1 Electronic Auctions for Electronic Auctions for Perishable Goods : Perishable Goods : Lessons Learned from a Decade Lessons Learned from a Decade in the Dutch Flower Industry in the Dutch Flower Industry Eric van Heck AUEB, Athens, June 30, 2003 [email protected]
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1 Electronic Auctions for Perishable Goods : Lessons Learned from a Decade in the Dutch Flower Industry Electronic Auctions for Perishable Goods : Lessons.
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Electronic Auctions for Electronic Auctions for Perishable Goods :Perishable Goods :
Lessons Learned from a Decade Lessons Learned from a Decade in the Dutch Flower Industryin the Dutch Flower Industry
Central question of electronic market theory: how does Information and Communication Technology (ICT) change market behavior?
Focus this talk on traditional vs. electronic markets, not on the (electronic) markets vs. hierarchies debate.
We are moving from place to space!
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Many changes in switching from traditional to electronic markets occur often simultaneously; varieties of traditional markets and electronic markets occur. Consequently, many differences between traditional and electronic markets as well.
Which differences make a difference?
Methodological challenge in separating them!
This talk presents several analyses aimed at separation
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First study: Reengineering the Dutch First study: Reengineering the Dutch Flower AuctionsFlower Auctions
what are the characteristics and effects of the four electronic auction initiatives in the Dutch flower industry?
what are the reasons for the failures and the successes of these electronic initiatives?
what can we learn?
Four case studies in Dutch flower industry (Kambil & van Heck, Information Systems
Research, 1998)
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Dutch flower industryDutch flower industry
Holland is the world’s leading producer and distributor
Flowers: 59 % market share
Potted plants: 48% market share
VBA in Aalsmeer and BVH in Naaldwijk/Bleiswijk: annual turnover of $ 1,5 billion each
Growers are the sellers, wholesalers/retailers are the buyers
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Flower auction hallFlower auction hall
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Flowers transported from cold-storage warehouse to auction hall on carts.
Through auction hall below the respective clock (2-3 clocks per hall), sample shown by ‘raiser’ to buyers.
Buyers bid using Dutch auction clock: price starts high and drops fast. First person to stop the clock wins and pays that price. Invented in 1887.
Extremely fast! On average on transaction every 3 seconds.
Screen auctioning introduced in February 1996 for Anthuriums, later also for Gerbera
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Screen Auctioning: TheoryScreen Auctioning: Theory
Electronic product representation lacked certain information cues for bidders:
Color Possible diseases or imperfections Stiffness of the stem (important freshness indicator!)
Lemons problem! (Akerlof, 1970)
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Screen Auctioning: Main HypothesesScreen Auctioning: Main Hypotheses
Overall less product quality information available, so we have:
Hypothesis 1: Screen auctioning will lead to lower prices
Hypothesis 2: The screen auctioning effect will be stronger for more expensive flowers
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Screen Auctioning: DataScreen Auctioning: Data
Transaction database available, containing data on the transaction (price, quantity, date), as well as the flower (diameter, stemlength, quality code) and the identity of buyer and grower.
Additional control variable: VBN-price, average Anthurium price at all other Dutch flower auction for that month
All Anthurium transactions from 1995-1997 (N= 372,856)
Effects of electronic product representation separated from effects of lower search costs.
Lower prices in electronic markets can partially be explained by deficiencies in product representation (not just lower search costs) and expensive products suffer more.
Aucnet’s product representation and quality rating system increased prices, so a good product representation is essential for success.
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Third study: Third study: Buying-At-A-Distance (KOA) Buying-At-A-Distance (KOA)
The first study dealt with difference in product representation, but another category of differences is relevant:
Market State Information: public, non-transaction signals that influence trader behavior (adapted from Coval+Shumway, 2001)
‘Buzz’
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The KOA initiativeThe KOA initiative
Electronic bidding at a large Dutch flower auction
Online/KOA-bidders bid on the same clocks as offline bidders– Detailed comparison possible!
Two categories of KOA-bidders: internal (in the same building) and external (off-site)
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KOA: Bidder differencesKOA: Bidder differences
Internal KOA-buyers vs. auction hall-buyers: lower search costs and lower switching costs.
External KOA-buyers vs. auction hall-buyers: lower search costs and lower switching costs, less information about product quality and also less market state information.
Internal KOA-buyers vs. external KOA-buyers: more information about product quality and market state.
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KOA: HypothesesKOA: Hypotheses
H3: Because of lower search costs and lower switching costs, KOA-buyers will bid less than hall-buyers
H4a: Because of lower search costs and lower switching costs, both internal and external KOA buyers will bid less than auction hall buyers
H4b: Because of more product quality information being available to them, internal KOA buyers will bid more than external KOA buyers
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KOA: ModelKOA: Model
Regression model:
PRICE = + 1*DIAM + 2*WKDAY + 3*VBN + 4*QUANT +
5,I*FLWTYPEi + 6*KOAINT + 7 *KOAEXT+ .
81,803 transactions for flower Anthurium
Sequential regression: first the controls, then the KOA
variable
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KOA: ResultsKOA: Results
R2 = 0.713 after the first step, after addition of KOA only marginal, but significant increase.
KOA-coefficient 6<0, in accordance with H3
H4: KOAINT negative as expected, but KOAEXT
slightly positive and not significant
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KOA: DiscussionKOA: Discussion
Two surprises:– External KOA-bidders pay more than internal
KOA-bidders– External KOA-bidders pay the same as bidders in
the auction hall
Possible explanations:– Bidder heterogeneity is present, but no really
logical explanation– Market state information is important,
particularly regarding number of bidders
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KOA: LimitationsKOA: Limitations
Explanatory power of KOA for flower buying model negligible (but the goal was establishing a theoretical effect)
Causality of market state information is inferred, not rigorously controlled for ex ante (but laboratory experiments are in preparation)
Results only for one flower type (but replication data is being analyzed currently)
What about quality information?What about quality information?
Similar argument as in the screen auctioning case: the less quality information, the more important diameter
KOAINT: (Diam)=16.803 KOAEXT: (Diam)=18.749
Slight spanner in the works: (Diam)=17.954 for the auction hall buyers, even though they should be closer to the internals than the externals
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Discussion: what about market state Discussion: what about market state information?information?
How many people and who exactly are bidding, is salient information to bidders, but what if this is missing?
Option 1: Make conservative estimates, which would lead to earlier (and higher?) bidding
Option 2: Wait in the wings, which would lead to later (and lower?) bidding
Option 3: ???
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ConclusionsConclusions
Study 1: Markets are the meeting point for multiple stakeholders with conflicting incentives. No new IT-based initiative is likely to succeed if any powerful stakeholder is worse off after the IT-enabled innovation.
Study 2: Lower prices of electronic markets are partly due to lower quality of product representation;
Study 2+3+4: Different types of information cues (product information, market state information) in electronic markets lead to subtle changes in buying behavior;
Study 3+4: Lower search and switching costs lead to higher market transparency and therefore lower prices;
Information architecture of the electronic market is important.
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Look at www.makingmarkets.orgLook at www.makingmarkets.org
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And more info:And more info:
Otto Koppius, Information Architecture and Electronic Market Performance,PhD thesis, ERIM nr.13, May 2002. (www.erim.eur.nl)