Eric Van Heck

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Electronic Auctions for Perishable Goods : Lessons Learned from a Decade in the Dutch Flower Industry Eric van Heck AUEB, Athens, June 30, 2003 e.heck@fbk.eur.nl : 

Electronic Auctions for Perishable Goods : Lessons Learned from a Decade in the Dutch Flower Industry Eric van Heck AUEB, Athens, June 30, 2003 e.heck@fbk.eur.nl

Menu: 

Menu 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

Focus talk: 

Focus 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!

Slide4: 

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

First study: Reengineering the Dutch Flower Auctions: 

First study: Reengineering the Dutch Flower 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)

Slide6: 

Dutch 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

Flower auction hall: 

Flower auction hall

Slide8: 

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.

Dutch auction clock: 

Dutch auction clock

Distribution to buyers: 

Distribution to buyers

Slide11: 

Four Case Studies Vidifleur Auction 1991 Sample Based Auction 1994 Tele Flower Auction as new entrant 1995 Buying At a Distance Auction 1996

Slide12: 

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

Slide13: 

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)

Slide14: 

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

Slide15: 

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

Slide16: 

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

Slide18: 

Tele Flower Auction

Slide19: 

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

Slide20: 

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

Slide21: 

TFA and KOA

Slide23: 

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

Critical factors: 

Critical 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

A model of Exchange Processes Updated version (2002): 

A model of Exchange Processes Updated version (2002) trade context processes basic trade processes in ”Making Markets" Kambil & Van Heck (2002). Harvard Business School Press. June 2002 product representation regulation influence dispute resolution search valuation logistics payment & settlements authentication communications & computing risk management

Two hurdles to value: 

Two 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.

Achieve critical mass quickly: 

Achieve critical mass quickly Subsidize early user adoption Increase the cost of alternative transaction mechanisms One step at the time. Reduce transition risk and effort

A Framework for Action: 

A 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 ?

For each process, conduct the five step analysis: 

For each process, conduct the five step analysis Map the current structure of market processes Identify how new technologies may be used to reengineer major market processes Consider how required process changes will affect each stakeholder Develop strategies for attracting important stakeholders Develop an action plan for introducing new trading processes

Second study: Screen Auctioning: 

Second 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)

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?

Screen Auctioning: Implementation: 

Screen 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.

Screen Auctioning: Implementation: 

Screen Auctioning: Implementation

Screen Auctioning: Implementation: 

Screen Auctioning: Implementation Screen auctioning introduced in February 1996 for Anthuriums, later also for Gerbera

Screen Auctioning: Theory: 

Screen 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)

Screen Auctioning: Main Hypotheses: 

Screen 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

Screen Auctioning: Data: 

Screen 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)

Screen Auctioning: Analysis: 

Screen 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

Screen Auctioning: Analysis: 

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

Screen Auctioning: Discussion: 

Screen 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.

Buying behavior under quality uncertainty: 

Buying behavior under quality 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

Screen Auctioning: Conclusion: 

Screen 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.

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

Third study: 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’

The KOA initiative: 

The 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)

KOA: Bidder differences: 

KOA: 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.

KOA: Hypotheses: 

KOA: 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

KOA: Model: 

KOA: 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

KOA: Results: 

KOA: 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

KOA: Discussion: 

KOA: 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

KOA: Limitations: 

KOA: 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)

Fourth study: KOA Bidder Analysis: 

Fourth 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)

Results KOA Bidder Analysis: 

Results 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

Interpretation KOA Bidder Analysis: 

Interpretation 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

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

Discussion: what about market state information?: 

Discussion: what about market state 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: ???

Conclusions: 

Conclusions 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.

Look at www.makingmarkets.org: 

Look at www.makingmarkets.org

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

Theory of Electronic Markets (Koppius, 2002) : 

Theory of Electronic Markets (Koppius, 2002)

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