Presentation on theme: "1 Electronic Auctions for Perishable Goods : Lessons Learned from a Decade in the Dutch Flower Industry Electronic Auctions for Perishable Goods : Lessons."— Presentation transcript:
1 Electronic Auctions for Perishable Goods : Lessons Learned from a Decade in the Dutch Flower Industry Electronic Auctions for Perishable Goods : Lessons Learned from a Decade in the Dutch Flower Industry Eric van Heck AUEB, Athens, June 30, 2003 email@example.com
2Menu n Motivation and Focus n First study: Reengineering Dutch Flower Auctions n Second study: Screen Auctioning n Third study: Buying-At-A-Distance (KOA) n Fourth study: KOA Bidder Analysis n Conclusions
3 Focus talk n Central question of electronic market theory: how does Information and Communication Technology (ICT) change market behavior? n Focus this talk on traditional vs. electronic markets, not on the (electronic) markets vs. hierarchies debate. n We are moving from place to space!
4 n 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. n Which differences make a difference? n Methodological challenge in separating them! n This talk presents several analyses aimed at separation
5 First study: Reengineering the Dutch Flower Auctions n what are the characteristics and effects of the four electronic auction initiatives in the Dutch flower industry? n what are the reasons for the failures and the successes of these electronic initiatives? n what can we learn? n Four case studies in Dutch flower industry (Kambil & van Heck, Information Systems Research, 1998)
6 Dutch flower industry Dutch flower industry n Holland is the worlds leading producer and distributor n Flowers: 59 % market share n Potted plants: 48% market share n VBA in Aalsmeer and BVH in Naaldwijk/Bleiswijk: annual turnover of $ 1,5 billion each n Growers are the sellers, wholesalers/retailers are the buyers
8 n Flowers transported from cold-storage warehouse to auction hall on carts. n Through auction hall below the respective clock (2- 3 clocks per hall), sample shown by raiser to buyers. n 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. n Extremely fast! On average on transaction every 3 seconds.
11 Four Case Studies Four Case Studies n Vidifleur Auction 1991 n Sample Based Auction 1994 n Tele Flower Auction as new entrant 1995 n Buying At a Distance Auction 1996
12 1. Vidifleur Auction (VA) 1. Vidifleur Auction (VA) n BVH / Potted plants / 1991 n real time video images displayed at a screen in the auction hall n product representation: real lot on site and video image on screen n buyers bid in the auction hall and on-line
13 Why was VA a failure? Why was VA a failure? n no new efficiencies for the buyers n quality of the video display was poor n trading from outside the hall created an informational disadvantage (no social interaction)
14 2. Sample Based Auction (SBA) 2. Sample Based Auction (SBA) n VBA / Potted Plants / 1994 n Logistics directly from growers to buyers place n Quality grading on sample n EDI technology n Product representation: sample of lot
15 Why was SBA a failure? Why was SBA a failure? n Buyers didnt trust the sample n Slower auction because of specification of packaging/delivery by buyers n Next day delivery was for some buyers difficult n SBA became in a dead spiral: decreasing supply - lower prices
16 3. Tele Flower Auction (TFA) 3. Tele Flower Auction (TFA) n East African Flowers / Flowers / 1995 n Buyers can search supply data base n Logistics from storage rooms to buyers place n Product representation: real time digital image on screen n Buyers bid on-line via ISDN connection
19 Why is TFA a success? Why is TFA a success? n Buyers trust the quality of the flowers (indicated on their screen) n After-sales process is fast: delivery within 30 minutes by EAF n Use of Dutch auction clock: no learning barriers
20 4. Buying at a Distance auction (KOA) 4. Buying at a Distance auction (KOA) n BVH / Flowers / 1996 n Buyers can search supply data base n Logistics via auction room to buyers place n Buyers can bid off-line and on-line n Real lot on site, digital image on screen
23 Why is KOA a success? Why is KOA a success? n Better overview and communication between purchase and sales people of the wholesale firms n Lower travel costs for on-line buyers n Amount of buyers (physically or electronically connected) will be stable or increase – expect increasing prices
24 Critical factors n Vidifleur Auction : product representation on screen, information disadvantage of online buyers n Sample based auction : product representation by sample, slower auction, unequally distributed benefits for sellers and buyers n Tele flower auction: digital product representation, logistics, ISDN technology, only way to get African products, low learning costs n Buying At a Distance: More reach for buyers and auctioneer
25 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 regulationinfluence dispute resolution searchvaluationlogisticspayment & settlements authentication communications & computing risk management
26 Two hurdles to value n New electronic markets challenge the status quo and the existing relationships between buyers and sellers. n New market mechanisms must at a minimum improve some or all the basic processes.
27 Achieve critical mass quickly n Subsidize early user adoption n Increase the cost of alternative transaction mechanisms n One step at the time. n Reduce transition risk and effort
28 A Framework for Action Buyers Market Maker Sellers or Auctioneer Processes n Search n Pricing n Logistics n Payment & Settlement n Authentication n Product representation n Regulation n Risk management n Influence n Dispute resolution n Communications & Computing Net Benefits Positive or Positive or Positive Negative ? Negative ? Negative ?
29 For each process, conduct the five step analysis 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
30 Second study: Screen Auctioning n What are the implications of electronic product representation? n Field study at a large Dutch flower auction (Koppius, van Heck, and Wolters, forthcoming in Decision Support Systems)
31 Screen Auctioning: why? n High logistical complexity of transporting flowers through the auction block. n Logistical and trade processes are tightly coupled. n Breakdown of logistics causes immediate halt of trading. n How to decouple the logistical processes from the trade processes?
32 Screen Auctioning: Implementation n Replace the physical product representation with electronic product representation. n Flowers remain in cold storage warehouse and go directly to the shipping area after the sale n 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. n Not a fully electronic market, but a step towards.
34 Screen Auctioning: Implementation n Screen auctioning introduced in February 1996 for Anthuriums, later also for Gerbera
35 Screen Auctioning: Theory n Electronic product representation lacked certain information cues for bidders: n Color n Possible diseases or imperfections n Stiffness of the stem (important freshness indicator!) n Lemons problem! (Akerlof, 1970)
36 Screen Auctioning: Main Hypotheses n Overall less product quality information available, so we have: n Hypothesis 1: Screen auctioning will lead to lower prices n Hypothesis 2: The screen auctioning effect will be stronger for more expensive flowers
37 Screen Auctioning: Data n 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. n Additional control variable: VBN-price, average Anthurium price at all other Dutch flower auction for that month n All Anthurium transactions from 1995-1997 (N= 372,856)
38 Screen Auctioning: Analysis n OLS Regression model: PRICE = + 1 *DIAM + 2 *WKDAY + 3 *VBN + 4 *QUANT + 5,I *FLWTYPE i + 6 *SCRAUC +. n R 2 = 0.588 6 is negative overall, as well as for 8 of the 9 flower-subtypes separately. n Conclusion: hypothesis 1 accepted
39 Screen Auctioning: Analysis n Hypothesis 2: R 2 = -.735 (sig. < 0.05)
40 Screen Auctioning: Discussion n Two alternative explanations for lower prices: n Earlier auctioning time for screen auctioning, but this would have led to higher prices. n 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 n Behavioral decision theory: in the absence of salient cues, people rely more on the available cues (compensatory decision-making) n Corollary: diameter should become a more important factor after screen auctioning n Pre: (Diam) = 14.094 n Post: (Diam) = 16.214
42 Screen Auctioning: Conclusion n Effects of electronic product representation separated from effects of lower search costs. n Lower prices in electronic markets can partially be explained by deficiencies in product representation (not just lower search costs) and expensive products suffer more. n Aucnets 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) n The first study dealt with difference in product representation, but another category of differences is relevant: n Market State Information: public, non-transaction signals that influence trader behavior (adapted from Coval+Shumway, 2001) n Buzz
44 The KOA initiative n Electronic bidding at a large Dutch flower auction n Online/KOA-bidders bid on the same clocks as offline bidders – Detailed comparison possible! n 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 n H3: Because of lower search costs and lower switching costs, KOA-buyers will bid less than hall- buyers n H4a: Because of lower search costs and lower switching costs, both internal and external KOA buyers will bid less than auction hall buyers n H4b: Because of more product quality information being available to them, internal KOA buyers will bid more than external KOA buyers
49 KOA: Model n Regression model: PRICE = + 1 *DIAM + 2 *WKDAY + 3 *VBN + 4 *QUANT + 5,I *FLWTYPE i + 6 *KOAINT + 7 *KOAEXT+. n 81,803 transactions for flower Anthurium n Sequential regression: first the controls, then the KOA variable
50 KOA: Results n R 2 = 0.713 after the first step, after addition of KOA only marginal, but significant increase. KOA-coefficient 6 <0, in accordance with H3 n H4: KOAINT negative as expected, but KOAEXT slightly positive and not significant
51 KOA: Discussion n Two surprises: – External KOA-bidders pay more than internal KOA-bidders – External KOA-bidders pay the same as bidders in the auction hall n Possible explanations: – Bidder heterogeneity is present, but no really logical explanation – Market state information is important, particularly regarding number of bidders
52 KOA: Limitations n Explanatory power of KOA for flower buying model negligible (but the goal was establishing a theoretical effect) n Causality of market state information is inferred, not rigorously controlled for ex ante (but laboratory experiments are in preparation) n Results only for one flower type (but replication data is being analyzed currently)
53 Fourth study: KOA Bidder Analysis n Are the differences due to bidder heterogeneity? n Use screen auctioning dataset to estimate bidder differences n Compare KOAINT and KOAEXT for 1995 (pre-screen auctioning) and 1998 (post-KOA)
54 Results KOA Bidder Analysis n 1995: – (KOAINT) = -1.65 <0.01 – (KOAEXT) = 1.109 (but not significant) n 1998: – (KOAINT) = -3.608 <0.01 – (KOAEXT) = -2.767 <0.05 n Future external bidders indistinguishable from auction hall bidders, but future internal bidders already bid lower than average n Strong KOA-effect for both types of bidders, even more so for the external bidders. n Lower search and switching costs more salient than product quality information and market state information
55 Interpretation KOA Bidder Analysis n Internal KOA bidders were the early adopters and they still have the best of both worlds n But the external KOA bidders (fast followers) are catching up n More KOA-adopters implies more market transparency, further lowering prices n Corroborating evidence: influence of VBN prices n KOAINT, KOAEXT: (VBN)<1 n Hall: (VBN)>1
56 What about quality information? n Similar argument as in the screen auctioning case: the less quality information, the more important diameter n KOAINT: (Diam)=16.803 n KOAEXT: (Diam)=18.749 n 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
57 Discussion: what about market state information? n How many people and who exactly are bidding, is salient information to bidders, but what if this is missing? n Option 1: Make conservative estimates, which would lead to earlier (and higher?) bidding n Option 2: Wait in the wings, which would lead to later (and lower?) bidding n Option 3: ???
58Conclusions n 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. n Study 2: Lower prices of electronic markets are partly due to lower quality of product representation; n Study 2+3+4: Different types of information cues (product information, market state information) in electronic markets lead to subtle changes in buying behavior; n Study 3+4: Lower search and switching costs lead to higher market transparency and therefore lower prices; n Information architecture of the electronic market is important.
60 And more info: Otto Koppius, Information Architecture and Electronic Market Performance, PhD thesis, ERIM nr.13, May 2002. (www.erim.eur.nl)www.erim.eur.nl Best PhD Dissertation ICIS 2002 Barcelona
61 S1 S3 I2 B2 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 Electronic Markets (Koppius, 2002)