whole lot. Buyers can withdraw their context processes are communications and comput-
willingness to buy: they can indicate to the auctioneer ing, product representation, legitimization, influence,
fewer or more units then they originally intended to at and dispute resolution.
the time they pushed the button. During the auction- Table 2 presents the results of the analysis of
ing of the lot, buyers produce information on the value screen auctioning with the help of the process – stake-
of the lot; this information is available to all buyers. holder framework. For each of the stakeholders, the
Given these characteristics, we call the Dutch flower expectations related to screen auctioning are described
auction a multi-unit, discriminatory auction. and as can be seen, sellers, intermediary and buyers
As mentioned previously, all flowers that are put differed in their expectations of the effects of screen
up for auction pass through the auction hall in order to auctioning. Table 2 also describes the changes for
be shown to the buyers just before the bidding starts. each of the processes compared with the traditional
Given the large daily turnover, this process entails Dutch flower auction system.
tremendous logistical difficulties for the auction. To
alleviate these, the flower auction introduced screen
auctioning for the flower type Anthurium in February 5. Screen auctioning: multiple regression model
1996. In screen auctioning, the buyers are still present
in the auction hall, but they are no longer shown the To quantitatively investigate the impact of screen
flowers as in the traditional auction method. Instead, auctioning, the auction transaction database from
the flowers remain in the warehouse and buyers are January 1995 until December 1997 was available
shown a picture for that type of flower. This is a (screen auctioning was introduced on February 13th,
generic picture, irrespective of lot differences within 1996). In this database, for every transaction various
the same type, but they still see the specific product data are kept, including data related to the seller, the
characteristics of that particular lot below the auction buyer, the product (quality, stemlength and diameter,
clock. When screen auctioning was introduced, two etc.), and the transaction itself (price, quantity, date).
other aspects of the trading process changed as well. We constructed the following model for explaining
The time of auctioning Anthuriums was rescheduled the price of an Anthurium at the auction:
to an earlier time (6 am) and screen auctioning was
introduced as a third clock in one of the auction halls, PRICE ¼ a þ b1 DIAM þ b2 WKDAY þ b3 VBN
so now three concurrent auctions take place in that þ b4 QUANT þ b5 SCRAUC þ e: ð1Þ

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Table 2
Process – stakeholder analysis of screen auctioning
Exchange process Growers Auction Buyers
Search No change No change No change
Valuation Expected: possible higher prices Expected: no expectation with Expected: no expectation with
because of earlier auctioning time regard to impact on prices regard to impact on prices
Logistics Expected: more trading capacity Expected: auction hall logistics Expected: faster delivery of
at auction hall would be eliminated allowing flowers due to by-passing
for cheaper and more frequent auction hall
transactions and new space
for clocks
Payments and No change No change No change
settlements
Authentication No change No change No change
Communication No change Major change: digital representation No change
and computing of product with standard image
next to clock
Product No change Expected: Generic digital representation Expected: Generic digital
representation of each lot would represent the actual representation of each lot
flower accurately enough could lead to less information
on quality of flowers
Legitimization No change No change No change
Influence No change No change No change
Dispute resolution No change No change No change
Net benefits Positive Positive Neutral
There are several factors that influence the Anthur- actions after February 13, 1996. This is the key
ium price that we use as control variables in our explanatory variable.
model. For Anthuriums, diameter of the flower Under screen auctioning, buyers lack two product
(DIAM) is an important descriptive characteristic. characteristics compared to the physical representa-
The day of the week (WKDAY) influences price as tion: the color and the stiffness of the flower, which
well because different days of the week have struc- could be judged when the ‘raiser’ would show the
turally different supply and demand characteristics. flower to the bidders. Particularly, the absence of the
Similarly, the trade of Anthuriums (and flowers in stiffness cue is problematic, because stiffness is an
general) is highly seasonally dependent. Therefore, important indicator of flower freshness, which in turn
we corrected for this seasonal effect in the regression is an important determinant of a buyer’s willingness-
by including the average Anthurium price at all other to-pay for that flower. This lack of freshness informa-
flower auctions in Holland (VBN) as an extra vari- tion will lead buyers to expect a lower quality on
able. Taking the average Anthurium price at the other
flower auctions into account also corrects for any
market-level phenomena that may influence the over- Table 3
all Anthurium price, as these should occur at the other Descriptive statistics
auctions as well as the auction investigated here. The N Minimum Maximum Mean Standard
quantity of the transaction (QUANT) is taken into deviation
account because bidders are expected to bid different- VBN 154,074 80.40 239.90 145.50 43.38
ly for large or small quantities. We did not have any DIAM 152,583 1 99 11.51 2.85
QUANT 154,074 5 2300 76.70 2.83
prior expectations regarding the effects of these con- LOGQUANT 154,074 1.61 7.74 4.34 1.04
trol variables. The effect of screen auctioning in the PRICE 154,074 30 495 111.05 1.83
model is a dummy variable SCRAUC: 0 (without LOGPRICE 154,074 3.40 6.20 4.71 0.607
screen auctioning) for transactions before February Valid N 152,583
13, 1996; or 1 (with screen auctioning) for trans- (listwise)

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Table 4
Cross-correlations
DIAM SCRAUC VBN MON TUE WED THU FRI LOGQUANT
DIAM 1.000
.
SCRAUC 0.008 1.000
(0.002) .
VBN 0.038 À 0.004 1.000
(0.000) (0.080) .
MON À 0.010 0.007 0.020 1.000
(0.000) (0.003) (0.000) .
TUE À 0.002 0.013 À 0.015 À 0.327 1.000
(0.496) (0.000) (0.000) (0.000) .
WED 0.018 À 0.021 À 0.002 À 0.299 À 0.282 1.000
(0.000) (0.000) (0.424) (0.000) (0.000) .
THU À 0.007 0.002 À 0.010 À 0.181 À 0.171 À 0.156 1.000
(0.004) (0.385) (0.000) (0.000) (0.000) (0.000) .
FRI 0.000 À 0.003 0.004 À 0.309 À 0.291 À 0.266 À 0.161 1.000
(0.967) (0.302) (0.137) (0.000) (0.000) (0.000) (0.000) .
LOGQUANT À 0.041 À 0.014 À 0.071 0.023 0.009 À 0.025 À 0.019 0.005 1.000
(0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.058) .
average for fear of purchasing a ‘lemon’ [1]. We normality assumptions. Descriptive statistics of the
therefore have the following hypothesis: dependent and independent variables are given in
Table 3 (dummy variables excluded). Cross-correla-
Hypothesis 1. b5 < 0, i.e. screen auctioning will lead
tion can be found in Table 4.
to lower prices.
The parameters of the model are estimated with
We analyzed the most traded type of Anthurium— multiple regression analysis, using ordinary least
the Anthurium Tropical. Approximately 98% of all squares (OLS). The model was tested for homosce-
Anthurium Tropical flowers traded were of the highest dasticity and linearity by examining the residual plots.
quality (quality grade A1), so we focused only on this The plots did not indicate any heteroscedasticity or
quality grade and removed quality grades A2, B1 and non-linearity. Furthermore, the tolerance limits indi-
B2 from the analysis. These operations resulted in a cate that there is hardly any collinearity present. Only
remaining database of 154.074 transactions. the days of the week show some collinearity, which is
We needed to take the natural log of the price and to be expected given the fact that one of them is
the quantity. Both variables were very skewed to the redundant. The results are presented in Table 5, all the
right; taking natural logs restores the validity of the results are significant at the 1% confidence level,
Table 5
Regression results
Unstandardized Standard Standardized t Sig. Tolerance
coefficients B error coefficients b
(Constant) 2.678 0.007 363.986 0.000
DIAM 0.0767 0.000 0.360 217.428 0.000 0.997
SCRAUC À 0.0216 0.002 À 0.017 À 10.358 0.000 0.999
VBN 9.11e À 03 0.000 0.651 392.207 0.000 0.993
TUE À 0.0195 0.003 À 0.014 À 6.806 0.000 0.683
WED À 0.0619 0.003 À 0.041 À 20.790 0.000 0.699
THU À 8.16e À 03 0.004 À 0.004 À 2.067 0.039 0.819
FRI À 0.0182 0.003 À 0.012 À 6.199 0.000 0.694
LOGQUANT À 0.0322 0.001 À 0.055 À 33.190 0.000 0.992
Dependent variable: LOGPRICE.

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except for the Thursday dummy, which is significant subset of grower dummy variables. These grower
at the 5% level. The model explained 58.3% of the dummy variables can be thought of as an indication
combined variance in Anthurium Tropical prices. As for a grower’s reputation. The analysis yielded similar
can be seen from Table 5, the SCRAUC dummy is results: a marginal increase in adjusted R2 without
negative, meaning that Hypothesis 1 is accepted: any significant changes in the other coefficients
screen auctioning did indeed lead to lower prices. compared to the original model. Therefore, although
The magnitude of the effect can be inferred by taking there is a very small reputation effect, it was left out
the exponent of b5, yielding an average price drop of of the model.
about 2.1%. With a weighted average price of about The discussion above is essentially about modeling
1.21 guilders, this corresponds to almost 2.5 cents. decisions that could have explained an effect attribut-
ed to screen auctioning. However, there are also rival
explanations that could not be modeled, yet may
6. Discussion account for the screen auctioning effect. When screen
auctioning was introduced, the product representation
Some other factors are not in the model that may changed, but at the same time two other aspects of the
also influence bidding behavior and price setting. It is auction process changed as well: the introduction of a
likely that different buyers bid structurally different third auction clock in the auction hall and the earlier
and therefore, dummy variables for the buyers should auctioning time. We will now discuss how these two
be incorporated in the model as well. For instance, rival explanations may affect flower prices.
most buyers buy ‘on order’, which means that they Several buyers complained about how difficult it
have to get a certain amount of flowers that day was to keep track of three clocks at the same time.
because of pre-orders from their customers. This will Most buyers at the flower auction act as agents for
most likely cause them to bid higher on average. their customers, and they mainly buy ‘on order’.
Similarly, there are some speculative buyers in the Hence, they do not want to risk not being able to
market that are only likely to bid for flowers if they deliver the flowers their customers ordered. This
can get them at a low price. This will most likely means that the increased cognitive complexity of the
cause them to bid lower on average. This extension of bidding process and the accompanying increase in
the model was considered but eventually decided uncertainty would lead to buyers stopping the auction
against. This extended model was tested on a subset clock sooner, resulting in higher prices.
of transactions with the 20 largest buyers, who There is no empirical evidence on the influence of
accounted for almost 28% of all transactions. Intro- the time of day on bidding behavior, but flower
duction of buyer dummy variables in this case only auctioneers told us that in their experience, earlier
marginally raised the adjusted R2 and did not signif- auctioning times lead to higher prices. As these two
icantly alter any of the coefficients of the variables potentially confounding factors both would have led
compared to the original model without the buyer to higher prices and not the lower prices we observed,
dummy variables. Since there are several hundreds of we conclude that the product representation factor is
buyers, the introduction of dummy variables for all the most likely explanation for the price drop.
buyers would add serious additional computational Finally, it is important to note that despite the price
requirements, without any qualitatively significant reduction, the flower auction still considered screen
changes in the result to be expected. So in the interest auctioning a moderate success. The auction receives a
of parsimony, although it does explain a small part of percentage of the transaction value as a fee, so lower
the variance, the buyer variable was left out of the prices imply a loss of transaction fees. However,
model. according to auction personnel, this loss was compen-
Similarly, one could argue that different sellers sated by the reduced costs of the internal logistical
(i.e. the growers) might receive different prices on processes by an order of magnitude. As the auction is
average for their flowers because of their reputation established as a cooperative of the growers, these
for producing high (or low) quality flowers. Like the reduced costs indirectly compensate growers for the
buyer’s case, we tested an extended model with a loss of direct revenue.

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7. Conclusions and implications system with four categories where growers self-report
the quality of the flowers. Partly because of oppor-
The paper presents results of empirical research on tunism, partly because of the few categories, as a
the price impact of a new method of auctioning, using result 98% of the flowers are in the highest quality
transaction data obtained at a large Dutch flower category. This effectively renders the quality rating
auction. In February 1996, this auction introduced system useless. Note that this is also in accordance
screen auctioning to separate the logistical processes with the results in [8], which identify Aucnet’s quality
from the price discovery process, thus decreasing the rating system as a key success factor of the auction.
costs and complexity of the total distribution process. Perhaps a good quality assurance process is the real
In screen auctioning, the buyers are still present in the key to a successful electronic market.
auction hall, but they are no longer shown the actual
flowers. Instead, a generic picture is displayed on a Acknowledgements
monitor next to the auction clock. Screen auctioning
can be seen as an intermediate step towards a full The authors gratefully acknowledge the comments
electronic market, since they both involve a shift from of two anonymous reviewers as well as the cooper-
live product representation to image-based product ation from the flower auction.
representation. The difference is that in screen auction-
ing, contrary to an electronic market, the buyers still
assemble physically in the auction hall. Despite the References
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Otto Koppius is an assistant professor at Matthijs Wolters is an assistant professor
the Rotterdam School of Management at in the Department of Management and
Erasmus University Rotterdam. He holds Organization at the Faculty of Economics
an MSc in Applied Mathematics from the and Business Administration, Free Univer-
University of Twente and a PhD in Busi- sity Amsterdam. He holds an MSc in
ness Administration from Erasmus Univer- econometrics from the University of Gro-
sity Rotterdam. His research interests ningen, the Netherlands, specializing in
include electronic markets and auctions, operations research and statistics and a
network analysis and decision theory. On PhD in information systems from the Rot-
these topics he has authored various con- terdam School of Management at Erasmus
ference papers including HICSS, WISE, University Rotterdam. His thesis investi-
INFORMS, ECIS and the Academy of Management. He has been gates the impact of Information and Communication Technology
a visiting researcher at the University of Michigan and at the IBM (ICT) in combination with modularity on supply chains of organ-
T.J. Watson Research Center. His dissertation won the Best Doctoral izations. His research focus is on how organizations, by using these
Dissertation Award at ICIS 2002. technologies and concepts, can increase their service towards
customers by becoming more flexible and responsive. On these
Eric van Heck is a Professor at the De- topics he has authored several conference papers and articles in
partment of Decision and Information Sci- edited books.
ences, Rotterdam School of Management,
Erasmus University Rotterdam. His main
research interests are EDI Systems, ICT-
enabled Business Network Redesign, and
Electronic Markets. He is a specialist in
Electronic Auctions. He is (co-)author/edi-
tor of 12 books, the latest of which is
Making Markets: How Firms Can Design
and Profit from Online Auctions and
Exchanges (co-authored with Ajit Kambil), published by Harvard
Business School Press. His articles have been published in journals
such as California Management Review, Communications of the
ACM, Harvard Business Review, Information Systems Research,
and WirtschaftsInformatik. Previously, he worked for Cap Gemini
Nederland, Wageningen University, and Tilburg University. He was
a visiting scholar at the Leonard N. Stern School of Business at New
York University. He received his MSc and PhD from Wageningen
University.