The Importance of Product Representation Online
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The impact of product representation online - results of an empirical analysis of image auctioning of flowers.

The impact of product representation online - results of an empirical analysis of image auctioning of flowers.

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    The Importance of Product Representation Online The Importance of Product Representation Online Document Transcript

    • ARTICLE IN PRESS Decision Support Systems xx (2003) xxx – xxx www.elsevier.com/locate/dsw The importance of product representation online: empirical results and implications for electronic markets Otto R. Koppius a,*, Eric van Heck a, Matthijs J.J. Wolters b a Department of Decision and Information Sciences (F1-31), Rotterdam School of Management, Erasmus University Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands b Free University Amsterdam, Amsterdam, The Netherlands Received 1 May 2001; accepted 1 September 2002 Abstract We investigate the effects of online product representation at a large Dutch flower auction that implemented screen auctioning. In screen auctioning, flowers are not physically shown to the buyers anymore; instead, an image is presented on a screen to buyers in the auction hall. This online product representation entailed a decrease in information about flower quality compared to the physical product representation. Analysis of the transaction data before and after screen auctioning revealed lower prices after the introduction of screen auctioning. We conclude that deficient product representation may be a partial explanation for reduced prices in electronic markets. D 2003 Elsevier B.V. All rights reserved. Keywords: Electronic commerce; Electronic markets; Auctions; Product representation; Flower auction 1. Introduction auction is the matching of demand and supply at the ‘best price’ at one specific point in time. The advan- The rapid developments in information technology tages, however, must be weighed up against lower and its applications in business have resulted in switching costs for auction participants. Are auctions electronic markets being increasingly popular. These always beneficial to the companies involved? For markets can result in significant savings for both instance, what is the impact of electronic auctions sellers and buyers. Savings are made by reducing on the prices of traded goods? A lot of attention has transaction costs, increasing the circle of potential been paid to potential benefits for participants in customers as well as by improving the search-and- electronic markets, but not much is known about the find capabilities for all parties concerned [12]. Elec- actual consequences of the introduction of electronic tronic auctioning in particular is a rapidly expanding markets for the various participants. These consequen- application [7]. The additional benefit of an electronic ces are to some extent dependent on the design of the electronic market and one of these design factors is the product representation online. Possible represen- * Corresponding author. Tel.: +31-10-408-2032; fax: +31-10- tation methods vary from listing the product character- 408-9010. istics to providing a picture to a full audio/video E-mail address: O.Koppius@fbk.eur.nl (O.R. Koppius). presentation of the product in question or combina- 0167-9236/03/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0167-9236(03)00097-6 DECSUP-01095
    • ARTICLE IN PRESS 2 O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx tions of these. The central research question here is marketplaces could reduce the search costs that buyers how a different product representation online impacts must incur to acquire information about seller prices the prices paid for those products. and product offerings. The lowered search costs allow In this article we present the first part of a large buyers to look at more product offerings and make it research project dealing with empirical research on difficult for sellers to sustain high prices. Bakos electronic auctions in the Dutch flower industry. We therefore formulated a reduced price hypothesis: pri- investigate a new method of auctioning at an anony- ces would be lower in electronic markets. Empirical mous Dutch flower auction called screen auctioning, research related to electronic markets, however, where flowers are not physically shown to the buyers showed mixed results. For example, in [2], satellite anymore, but instead an image is presented on a video cattle auctions were compared with regional screen to the buyers in the auction hall. Note that market prices. Prices for both the regional and video the screen auction itself is not an electronic market, auctions were adjusted for quality differences, trans- because buyers still physically assemble in the auction portation costs, commissions, and days to delivery. hall. However, it can be considered an intermediate Net prices paid by buyers and received by sellers in step towards an electronic market, because in an video auctions exceeded the prices for the three major electronic market the products are not physically regional auction markets. In [8], it was showed that presented either. Moving to a completely electronic the prices of secondhand cars traded through an market could entail many more changes such as electronic market place (Aucnet) could actually be buyers not physically meeting anymore or lower higher than those products sold in traditional markets. search costs that could have varying effects on the Potential explanations are that Aucnet focused on different stakeholders in the market. The investigation relatively newer secondhand cars and also buyers of screen auctioning provides an isolation of the are willing to pay the premium (i.e. a slightly higher product representation effects from these other effects. price) because they not only avoid a large waste of The remainder of this article is organized as fol- time spent on attending physical auctions but also lows: Section 2 summarizes previous research on easily locate a vehicle that best matches their prefer- electronic markets and Section 3 explains the charac- ences. In both these studies, multiple factors could teristics of the Dutch flower industry and its auctions. have accounted for the price differences, making In Section 4, we analyze screen auctioning from a unambiguous interpretation of the results difficult. In process – stakeholder perspective. In Section 5, the this study however, we are able to isolate the effects of model is presented and validated using transaction one of these factors, namely the effects of online data obtained at an anonymous Dutch flower auction. product representation. Section 6 discusses the results and Section 7 ends with conclusions and implications for research and practice. 3. The flower industry and auctions 2. Previous research on electronic markets The Netherlands is the world’s leading producer and distributor of cut flowers. The Dutch dominated Prior research on the effects of Information and the world export market for cut flowers in 1996 with Communication Technology (ICT) on exchange a 59% share and for potted plants with a 48% share. organizations and processes typically applied transac- The world’s two biggest flower auctions are in Aals- tion cost theory and agency theory to predict shifts meer (Flower Auction Aalsmeer) and Naaldwijk/ from hierarchies towards market or other intermediate Bleiswijk (Flora Holland); every day on average 30 forms of organization [4,5,9]. A central argument of million flowers—originating not only from the Neth- these articles was that ICT would improve communi- erlands but also from countries such as Israel, Kenya cation searches, monitoring and information-sorting and Zimbabwe—are traded in 100,000 transactions. capabilities, to reduce transaction costs and allow In total there are seven Dutch flower auctions, purchasers to take advantage of production economics namely in the villages of Aalsmeer, Naaldwijk/Bleis- available in markets. Bakos [3] argued that electronic wijk, Rijnsburg, Grubbenvorst, Eelde, Bemmel, and
    • ARTICLE IN PRESS O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx 3 Vleuten. The Dutch flower auctions play a vital role price determined by the auctioneer, and drops until a in Holland’s leadership of this industry, by providing buyer stops the clock by pushing a button. The efficient centers for price discovery and transactions auctioneer asks the buyer by intercom how many of flowers between buyers and sellers. These auctions units of the lot he or she will buy. The buyer provides traditionally use the ‘Dutch auction’ as the mecha- the number of units. The clock is then reset, and the nism for price discovery. They are established as process begins for the remaining flowers, sometimes cooperatives by the Dutch growers. introducing a new minimum purchase quantity, until We will describe the auction rules of the Dutch all units of the lot are sold. Table 1 illustrates the flower auction concept using some empirical data to auction process by an example with some actual illustrate its characteristics and results [11]. There are auction data. The first rows deal with producer 1234 approximately 3500 varieties of cut flowers. These (column 2), who is responsible for transactions 408 to varieties are classified into 120 auction groups, 420 (column 1). On January 4, 1996 this producer according to the variety, size of the lot, and quality delivered roses (product group 52), or more specifi- of the flowers. Dutch flower auctions use a clock for cally the brown rose ‘Leonidas’ (product number price discovery as follows. The computerized auction 10288). He delivered four lots of that type of rose clock in the room provides the buyers with product (column 4). These lots had the same type of quality characteristics such as stemlength or diameter or (A1), but were different in length (70, 60, 50, and 80 number of leaves (dependent on the particular flower cm, respectively) and in amounts of 9, 5, 3, and 12 type), as well as information on the producer, unit of units, respectively. The first lot was auctioned, and currency, quality and minimum purchase quantity. buyer 3782 took 1 unit (out of 9) for a price of 93 The flowers are transported through the front of the cents per stem. The rest of the lot was auctioned again, auction room, where there is a person (the ‘raiser’) and buyer 1854 bought 2 units for 95 cents. The who shows the flower to the more than hundred remainder of the lot (6 units) was auctioned, and buyers in the stand. The clock hand starts at a high buyer 727 bought 3 units for 96 cents. Finally, buyer Table 1 Auction data illustrating the Dutch flower auction concept (source: [11]) Transaction Producer Product Product Quality Length Total no. Stems Buyer No. of Price in no. group in cm of units per unit units cts./stem 408 1234 52 10288 A1 70 9 100 3782 1 93 409 1854 2 95 410 727 3 96 411 42 3 97 412 1234 52 10288 A1 60 5 100 727 4 89 413 1824 1 91 414 1234 52 10288 A1 50 3 100 3090 1 67 415 2528 2 68 416 1234 52 10288 A1 80 12 100 3282 4 109 417 4157 1 115 418 134 3 115 419 3462 2 116 420 3042 2 117 727 12 52 11087 A1 80 3 100 2893 2 91 728 752 1 87 729 12 52 11087 A1 70 6 100 727 1 79 730 1768 2 77 731 3004 3 77 732 12 52 11087 A1 60 8 100 3219 1 56 733 2669 3 56 734 727 4 54 735 12 52 11087 A1 50 3 100 727 3 46
    • ARTICLE IN PRESS 4 O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx 42 bought the rest of the lot for 97 cents. The table hall. In February 1998, screen auctioning was expand- shows that the price may increase during the auction- ed to cover the flower type Gerbera as well. The ing of a lot (see, for example, transactions 408 research in this article deals with Anthuriums only. through 411) or may decrease within a lot (see, for example, transactions 729 through 731). So the price is very volatile, considering different lots of the same 4. Screen auctioning: process– stakeholder analysis producer or different lots of different producers. Buyers must be physically present in the auction Previous research dealing with reengineering the room. In practice, it turns out that the Dutch flower Dutch flower auctions is published in [6,10,11]. auction is an extremely time-efficient auction mecha- Kambil and van Heck [6] specify a generalizable nism: it can handle a transaction every 4 seconds. It model of exchange processes and develop a pro- also reduces the amount of time that growers must cess –stakeholder analysis framework to evaluate al- spend on price discovery and bidding; hence they can ternative market designs. They identify five basic focus on production. The auction provides a central trade processes: search, valuation, logistics, payments location for the meeting of buyers, creating efficient and settlements, and authentication. The basic trade quality control and logistics of product redistribution. processes are distinct processes required in all trans- This auction has ‘‘backtracking’’ possibilities: al- actions of goods and services. The trade context though the price movements are decreasing per sub- processes facilitate and enable or reduce the costs or lot, the price can be multidirectional (up or down) ‘‘frictions’’ in the basic processes. The five trade within the whole lot. Buyers can withdraw their context processes are communications and comput- willingness to buy: they can indicate to the auctioneer ing, product representation, legitimization, influence, fewer or more units then they originally intended to at and dispute resolution. the time they pushed the button. During the auction- Table 2 presents the results of the analysis of ing of the lot, buyers produce information on the value screen auctioning with the help of the process – stake- of the lot; this information is available to all buyers. holder framework. For each of the stakeholders, the Given these characteristics, we call the Dutch flower expectations related to screen auctioning are described auction a multi-unit, discriminatory auction. and as can be seen, sellers, intermediary and buyers As mentioned previously, all flowers that are put differed in their expectations of the effects of screen up for auction pass through the auction hall in order to auctioning. Table 2 also describes the changes for be shown to the buyers just before the bidding starts. each of the processes compared with the traditional Given the large daily turnover, this process entails Dutch flower auction system. tremendous logistical difficulties for the auction. To alleviate these, the flower auction introduced screen auctioning for the flower type Anthurium in February 5. Screen auctioning: multiple regression model 1996. In screen auctioning, the buyers are still present in the auction hall, but they are no longer shown the To quantitatively investigate the impact of screen flowers as in the traditional auction method. Instead, auctioning, the auction transaction database from the flowers remain in the warehouse and buyers are January 1995 until December 1997 was available shown a picture for that type of flower. This is a (screen auctioning was introduced on February 13th, generic picture, irrespective of lot differences within 1996). In this database, for every transaction various the same type, but they still see the specific product data are kept, including data related to the seller, the characteristics of that particular lot below the auction buyer, the product (quality, stemlength and diameter, clock. When screen auctioning was introduced, two etc.), and the transaction itself (price, quantity, date). other aspects of the trading process changed as well. We constructed the following model for explaining The time of auctioning Anthuriums was rescheduled the price of an Anthurium at the auction: to an earlier time (6 am) and screen auctioning was introduced as a third clock in one of the auction halls, PRICE ¼ a þ b1 DIAM þ b2 WKDAY þ b3 VBN so now three concurrent auctions take place in that þ b4 QUANT þ b5 SCRAUC þ e: ð1Þ
    • ARTICLE IN PRESS O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx 5 Table 2 Process – stakeholder analysis of screen auctioning Exchange process Growers Auction Buyers Search No change No change No change Valuation Expected: possible higher prices Expected: no expectation with Expected: no expectation with because of earlier auctioning time regard to impact on prices regard to impact on prices Logistics Expected: more trading capacity Expected: auction hall logistics Expected: faster delivery of at auction hall would be eliminated allowing flowers due to by-passing for cheaper and more frequent auction hall transactions and new space for clocks Payments and No change No change No change settlements Authentication No change No change No change Communication No change Major change: digital representation No change and computing of product with standard image next to clock Product No change Expected: Generic digital representation Expected: Generic digital representation of each lot would represent the actual representation of each lot flower accurately enough could lead to less information on quality of flowers Legitimization No change No change No change Influence No change No change No change Dispute resolution No change No change No change Net benefits Positive Positive Neutral There are several factors that influence the Anthur- actions after February 13, 1996. This is the key ium price that we use as control variables in our explanatory variable. model. For Anthuriums, diameter of the flower Under screen auctioning, buyers lack two product (DIAM) is an important descriptive characteristic. characteristics compared to the physical representa- The day of the week (WKDAY) influences price as tion: the color and the stiffness of the flower, which well because different days of the week have struc- could be judged when the ‘raiser’ would show the turally different supply and demand characteristics. flower to the bidders. Particularly, the absence of the Similarly, the trade of Anthuriums (and flowers in stiffness cue is problematic, because stiffness is an general) is highly seasonally dependent. Therefore, important indicator of flower freshness, which in turn we corrected for this seasonal effect in the regression is an important determinant of a buyer’s willingness- by including the average Anthurium price at all other to-pay for that flower. This lack of freshness informa- flower auctions in Holland (VBN) as an extra vari- tion will lead buyers to expect a lower quality on able. Taking the average Anthurium price at the other flower auctions into account also corrects for any market-level phenomena that may influence the over- Table 3 all Anthurium price, as these should occur at the other Descriptive statistics auctions as well as the auction investigated here. The N Minimum Maximum Mean Standard quantity of the transaction (QUANT) is taken into deviation account because bidders are expected to bid different- VBN 154,074 80.40 239.90 145.50 43.38 ly for large or small quantities. We did not have any DIAM 152,583 1 99 11.51 2.85 QUANT 154,074 5 2300 76.70 2.83 prior expectations regarding the effects of these con- LOGQUANT 154,074 1.61 7.74 4.34 1.04 trol variables. The effect of screen auctioning in the PRICE 154,074 30 495 111.05 1.83 model is a dummy variable SCRAUC: 0 (without LOGPRICE 154,074 3.40 6.20 4.71 0.607 screen auctioning) for transactions before February Valid N 152,583 13, 1996; or 1 (with screen auctioning) for trans- (listwise)
    • ARTICLE IN PRESS 6 O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx Table 4 Cross-correlations DIAM SCRAUC VBN MON TUE WED THU FRI LOGQUANT DIAM 1.000 . SCRAUC 0.008 1.000 (0.002) . VBN 0.038 À 0.004 1.000 (0.000) (0.080) . MON À 0.010 0.007 0.020 1.000 (0.000) (0.003) (0.000) . TUE À 0.002 0.013 À 0.015 À 0.327 1.000 (0.496) (0.000) (0.000) (0.000) . WED 0.018 À 0.021 À 0.002 À 0.299 À 0.282 1.000 (0.000) (0.000) (0.424) (0.000) (0.000) . THU À 0.007 0.002 À 0.010 À 0.181 À 0.171 À 0.156 1.000 (0.004) (0.385) (0.000) (0.000) (0.000) (0.000) . FRI 0.000 À 0.003 0.004 À 0.309 À 0.291 À 0.266 À 0.161 1.000 (0.967) (0.302) (0.137) (0.000) (0.000) (0.000) (0.000) . LOGQUANT À 0.041 À 0.014 À 0.071 0.023 0.009 À 0.025 À 0.019 0.005 1.000 (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.058) . average for fear of purchasing a ‘lemon’ [1]. We normality assumptions. Descriptive statistics of the therefore have the following hypothesis: dependent and independent variables are given in Table 3 (dummy variables excluded). Cross-correla- Hypothesis 1. b5 < 0, i.e. screen auctioning will lead tion can be found in Table 4. to lower prices. The parameters of the model are estimated with We analyzed the most traded type of Anthurium— multiple regression analysis, using ordinary least the Anthurium Tropical. Approximately 98% of all squares (OLS). The model was tested for homosce- Anthurium Tropical flowers traded were of the highest dasticity and linearity by examining the residual plots. quality (quality grade A1), so we focused only on this The plots did not indicate any heteroscedasticity or quality grade and removed quality grades A2, B1 and non-linearity. Furthermore, the tolerance limits indi- B2 from the analysis. These operations resulted in a cate that there is hardly any collinearity present. Only remaining database of 154.074 transactions. the days of the week show some collinearity, which is We needed to take the natural log of the price and to be expected given the fact that one of them is the quantity. Both variables were very skewed to the redundant. The results are presented in Table 5, all the right; taking natural logs restores the validity of the results are significant at the 1% confidence level, Table 5 Regression results Unstandardized Standard Standardized t Sig. Tolerance coefficients B error coefficients b (Constant) 2.678 0.007 363.986 0.000 DIAM 0.0767 0.000 0.360 217.428 0.000 0.997 SCRAUC À 0.0216 0.002 À 0.017 À 10.358 0.000 0.999 VBN 9.11e À 03 0.000 0.651 392.207 0.000 0.993 TUE À 0.0195 0.003 À 0.014 À 6.806 0.000 0.683 WED À 0.0619 0.003 À 0.041 À 20.790 0.000 0.699 THU À 8.16e À 03 0.004 À 0.004 À 2.067 0.039 0.819 FRI À 0.0182 0.003 À 0.012 À 6.199 0.000 0.694 LOGQUANT À 0.0322 0.001 À 0.055 À 33.190 0.000 0.992 Dependent variable: LOGPRICE.
    • ARTICLE IN PRESS O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx 7 except for the Thursday dummy, which is significant subset of grower dummy variables. These grower at the 5% level. The model explained 58.3% of the dummy variables can be thought of as an indication combined variance in Anthurium Tropical prices. As for a grower’s reputation. The analysis yielded similar can be seen from Table 5, the SCRAUC dummy is results: a marginal increase in adjusted R2 without negative, meaning that Hypothesis 1 is accepted: any significant changes in the other coefficients screen auctioning did indeed lead to lower prices. compared to the original model. Therefore, although The magnitude of the effect can be inferred by taking there is a very small reputation effect, it was left out the exponent of b5, yielding an average price drop of of the model. about 2.1%. With a weighted average price of about The discussion above is essentially about modeling 1.21 guilders, this corresponds to almost 2.5 cents. decisions that could have explained an effect attribut- ed to screen auctioning. However, there are also rival explanations that could not be modeled, yet may 6. Discussion account for the screen auctioning effect. When screen auctioning was introduced, the product representation Some other factors are not in the model that may changed, but at the same time two other aspects of the also influence bidding behavior and price setting. It is auction process changed as well: the introduction of a likely that different buyers bid structurally different third auction clock in the auction hall and the earlier and therefore, dummy variables for the buyers should auctioning time. We will now discuss how these two be incorporated in the model as well. For instance, rival explanations may affect flower prices. most buyers buy ‘on order’, which means that they Several buyers complained about how difficult it have to get a certain amount of flowers that day was to keep track of three clocks at the same time. because of pre-orders from their customers. This will Most buyers at the flower auction act as agents for most likely cause them to bid higher on average. their customers, and they mainly buy ‘on order’. Similarly, there are some speculative buyers in the Hence, they do not want to risk not being able to market that are only likely to bid for flowers if they deliver the flowers their customers ordered. This can get them at a low price. This will most likely means that the increased cognitive complexity of the cause them to bid lower on average. This extension of bidding process and the accompanying increase in the model was considered but eventually decided uncertainty would lead to buyers stopping the auction against. This extended model was tested on a subset clock sooner, resulting in higher prices. of transactions with the 20 largest buyers, who There is no empirical evidence on the influence of accounted for almost 28% of all transactions. Intro- the time of day on bidding behavior, but flower duction of buyer dummy variables in this case only auctioneers told us that in their experience, earlier marginally raised the adjusted R2 and did not signif- auctioning times lead to higher prices. As these two icantly alter any of the coefficients of the variables potentially confounding factors both would have led compared to the original model without the buyer to higher prices and not the lower prices we observed, dummy variables. Since there are several hundreds of we conclude that the product representation factor is buyers, the introduction of dummy variables for all the most likely explanation for the price drop. buyers would add serious additional computational Finally, it is important to note that despite the price requirements, without any qualitatively significant reduction, the flower auction still considered screen changes in the result to be expected. So in the interest auctioning a moderate success. The auction receives a of parsimony, although it does explain a small part of percentage of the transaction value as a fee, so lower the variance, the buyer variable was left out of the prices imply a loss of transaction fees. However, model. according to auction personnel, this loss was compen- Similarly, one could argue that different sellers sated by the reduced costs of the internal logistical (i.e. the growers) might receive different prices on processes by an order of magnitude. As the auction is average for their flowers because of their reputation established as a cooperative of the growers, these for producing high (or low) quality flowers. Like the reduced costs indirectly compensate growers for the buyer’s case, we tested an extended model with a loss of direct revenue.
    • ARTICLE IN PRESS 8 O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx 7. Conclusions and implications system with four categories where growers self-report the quality of the flowers. Partly because of oppor- The paper presents results of empirical research on tunism, partly because of the few categories, as a the price impact of a new method of auctioning, using result 98% of the flowers are in the highest quality transaction data obtained at a large Dutch flower category. This effectively renders the quality rating auction. In February 1996, this auction introduced system useless. Note that this is also in accordance screen auctioning to separate the logistical processes with the results in [8], which identify Aucnet’s quality from the price discovery process, thus decreasing the rating system as a key success factor of the auction. costs and complexity of the total distribution process. Perhaps a good quality assurance process is the real In screen auctioning, the buyers are still present in the key to a successful electronic market. auction hall, but they are no longer shown the actual flowers. Instead, a generic picture is displayed on a Acknowledgements monitor next to the auction clock. Screen auctioning can be seen as an intermediate step towards a full The authors gratefully acknowledge the comments electronic market, since they both involve a shift from of two anonymous reviewers as well as the cooper- live product representation to image-based product ation from the flower auction. representation. The difference is that in screen auction- ing, contrary to an electronic market, the buyers still assemble physically in the auction hall. Despite the References proclaimed huge potential of electronic markets for [1] G. Akerlof, The market for ‘Lemons’: quality uncertainty and organizations nowadays and broad scientific research the market mechanism, Quarterly Journal of Economics, on this theme, controversy exists with regards to the (1970) 488 – 500. effects of electronic markets on governance structures [2] D. Bailey, M.C. Peterson, B.W. Brorsen, A comparison of and price mechanisms. Little is known about the video cattle auction and regional market prices, American consequences for the various participants. Some Journal of Agricultural Economics 73 (May) (1991) 465 – 475. [3] J.Y. Bakos, A strategic analysis of electronic marketplaces, authors predict lower prices due to lower search costs MIS Quarterly 15 (3) (1991) 295 – 310. [3], others find conflicting evidence from cattle auc- [4] E.K. Clemons, S.P. Reddi, M.C. Row, The impact of informa- tions and auctions for secondhand cars [2,8]. We con- tion technology on the organization of economic activity: the tributed to this debate by analyzing an extensive ‘‘move to the middle’’ hypothesis, Journal of Management Information Systems 10 (2) (1993) 9 – 36. database of flower transactions. A multiple regression [5] C.P. Holland, A.G. Lockett, Mixed-mode network structures: model was formulated to assess the impact of the the strategic use of electronic communication by organiza- introduction of screen auctioning on the price forma- tions, Organization Science 8 (5) (1997) 475 – 488. tion process and a significant price drop of about 2.1% [6] A. Kambil, E. van Heck, Re-engineering the Dutch flower was found. There are several factors that play a role in auctions: a framework for analyzing exchange organizations, determining the price effects of screen auctioning. The Information Systems Research 9 (1) (1998) 1 – 19. [7] S. Klein, Introduction to electronic auctions, International introduction of the third clock and earlier time of auc- Journal of Electronic Markets 7 (4) (1997) 3 – 6. tioning would both lead to higher prices ceteris paribus. [8] H.G. Lee, Do electronic marketplaces lower the price of Therefore we are led to conclude that the deficient goods? Communications of the ACM 41 (1) (1998) 73 – 80. informational quality of the on-screen picture in com- [9] T.W. Malone, J. Yates, R.I. Benjamin, Electronic markets and parison to the real flower is the cause of the price drop. electronic hierarchies, Communications of the ACM 30 (6) (1987) 484 – 497. Although screen auctioning is not a pure electronic [10] E. van Heck, P.M. Ribbers, Experiences with electronic auc- market, the results indicate that a decrease in price tions in the Dutch flower industry, International Journal of level should be expected when goods are traded and Electronic Markets 7 (4) (1997) 29 – 34. sold electronically, unless ways are found to counter [11] E. van Heck, P.M. Ribbers, Introducing electronic auction the information-quality problem. For example, one systems in the Dutch flower industry—a comparison of two initiatives, Wirtschaftsinformatik 40 (3) (1998) 223 – 231. factor that may have aggravated the negative results of [12] E. van Heck, P. Vervest, How should CIOs deal with web- screen auctioning for the auction is the lack of a good based auctions? Communications of the ACM 41 (7) (1998) quality rating system. The auction operates a quality 99 – 100.
    • ARTICLE IN PRESS O.R. Koppius et al. / Decision Support Systems xx (2003) xxx–xxx 9 Otto Koppius is an assistant professor at Matthijs Wolters is an assistant professor the Rotterdam School of Management at in the Department of Management and Erasmus University Rotterdam. He holds Organization at the Faculty of Economics an MSc in Applied Mathematics from the and Business Administration, Free Univer- University of Twente and a PhD in Busi- sity Amsterdam. He holds an MSc in ness Administration from Erasmus Univer- econometrics from the University of Gro- sity Rotterdam. His research interests ningen, the Netherlands, specializing in include electronic markets and auctions, operations research and statistics and a network analysis and decision theory. On PhD in information systems from the Rot- these topics he has authored various con- terdam School of Management at Erasmus ference papers including HICSS, WISE, University Rotterdam. His thesis investi- INFORMS, ECIS and the Academy of Management. He has been gates the impact of Information and Communication Technology a visiting researcher at the University of Michigan and at the IBM (ICT) in combination with modularity on supply chains of organ- T.J. Watson Research Center. His dissertation won the Best Doctoral izations. His research focus is on how organizations, by using these Dissertation Award at ICIS 2002. technologies and concepts, can increase their service towards customers by becoming more flexible and responsive. On these Eric van Heck is a Professor at the De- topics he has authored several conference papers and articles in partment of Decision and Information Sci- edited books. ences, Rotterdam School of Management, Erasmus University Rotterdam. His main research interests are EDI Systems, ICT- enabled Business Network Redesign, and Electronic Markets. He is a specialist in Electronic Auctions. He is (co-)author/edi- tor of 12 books, the latest of which is Making Markets: How Firms Can Design and Profit from Online Auctions and Exchanges (co-authored with Ajit Kambil), published by Harvard Business School Press. His articles have been published in journals such as California Management Review, Communications of the ACM, Harvard Business Review, Information Systems Research, and WirtschaftsInformatik. Previously, he worked for Cap Gemini Nederland, Wageningen University, and Tilburg University. He was a visiting scholar at the Leonard N. Stern School of Business at New York University. He received his MSc and PhD from Wageningen University.