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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

Eric van Heck

AUEB, Athens, June 30, 2003e.heck@fbk.eur.nl

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MenuMenu

Motivation and Focus

First study: Reengineering Dutch Flower Auctions

Second study: Screen Auctioning

Third study: Buying-At-A-Distance (KOA)

Fourth study: KOA Bidder Analysis

Conclusions

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Focus talkFocus talk

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.

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Dutch auction clockDutch auction clock

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Distribution to buyersDistribution to buyers

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Four Case StudiesFour Case Studies

Vidifleur Auction 1991

Sample Based Auction 1994

Tele Flower Auction as new entrant 1995

Buying At a Distance Auction 1996

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1. Vidifleur Auction (VA)1. Vidifleur Auction (VA)

BVH / Potted plants / 1991

real time video images displayed at a screen in the auction hall

product representation: real lot on site and video image on screen

buyers bid in the auction hall and on-line

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Why was VA a failure?Why was VA a failure?

no new efficiencies for the buyers

quality of the video display was poor

trading from outside the hall created an informational disadvantage (no social interaction)

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2. Sample Based Auction (SBA)2. Sample Based Auction (SBA)

VBA / Potted Plants / 1994

Logistics directly from grower’s to buyer’s place

Quality grading on sample

EDI technology

Product representation: sample of lot

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Why was SBA a failure?Why was SBA a failure?

Buyers didn’t trust the sample

Slower auction because of specification of packaging/delivery by buyers

Next day delivery was for some buyers difficult

SBA became in a dead spiral: decreasing supply - lower prices

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3. Tele Flower Auction (TFA)3. Tele Flower Auction (TFA)

East African Flowers / Flowers / 1995

Buyers can search supply data base

Logistics from storage rooms to buyer’s place

Product representation: real time digital image on screen

Buyers bid on-line via ISDN connection

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Tele Flower AuctionTele Flower Auction

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Why is TFA a success?Why is TFA a success?

Buyers trust the quality of the flowers (indicated on their screen)

After-sales process is fast: delivery within 30 minutes by EAF

Use of Dutch auction clock: no learning barriers

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4. Buying at a Distance auction (KOA)4. Buying at a Distance auction (KOA)

BVH / Flowers / 1996

Buyers can search supply data base

Logistics via auction room to buyers’ place

Buyers can bid off-line and on-line

Real lot on site, digital image on screen

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TFA and KOATFA and KOA

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Why is KOA a success?Why is KOA a success?

Better overview and communication between purchase and sales people of the wholesale firms

Lower travel costs for on-line buyers

Amount of buyers (physically or electronically connected) will be stable or increase – expect increasing prices

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Critical factorsCritical factors

Vidifleur Auction : product representation on screen, information disadvantage of online buyers

Sample based auction : product representation by sample, slower auction, unequally distributed benefits for sellers and buyers

Tele flower auction: digital product representation, logistics, ISDN technology, only way to get African products, low learning costs

Buying At a Distance: More reach for buyers and auctioneer

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A model of Exchange ProcessesA model of Exchange ProcessesUpdated version (2002)Updated version (2002)

trade contextprocesses

basic tradeprocesses

in ”Making Markets"Kambil & Van Heck (2002). Harvard Business School Press. June 2002

product representation

regulation influence disputeresolution

search valuation logistics payment &settlements

authentication

communications & computing

riskmanagement

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Two hurdles to valueTwo hurdles to value

New electronic markets challenge the status quo and the existing relationships between buyers and sellers.

New market mechanisms must at a minimum improve some or all the basic processes.

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Achieve critical mass quicklyAchieve critical mass quickly

Subsidize early user adoption

Increase the cost of alternative transaction mechanisms

One step at the time.

Reduce transition risk and effort

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A Framework for ActionA Framework for Action

Buyers Market Maker Sellers or Auctioneer

Processes

Search Pricing Logistics Payment & Settlement Authentication Product representation Regulation Risk management Influence Dispute resolution Communications & Computing

Net Benefits Positive or Positive or Positive Negative ? Negative ? Negative ?

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For each process, conduct the five step For each process, conduct the five step analysisanalysis

1. Map the current structure of market processes

2. Identify how new technologies may be used to reengineer major market processes

3. Consider how required process changes will affect each stakeholder

4. Develop strategies for attracting important stakeholders

5. Develop an action plan for introducing new trading processes

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Second study: Screen AuctioningSecond study: Screen Auctioning

What are the implications of electronic product representation?

Field study at a large Dutch flower auction (Koppius, van Heck, and Wolters, forthcoming in Decision Support Systems)

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Screen Auctioning: why?Screen Auctioning: why?

High logistical complexity of transporting flowers through the auction block.

Logistical and trade processes are tightly coupled.

Breakdown of logistics causes immediate halt of trading.

How to decouple the logistical processes from the trade processes?

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Screen Auctioning: ImplementationScreen Auctioning: Implementation

Replace the physical product representation with electronic product representation.

Flowers remain in cold storage warehouse and go directly to the shipping area after the sale

Buyers are still in the auction hall and see a (generic) picture of the flower instead, plus the regular product characteristics of the old situation.

Not a fully electronic market, but a step towards.

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Screen Auctioning: ImplementationScreen Auctioning: Implementation

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Screen Auctioning: ImplementationScreen Auctioning: Implementation

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)

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Screen Auctioning: AnalysisScreen Auctioning: Analysis

OLS Regression model:

PRICE = + 1*DIAM + 2*WKDAY + 3*VBN +

4*QUANT + 5,I*FLWTYPEi + 6 *SCRAUC + .

R2 = 0.588

6 is negative overall, as well as for 8 of the 9

flower-subtypes separately.

Conclusion: hypothesis 1 accepted

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Screen Auctioning: AnalysisScreen Auctioning: Analysis

Hypothesis 2: R2 = -.735 (sig. < 0.05)

PRODUCT

806040200-20

SCRAUC

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20

10

0

-10

-20

-30

-40

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Screen Auctioning: DiscussionScreen Auctioning: Discussion

Two alternative explanations for lower prices:

Earlier auctioning time for screen auctioning, but this would have led to higher prices.

Introduction of third auction clock, but the increased cognitive complexity would be likely to lead to higher prices, given risk-averse buyers.

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Buying behavior under Buying behavior under quality uncertaintyquality uncertainty

Behavioral decision theory: in the absence of salient cues, people rely more on the available cues (compensatory decision-making)

Corollary: diameter should become a more important factor after screen auctioning

Pre: (Diam) = 14.094 Post: (Diam) = 16.214

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Screen Auctioning: ConclusionScreen Auctioning: Conclusion

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)

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Fourth study: KOA Bidder AnalysisFourth study: KOA Bidder Analysis

Are the differences due to bidder heterogeneity?

Use screen auctioning dataset to estimate bidder differences

Compare KOAINT and KOAEXT for 1995 (pre-screen auctioning) and 1998 (post-KOA)

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Results KOA Bidder AnalysisResults KOA Bidder Analysis

1995: (KOAINT) = -1.65 <0.01 (KOAEXT) = 1.109 (but not significant)

1998: (KOAINT) = -3.608 <0.01 (KOAEXT) = -2.767 <0.05

Future external bidders indistinguishable from auction hall bidders, but future internal bidders already bid lower than average

Strong KOA-effect for both types of bidders, even more so for the external bidders.

Lower search and switching costs more salient than product quality information and market state information

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Interpretation KOA Bidder AnalysisInterpretation KOA Bidder Analysis

Internal KOA bidders were the early adopters and they still have the best of both worlds

But the external KOA bidders (fast followers) are catching up

More KOA-adopters implies more market transparency, further lowering prices

Corroborating evidence: influence of VBN prices KOAINT, KOAEXT: (VBN)<1 Hall: (VBN)>1

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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)

Best PhD Dissertation ICIS 2002 Barcelona

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S1

S3

I2B2

B1

I1

S2

B3

Information exchange processes among traders

Information Architecture

S4

Information and Communication Technology

Market Outcome

Market rules (allocation and transaction validity)

Market info. set:- Product info.- Transaction info.- Market state info.

Market Performance

Performance Criteria

Theory of Theory of Electronic Markets Electronic Markets (Koppius, 2002)(Koppius, 2002)