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

2. 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

3. 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!

4. 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

5. 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)

6. 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

7. Flower auction hall

8. 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.

9. Dutch auction clock

10. Distribution to buyers

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

12. 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

13. 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)

14. 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

15. 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

16. 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

18. Tele Flower Auction

19. 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

20. 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

21. TFA and KOA

23. 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

24. 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

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

26. 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.

27. 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

28. 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 ?

29. 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

30. 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)

31. 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?

32. 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.

33. Screen Auctioning: Implementation

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

35. 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)

36. 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

37. 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)

38. 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

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

40. 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.

41. 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

42. 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.

43. 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’

44. 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)

47. 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.

48. 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

49. 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

50. 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