Analysis and design of business-to-consumer online auctions

Analysis and Design of Business-to-Consumer Online Auctions

Ravi Bapna?Paulo Goes?Alok Gupta

Department of Operations and Information Management,U-41IM,School of Business,

University of Connecticut,Storrs,Connecticut06269

Department of Operations and Information Management,U-41IM,School of Business,

University of Connecticut,Storrs,Connecticut06269

Information and Decision Sciences Department,3-365Carlson School of Management,University of Minnesota,

32119th Avenue South,Minneapolis,Minnesota55455

bapna@https://www.wendangku.net/doc/ba2827009.html,?paulo@https://www.wendangku.net/doc/ba2827009.html,?alokgupta@https://www.wendangku.net/doc/ba2827009.html,

B usiness-to-consumer online auctions form an important element in the portfolio of mer-

cantile processes that facilitate electronic commerce activity.Much of traditional auction theory has focused on analyzing single-item auctions in isolation from the market context

in which they take place.We demonstrate the weakness of such approaches in online set-

tings where a majority of auctions are multiunit in nature.Rather than pursuing a classical

approach and assuming knowledge of the distribution of consumers’valuations,we empha-

size the largely ignored discrete and sequential nature of such auctions.We derive a general

expression that characterizes the multiple equilibria that can arise in such auctions and seg-

regate these into desirable and undesirable categories.Our analytical and empirical results,

obtained by tracking real-world online auctions,indicate that bid increment is an important

factor amongst the control factors that online auctioneers can manipulate and control.We

show that consumer bidding strategies in such auctions are not uniform and that the level of

bid increment chosen in?uences them.With a motive of providing concrete strategic direc-

tions to online auctioneers,we derive an absolute upper bound for the bid increment.Based

on the theoretical upper bound we propose a heuristic decision rule for setting the bid incre-

ment.Empirical evidence lends support to the hypothesis that setting a bid increment higher

than that suggested by the heuristic decision rule has a negative impact on the auctioneer’s

revenue.

(Online Auctions;Dynamic Pricing;Pricing Mechanisms)

1.Introduction

Business-to-consumer B2C online auctions are heat-ing up as an ef?cient and?exible sales channel. Along with the other two types of online auctions, namely consumer-to-consumer C2C and business-to-business B2B auctions,they represent a new class of mercantile processes that are ushering in the net-worked economy,but are not fully understood yet. Van Heck and Vervest(1998)have called for an exten-sive examination of the pervasive impact of advance electronic communication on the theory and practice of auctions.For a wide variety of goods sold over the Internet,consumers now have an interesting choice of mercantile processes to utilize to buy these goods. Broadly,these different processes can be broken into static posted-price mechanisms and dynamic interac-tive pricing mechanisms such as online auctions.In this context,online auctioneers are striving to?nd the correct strategic and tactical direction that ensures their long-term pro?tability.This involves optimizing

existing as well as designing interesting new mer-cantile processes to sell their products.This paper is an attempt to analyze this emerging market structure with respect to the welfare of the different economic agents involved,namely the B2C auctioneers(we implicitly assume the joint interest of the seller and the auctioneer,if they are different)and consumers. There already exists a vast body of literature dealing with the theory of auctions and optimal mechanism design(see McAfee and McMillan1987, Milgrom1989,and Myerson1981for a review).How-ever,the majority of the work focuses on the analysis of single auctions and is carried out in isolation from the broader context of the markets in which these auctions take place(Rothkopf and Harstad1994a).It should also be noted that despite the presence of a vast body of literature dealing with traditional auc-tions,one could not call these auctions’mainstream’from a consumer’s viewpoint.It is only after the syn-ergetic combination of traditional auctions with Inter-net technology that this sphere of economic activity has blossomed into signi?cance.

This paper contributes to the literature by enhanc-ing the conceptualization of the traditional theory of auction-based shopping in the emerging online con-text.Speci?cally,we contribute to the literature by addressing four questions that have not yet been resolved by researchers or practitioners:

1.Without making any distributional assumptions regarding the consumers’valuations,can we derive a structural characterization of the revenue curve of B2C online auctions,and can we derive bounds on the revenue?

2.In the presence of a multitude of decision vari-ables that can serve as control factors for online auc-tioneers,which are the ones that have signi?cant impact on revenue?

3.Are consumers’bidding strategies uniform,or can we identify heterogeneous bidding strategies pur-sued by consumers having different risk pro?les? Are there any interactions between consumer bidding strategies and key control factors of auctioneers?

4.Assuming a partial knowledge of the upper tail of the distribution of the consumers’valuations,can we prescribe a concrete and practical strategy to auc-tioneers that allows them to leverage the insights obtained from addressing the above two research questions?What are the cost implications of obtaining the distributional information mentioned above,and is it feasible?

There are a number of parameters,or control fac-tors,that auctioneers can set prior to the commence-ment of the auction.These can potentially in?uence the eventual revenue realization from the auction.The current practice of B2C online auctions reveals to us that control factors are the opening bid,the bid incre-ment,and the lot size.Other candidate control fac-tors can be the auction ending time,the time span of the auction,and the hidden reserve price.The end-ing time is not interesting because a close examination of such auctions indicates to us that all auctioneers employ a going,going,and gone period after the pas-sage of the announced closing time.Thus,the actual closing happens when no new bids are received for a predetermined time interval after the closing time. The time span of the auction is an interesting con-trol factor and its impact is modeled in our work through the opening bid,as detailed later.Another potential control factor that auctioneers could use is a hidden reserve price,below which they will not be willing to sell the goods.While observed in consumer-to-consumer auctions on eBay,B2C auctioneers such as Onsale do not use this construct.Rather,they uti-lize a low opening bid as a strategic marketing tool to attract Web traf?c(Bajari and Hortascu2000).It should be noted that the risk characteristics of the sellers(individual consumers as opposed to corpo-rations)and the nature of goods being auctioned on eBay are signi?cantly different from B2C auctions.

In this paper we do not address questions such as,“should an auctioneer sell all50units in a single auc-tion,or in10auctions of5units each?”While inter-esting,this issue is beyond the scope of this study. Its analysis requires a different and more restrictive set of assumptions(Beam et al.1999)which includes knowledge of the holding costs due to technological obsolescence and knowledge of population character-istics that goes beyond the bidding strategies we will be discussing.In this study we assume that lot-sizing decisions are simplistically made with the objective of clearing aging inventory fast.

To answer the?rst question,we develop a stylized theoretical model that analytically characterizes the structure of the most popular type of B2C auctions and develops a range of expected revenue for auc-tioneers.We do this without making any assumptions regarding the distribution of individuals’valuations. Subsequently,using empirical evidence collected from monitoring real-world online auctions,we identify the key decision variables that serve as control fac-tors for auctioneers who are attempting to maximize their revenue.In identifying these factors we note that many of the elegant and powerful theorems and some of the typical assumptions made in the clas-sical analysis of auctions do not hold in the emerg-ing online context.For instance,we observe that all B2C online auctions set a discrete bid increment and our analysis suggests that this is an important decision variable that impacts auctioneers’revenue. This is in stark contrast to the standard auction the-ory assumption that the amount bid is a contin-uous variable.There has been very little research done on the impact of discrete bid levels in auctions. Yamey(1972)and Rothkopf and Harstad(1994b) are the only researchers who have dealt with the somewhat related issue of analyzing auctions from a more decision-theoretic perspective rather than a game-theoretic perspective.However,their analysis deals with single-item auctions.This research is an attempt to reignite this neglected area of auction theory research by shifting the focus from examin-ing only the limiting behavior towards acknowledg-ing the discrete and sequential nature of the auction process.

Our empirical analysis helps us identify three prominent bidding strategies pursued by consumers. We compare the relative performance of these strate-gies with respect to the surplus they provide to their adopters and also examine their impact on the rev-enue generation process.

Lastly,with an objective of providing concrete directions to auctioneers regarding the design of B2C auction mechanisms,we discuss tactical issues regarding the setting of the bid increment and present an upper bound beyond which signi?cant loss of rev-enue could arise.The validity of this result is tested against observed strategies pursued by auctioneers.The empirical evidence shows a negative impact on revenue when the bid increment set by auctioneers exceeds the upper bound.

The core stylized theoretical model is presented in §2.In§3,we present our empirical?ndings,which help us identify the key decision variables for auction-eers and also the different consumer bidding strate-gies.Section4presents an approach,along with its empirical validations,towards designing auctions with near-optimal bid increments.Section5concludes this paper.

2.Theoretical Model

A vast majority of existing research focuses on auc-tions of a single item,whereas most of the B2C auc-tions conducted on the Web sell multiple identical units of an item using a mechanism analogous to,but not the same as,the?rst price,ascending,English auction.We call such auctions“Multiple Unit Pro-gressive Electronic Auctions(MUPEA)”.Bidders may purchase more than one unit,but all units must be demanded at the same price.Thus,demand reduction that can occur when bidders are allowed to present a demand curve(Ausubel1997)is not an issue.It is well known(Rothkopf and Harstad1994a)that single-item results do not carry over in multiple-unit settings.In addition,researchers using the classi-cal game-theoretic approach to model auctions often rely on making distributional assumptions regarding individuals’valuations to stochastically derive equi-librium conditions.We demonstrate the inappropri-ateness of such an approach in multiunit settings.In contrast,our approach focuses on the combinatorial dynamics of the penultimate rounds that determine which of the multiple equilibrium points is attained in a given instance.

While most of existing theory analyzes auctions under either the private or the common value set-ting,the online context in which these B2C auctions take place makes such a strict classi?cation inaccu-rate.The empirical evidence indicates that most of the items have both idiosyncratic(private)and com-mon value elements.This is more true given the pres-ence of imperfect substitutes and price comparison agents that provide information regarding the alter-native comparable products and their posted prices.

Thus,based on the general model of Milgrom and Weber(1982),the multiunit B2C auctions lie in the continuum between the private and common value models.

The presence of price comparison agents creates a mass of consumer valuation at or around the pre-vailing market price.Consequently,we would expect that in online auctions such bid levels would be real-ized towards the end of the auctions,rather than in the beginning or intermediate stages.This forms the motivation behind our attention to the combina-torial dynamics of the penultimate auction rounds. Next,we introduce some basic notation to describe our model.

Let there be N items to be auctioned and let there be I bidders,each with a value V i i=1 I,for the product.Let B i denote the current bid of consumer i. As mentioned earlier,all MUPEA have a minimum bid increment that we denote by k.Ignoring monitor-ing costs for the present,we assume that customers maximize their net value and hence always bid at the current asking price,provided that the current ask-ing price does not exceed their valuation of the item. Rothkopf and Harstad(1994b)characterize this as the pedestrian approach to https://www.wendangku.net/doc/ba2827009.html,ter in the paper we relax these assumptions and analyze the impact of alternative bidding strategies.When there are N bids at the same level,the asking price is incremented by k. Let B m be the highest bid that does not win the auction.In general,there can be several bidders who bid B m,but fail to win the auction.We de?ne the marginal consumer as the?rst person to bid B m,and who failed to win the auction.Let the value of the marginal consumer be denoted by V.Observe that no a priori knowledge is required about the marginal consumer’s exact valuation.We postulate that there will exist such a consumer whose position in the dis-tribution space of valuations,along with the temporal position of her bid,will determine that no further bids can be placed.This creates a partition of the distribu-tion space around V which,along with the temporal position of the marginal bid,creates the multiple equi-libria in such auctions.It is important to note that B m de?nes V and not vice versa.

It is clear from the bidding process that at the end of the auction there are only two possible bid values that are in the winning list.Let us denote them by B m and B m+k.By de?nition of the marginal customer,B m is the highest bid the marginal consumer can potentially offer,and B m≤V

We partition the set of bidders who are involved in the?nal bidding sequences into a set L of consumers who were able to bid at the level B m but failed to win the auction,and a set W of auction winners.Also,let the cardinality of the set L be M that is L =M.By construction, W =N.

We now characterize the structure of the winning bids in terms of the temporal position of the marginal consumer’s?nal bid B m and the bid increment k.Let j correspond with the temporal order of the marginal consumer’s bid.The case with j=N+1represents the case in which the marginal consumer never gets the chance to bid at the level B m,and all winners bid at this level.

Proposition1.If the marginal consumer is the j th person to bid at the level B m j=1 N+1,the win-ning bids of MUPEA have the following structure:B1m B2m B j?1m B m+k 1 B m+k 2 B m+k N? j?1 . Proof.Let the marginal consumer be the j th per-son to bid B m.Then,there were j?1 bidders from the set W who bid at the level B m before the marginal consumer.By de?nition of the set L,there are M?1 other bidders who bid their highest possible bid B m. Note that by construction M≤N? j?1 .

This implies that an additional N?j ? M?1 bid-ders from set W bid at the level B m.However,this leaves N? N?j?M+1+j?1 =M consumers from set W who cannot bid at the level B m.These con-sumers will bid at the level(B m+k).

Consider the?rst consumer who bids at the level (B m+k).He will displace either a consumer from the set L,or from N?j ? M?1 consumers from the set W who were able to bid B m.If the consumer displaced from the winners’list at the level B m was from the set W,then note that there will still be M consumers from the set W that have to bid at the level(B m+k) to enter the winners list.

However,if the consumer displaced was from the set L,then the remaining consumers who have to bid (B m+k)goes down by one to M?1.

This process will be repeated at every bid at level (B m+k).Note that because all the consumers from the set L have to be displaced,there will be M+ N?j ? M?1 =N? j?1 bids at the level(B m+k .Thus, there will be(j?1 bids at the level B m and N? j?1 bids at the level(B m+k in the winning bid struc-ture.1Thus,the winning bids under MUPEA have the structure:B1m B2m B j?1m B m+k 1 B m+k 2 B m+k N? j?1 .

Corollary1a.The revenue for MUPEA can be rep-resented by:

revenue=NB m+kb (1) where b=N? j?1 is the number of bids at the high level B m+k.

Corollary1b.The presence of N consumers having valuations≥B m does not guarantee that B m will be the marginal bid.

Proof.This is the case where b=0and j=N+1. The marginal customer never gets the chance to bid at the level B m,and the marginal bid then becomes B m?k .

From a managerial perspective,Proposition1indi-cates that unlike in single-item auctions,revenue from multiunit auctions can materialize in multiple ways depending on the value of j Each of the above-de?ned structure of the winning bids corresponds to a different equilibrium point.These various equilib-rium points will differ in the revenue they generate, as seen in Corollary1a,where b can vary from0to N. Rothkopf and Harstad(1994b)show that in single-item auctions(N=1)there are three mutually exclusive and exhaustive cases in which the?nal bid structure can materialize.In what follows we demon-strate that the number of possible cases leading to the various equilibrium points of Proposition1grows exponentially with N.

1Because the actual closing happens when no new bids are received for a predetermined time interval after the closing time,it implies that the marginal consumer has the opportunity to place her?nal bid,and consumers in general have the opportunity to bid up to their valuation(V i),if necessary.

Corollary1c.The number of mutua yexc usive and exhaustive cases in which the winning bid structure can form grows at an exponential rate with N.

Proof.Let n be the total number of customers hav-ing V i>B m.From Corollary1b we need n≥N+1to guarantee the equilibrium points of Proposition1at levels B m and B m+k.Consider the special case where n=N+1.

For each temporal position j=1 N+1that the marginal customer places his?nal bid,there are N! possible sequences of the other N bidders.Hence, all the equilibrium points described in Proposition 1can be achieved through(N+1)!possible bidding sequences.

Proposition1and its corollaries indicate that there can be multiple equilibrium points for these kinds of auctions,some of which are more desirable than others in terms of generated revenue.The exponen-tial growth described in Corollary1c once again highlights the inappropriateness of pursuing a more classical probabilistic approach towards optimizing expected revenue of auctioneers.The mathematical intractability of deriving an expected revenue func-tion,and subsequently optimizing it,becomes evident even for the most trivial distributions as N grows2. Instead,we utilize the structure of the winning bids to derive the bounds of total revenue for auctioneers and focus our attention on identifying key control factors.

2.1.Bounds on the Revenue

It should be noted that auctioneers have the privi-lege of setting an opening bid,similar to a reservation price,below which they do not wish to sell the goods. Let r denote the opening bid and let the“%”operator return the remainder of the division.

De?ne = V?r %k (2)

observe that B m=V? (3) In Equation(2), denotes a segment of the bid increment k that measures the distance between the marginal consumer’s valuation V and the nearest 2With N=17(the average lot size of of the90auctions we tracked),the number of mutually exclusive and exhaustive cases is 6,402,373,705,728,000.

lower feasible bid.For instance,if V =$80 r =$8,and k =$5,then the nearest feasible bid to V is $78,which implies that =2.

We assume that in the presence of other ?xed-price alternatives consumers choose to participate in such auctions with an objective of maximizing their net-worth.In the extreme we assume that a rational,net worth-maximizing consumer with valuation V i will be willing to bid his or her valuation.Bidding any higher would imply a nonpositive surplus with cer-tainty.In practice,this extreme bid will be determined jointly by the bid increment k and the auction open-ing bid r .We can now de?ne the bounds on an auctioneer’s total revenue in terms of the marginal consumer’s valuation V ,the bid increment k ,and .Proposition 2.Let V be the marginal consumer’s valuation,and be a segment of the bid increment k that measures the distance between the marginal consumer’s valuation V and the nearest lower feasible bid.Then the lower bound and the upper bound on the revenue of a seller selling multiple units under MUPEA are,respec-tively,N V ? and N V ? +k .

Proof.The lower bound follows from Corollary 1b and Equation (3).The upper bound occurs when the marginal customer is the ?rst position to bid (V ? =B m ).Following Proposition 1,it is easy to verify that by the de?nition of marginal customer there will be N bids placed at (V ? +k .

Corollary 2a.The upper bound is feasible provided there is a nonzero probabi l

itythat N or more individuals have valuations in excess of (V ? +k ).

Corollary 2b.The range of revenues under MUPEA is N ?k .

From a managerial perspective,an understanding of the revenue bounds is important as it gives the auc-tioneers incentive to design the auction parameters in a fashion that increases the likelihood of realizing the higher-revenue bid structures.

Example 1below illustrates the sequential progres-sion of bids that in?uence the derivation of these bounds.Subsequently,we deal with issues related to manipulating the bid increment,the range of auction-eer’s revenues,and the feasibility of the upper bound.

Example 1.Consider the following hypothetical scenario.Let N =3,k =$5,and r =$9.Let there be four bidders,say A,B,C,D,with true valuations of $100,$105,$106,$107,respectively.Let A be the marginal consumer.Observe that =1.

?The MUPEA lower bound occurs if we observe the following sequence of progressive bids:D(89)–C(89)–B(89)–A(94[(V ?k ? ])–B(94)–C(94)–D(99)–C(99)–B(99)–STOP .At this stage,A,the marginal consumer will have to bid $104to get in now,which he will not because his valuation is $100.Thus,the total revenue =$297.

?The MUPEA upper bound occurs if we observe the following sequence of progressive bids:B(94)–C(94)–D(94)–A(99[V ? ])–D(99)–C(99)–B(104)–C(104)–D(104)–STOP .Observe that A,the marginal consumer is the ?rst to get into the winners list at [V ? ]),and hence the last to get out at that level.At the terminal stage A would have to bid $104to get in now,which he will not because his valuation is $100.Thus,the total revenue =$312.

Next,we consider the impact on the range of total revenue of manipulating the bid increment k .Con-sider the difference between bid increment k and k where ∈ +.

Proposition 3.Anychange in the bid increment bya factor induces a proportional change in the range of total revenue.

Proof.The range of total revenue with bid incre-ment k is

N V ? V ?r %k +k

? N V ? V ?r %k

=Nk

With bid increment l the range of total revenue is

N V ? V ?r % k + k

? N V ? V ?r % k

= Nk

Thus,any change in the bid increment by a factor introduces an equal and corresponding change in the range of total revenue for the auctioneer.

The above proposition implies that auctioneers can strategically use the bid increment to control the level of uncertainty regarding the total revenue.As can be

seen from the above results,the bid increment also plays an important role in the revenue realization pro-cess.It should also be mentioned that the importance of setting an optimal bid increment gets magni?ed in multiple-unit settings as any misjudgment on the part of auctioneers could have N -fold consequences.Example 2illustrates a scenario where keeping every-thing else constant,the bid increment is manipu-lated to have a telling effect on the auctioneer’s revenue.

Example 2.Consider an auction with N =3and r =$102.Individual valuations are ranked as fol-lows:$110,$120,$120,$125,$125,$139$139,$139,$143,$143,$143.Let the individuals be labeled A B C I J K in increasing order of their valua-tions.Consider the following bid sequence:A(102),B(102),C(102),F(117),G(117),C(117),I(132),J(132),K(132).In this case the marginal consumer is F with a valuation of $139.

Case 1(k =$15).The top half of Figure 1shows the possible bid levels and the position of the marginal consumer at $139.Observe that =139?132=7,and the lower bound occurs at N V ? =$396.The upper bound at N V ? +k would imply that three con-sumers would have to bid $147,which is infeasible

Figure 1

Revenue Impact of Bid Increment

143143

Marginal Consumer

Upper Bound = Lower Bound = $132 x 3 =$396Range = 0

Upper Bound = $142 x 3 =$426Lower Bound = $122 x 3 = $366Range = 60

CASE 1: k = $15CASE 2: k = $20as no consumers have valuations above $143.Hence,the upper and lower bound are the same and the range =0.

Case 2(k =$20).The bottom half of Figure 1shows the possible bid levels for this case.Observe that =139?122=17,and the lower bound occurs at N V ? =$366.In contrast to Case 1,the bid lev-els corresponding to k =20facilitate the possibility of capturing the highest valuations leading to an upper bound of $426.This can be viewed as a reduction in the economic inef?ciency of the auction as it reduces the gap between the price paid by the auction win-ners and their valuations.Additionally,in contrast to the case with k =$15,the switch to k =$20at least makes it feasible for the auctioneer to come close to capturing the highest valuations at $143.

In the above discussion we have taken a decision-theoretic approach towards structurally characteriz-ing multiunit B 2C online auctions.Without resorting to any distributional assumptions and without focus-ing on the limiting behavior of individuals,we explain the discrete and sequential aspects of such an auction in terms of the marginal consumer’s val-uation,the bid increment,and the auction starting point.

2.2.Consumer Bidding Strategies

The analysis based on Propositions1–3assumes that consumers behave rationally and participate in such auctions with a view of maximizing their net worth.This implies that all consumers employ the same strategy while bidding.Such a strategy involves active participation(manual or using a programmed agent)that bids the minimum required bid at any stage during the auction.The theoretical basis for adopting this approach can be found in Rothkopf and Harstad’s(1994b)discussion of what is called the pedestrian approach.In adopting this strategy,bidders choose to be no more aggressive than necessary to continue competing.While such behavior,termed as participatory by us,is exhibited in reality,it is not the only kind of strategy employed by bidders.

A broader and more basic question in categorizing online bidders is whether we can capture some con-sistent“trait(s)”that would allow us to develop a for-mal typing methodology.We rely our segmentation on the basis of the theory of risk preferences(Slovic 1972)of consumers and on bounded rationality.Addi-tionally,as recommended by Messinger(1995),we designed a segmentation scheme that is observable(our automatic agent tracks all incoming bids),managerially relevant(it impacts auctioneers’revenue),and leads to clear distinctions between segments.Our empirical investigation identi?ed three distinct bidding strate-gies that segmented consumers(Bapna et al.2000). Section3.1speci?cally describes how the categories were identi?ed from the data.

Evaluators think that they know the true market value of the object they are interested in and try to bid that amount early to get ahead in the win-ners’list.However,due to bounded rationality,or lack of awareness of alternative information sources such as price-comparison agents,or by choosing not to incur the cost of monitoring the auction,they run the risk of bidding more than required to win a given auction.Their bidding higher than the mini-mum required amount also indicates their desire to minimize the uncertainty of being priced out of the auction,which amounts to a risk-aversion premium that should be expected to be paid.Participators,on the other hand,never bid any higher than the cur-rent minimum required bid and can be thought of as having a low monitoring cost,or perhaps even added “gaming”utility from the process of being a part of the auction.Bucklin and Lattin(1991)?rst intro-duced the notion of opportunistic shopping behavior to account for the fact that households do not run out of all products simultaneously.Such consumers can be categorized to be risk seeking because they wait until the last moments to enter into an auction,and they buy when they see a bargain.(See Table1.) The theoretical impact of utilizing just the partic-ipatory bidding strategy in Propositions1–3is that it leads to conservative estimates of the auctioneer’s revenue.It also implies that the auctioneer’s rev-enue is a nondecreasing function of the proportion of bidders using other strategies than the participatory strategy.This is formalized in Proposition4and tested empirically in the next section.

Proposition4.An increase in the number of bidders in the winning set who adopt a nonparticipatorybidding strategyhas a nondecreasing effect on the auctioneer’s revenue.

Proof.Observe that both opportunists and evalu-ators have to pay at least the minimum winning bid to win the auction.Participators,by de?nition,will Table1Consumer Segmentation Based on Bidding Strategy Evaluators Early one-time high bidders who have a clear idea of

their valuation

Bids are,usually,signi?cantly greater than

the minimum required bid at that time

Rare in traditional auction settings—high?xed cost of

making a single bid

Risk-averse

Bounded rationality

High monitoring costs

Participators Risk-neutral

Derive some utility(incur a time cost)from the process

of participating in the auction itself

Make a low initial bid equal to the minimum required bid

Progressively monitor the progress of the auction and

make ascending bids,never bidding higher than the

minimum required Low monitoring costs Opportunists Risk-seeking

Bargain hunters

Place minimum required bids just before the auction

closes(operationalized as the87.5–100th percentile

of the duration of the auction)

pay no higher than the minimum required bid.Thus, the revenue contribution of one additional participa-tor,who will replace an opportunist or an evaluator in the winner’s list,will be dominated by the contri-bution of the nonparticipatory bidder she displaces.It follows from induction that an increase in the num-ber of bidders adopting a nonparticipatory bidding strategy has a nondecreasing effect on the auction-eer’s revenue.

In the next section we examine whether our analyt-ical?ndings can be validated empirically.In particu-lar,we wish to determine which amongst the many decision variables that auctioneers can control have a signi?cant impact on their revenue,and what is the impact of the three different consumer bidding strategies on the welfare of the auctioneer and the consumers.

3.Empirical Investigation Propositions1–4and their associated corollaries have testable implications.They suggest that the structural characteristics and corresponding auctioneer’s rev-enue of MUPEA can largely be explained in terms of the marginal consumer’s valuation,the bid increment, and the auction starting point.Other researchers (Vakrat and Seidmann1999,Beam et al.1999)have suggested that the key revenue-impacting control fac-tors for auctioneers are the lot size and the time-span of auctions.To empirically test this dichotomy of opinion,we devised a data collection scheme that undertook a round-the-clock monitoring of100 real-world online auctions conducted by a popular website.3

3.1.Data

An automatic agent was programmed to capture, directly from the website,the HTML document con-taining a particular auction’s product description, minimum required bid,lot size,and current high bid-ders,at frequent intervals of15minutes.A parsing module developed in Visual Basic was utilized to con-dense all the information pertinent to a single auction,

3Popularity was based on https://www.wendangku.net/doc/ba2827009.html, ratings.including all the submitted bids,into a single spread-sheet.Of the100auctions tracked,90survived the screening process which was designed to ensure(a) that there was no sampling loss(due to occasional server breakdowns),and(b)that there was suf?cient interest in the auction itself,given that some auctions did not attract any bidders.Data collection lasted over a period of six months so as to ensure a large-enough sample size(>20)for each of the three levels of bid increment chosen($5,$10,and$20).

The consumer segmentation comes straight from mining the data.As we have HTML snapshots of the winning bids at intervals of?ve minutes,we can parse the HTML and observe whether a person placed a single bid(an evaluator),or how many times a per-son bid,where he started and stopped,and what was the earliest time he came in(to identify opportunists).

3.1.1.Bias in the Data.While we have been thor-ough with the data collection process,we do realize that there could still be potential sources for bias in the data.One such source could be related to the bid-ding strategies described in§2.2.What if bidders were not pure participators?We tagged a bidder as a par-ticipator if we recorded a bid at a low level in the early phases of an auction and subsequently recorded a progression of higher bids.However,this assumes that the bidders never jump bid within this strategy, and always stuck to bidding the minimum required bid.Another possible source of bias could arise from the use of the automated bidding agents(whom we treat as participators)provided by the auctioneers. While most of these do in fact behave like partici-pators,it is beyond the scope of this study to com-prehensively model their adoption and its consequent impact.

3.2.Measures and Model Speci?cation

We choose as our dependent variable the Average Offset Revenue(AOR)of an auction.The offsetting mechanism is designed to come up with a revenue metric that is independent of the magnitude of the auction(magnitude is de?ned as the average dollar value of the winning bids),and of other independent variables.The motivation for this metric comes from Proposition3.Note that for a given k the difference

in minimum and maximum revenue depends only on the lot size (N and k .Thus,to compare revenues from auctions with two different k values it is suf?-cient to normalize the revenue per customer and sub-tract the minimum winning bid (V ? ).Thus,we shift each winning bid to the left by an amount equal to B l ,where B l is the lowest winning bid for that auction and then divide the total revenue by each auction’s lot size to get AOR.Mathematically,

AOR =1N N

i =1 B i

?B l

where B i represents the i th winning bid.It is trivial to see that AOR is constructed to be independent of the magnitude of the value of an item.The AOR met-ric ensures that for the same bid increment and bid structure,the AOR would be identical,irrespective of the magnitude.

3.2.1.Test Hypotheses.Our independent vari-ables represent the three control factors that auction-eers can manipulate and the percentage of bidders adopting the participatory bidding strategy,denoted by %P .Recall that control factors are the bid incre-ment k ,the lot size N ,and the auction reserve price r .While the impact of %P is hypothesized in Proposi-tion 4,we wish to test the following three hypotheses through the regression model.

Table 2

Results from Multivariate Linear Regression

Regression statistics

R 2

0 223072676Observations 90

ANOVA

df

SS

MS F Signi?cance F Regression 43500 091249875 02286 101336

0 000229907

Residual 8512190 27171143 415Total

89

15690 36296Coef?cients

Standard Error t Stat P -value Intercept 3 4410411193 5415249260 9716270 333993k 0 9854557520 2300358234 2839234 81E-05N ?0 0830566130 075669383?1 097630 275468r ?0 002463360 008646295?0 28490 776411%P

0 218526659

5 632231882

0 038799

0 969141

Hypothesis 1.The bid increment has a signi?cant impact on the average offset revenue.

Hypothesis 2.Lowering the opening bid has a positive impact on the average offset revenue.

We expect to see a larger number of total number of bidders in auctions with low opening bids,and the resulting increased competition should have a posi-tive impact on the auctioneers’revenue.

Hypothesis 3.An increase in the lot size has a nega-tive impact on the average offset revenue.

With larger lot sizes the likelihood of having a larger number of bidders at the lower feasible win-ning bids increases,and hence our prior is that it will result in a lower revenue per bidder.We test for signi?cance the model

AOR = 0+ 1k + 2N + 3r + 4%P +

(4)

and get the following results.Table 2indicates that on the basis of risk of 0.05,the overall regression model is a good predictor;the F value of 6.01is greater than the tabulated F (4,86,0.95).The R 2value obtained is 22.30%indicating that perhaps not all of the variation is explained in terms of these independent variables.3.3.Findings

Importantly,amongst the independent variables only k ,the bid increment,is signi?cant in explaining the variation of the average offset revenue.Neither the

lot size N ,the reserve price r ,nor the percentage of participators is signi?cant.This validates our theoret-ical arguments,which highlights the importance of the bid increment as an important decision variable impacting auctioneers’revenues.

However,given the low R 2value,a residual anal-ysis of the model presented above revealed to us that the assumption of equal error variances,or homoskedasticity,was violated (the Chi-Square test statistic for the SSR,Harvey,and Gjeser tests was high and the p -values signi?cant at the 5%level).We searched for a model with a better ?t.Given that the bid increments chosen by the online auctioneers had a logarithmic pattern to them ($5,$10,$20),our intu-ition led us to employ the log transformation to the dependent variable.This model is speci?ed by ln AOR = 0+ 1k + 2N + 3r + 4%P +

(5)

This model has a better ?t,as indicated by the R 2value,which jumped from 22%to 40.6%,and the het-eroskedasticity tests,which were all insigni?cant.In addition,the results consistently indicate the signi?-cance of the bid increment and the lack of signi?cance of the other two controllable factors.Higher-order interaction terms were tried but were not signi?cant and did not add any explanatory weight to the model.See Table 3.

Table 3

Results from the Log Model

Regression statistics

R 2

0 406365615Observations 89

ANOVA

df

SS MS F Signi?cance F Regression 424 060442676 01511114 37531

5 55514E-09

Residual 8435 148412110 418433Total

88

59 20885478Coef?cients

Standard Error t Stat P -value Intercept 1 4678923940 191894257 6494863 04E-11k 0 0785893080 012448586 3131141 24E-08N ?0 0074997830 00410193?1 828350 071046r 9 88528E-050 0004693210 210630 833686%p

?0 013056175

0 306270373

?0 04263

0 966098

Note.One observation had to be dropped for the log transformation,because it had an AOR of 0,i.e.,the lower bound.

3.4.Consumer Welfare

To compare the relative performance of these cate-gories we introduced a metric (Bapna et al.2000)based on “loss of surplus.”This is the difference between an individual’s winning bid and the mini-mum winning bid B l .Using a metric similar to the AOR,we compared the relative performance of the bidder types.Speci?cally,for each auction we mea-sured how much a winning bidder was worse off than the lowest winning bidder and then averaged this quantity within bidder types.Note that the maximum amount of surplus that could be lost by an oppor-tunist is equal to the bid increment.This is because an opportunist can always force his way through by bidding one increment more than the current lowest winning bid at the expense of the last person to bid that amount.

The results of Table 4can be best interpreted if one thinks of the “Mean”row to re?ect the average money left on the table by a given bidder type.We can see that participators are signi?cantly better off than eval-uators and opportunists,while the difference between the evaluators and opportunists is not signi?cant.The evaluators chose not to utilize the current informa-tion regarding the winning bids that was available to them and essentially missed out on capitalizing on the sequential and discrete nature of these auctions.

Table 4Pairwise t -tests for Bidder-Type Loss-of-Surplus Comparisons.

Relative loss of surplus Participators Evaluators Participators Opportunists Evaluators Opportunists Mean 7 68753113 138477 68753110 9832913 1384710 98329Variance

61 17528626 2408

61 1752874 16433

626 240874 16433

Hypothesized mean difference 000df 91

146

94

t stat

?1 82328?2 451650 713055P T ?t one-tail

0 035772

0 0077

0 238789

Note.Higher values indicate more money left on the table.

The participators,on the other hand,made full use of such information and were rewarded for it.

Observation 1.The difference between the relative loss of surplus of the evaluators and the participators is a joint estimate of the monitoring cost and the risk averseness of bidders participating in Internet auctions.

Future research is needed to isolate these two fac-tors and re?ne their understanding.Figure 2depicts how the percentage of bidders pursuing these strate-gies varies with bid increment k .It is interesting to note that as bid increment increases from $5to $20there are systematic changes in the population mix of bidders.

Figure 2

Bidder Classi?cation Aggregated by Bid Increment

The percentage of participators increases signi?-cantly,that of evaluators (the group most desirable to auctioneers)reduces by more than half (from 59%to 23%),and that of opportunists increases marginally from 20to 27%.Thus,we see clear empirical evi-dence of interaction between the bid increment,an important decision variable that auctioneers can con-trol,and consumers’bidding behavior.

In the next section we discuss certain strategic poli-cies that auctioneers can use to set near-optimal bid increments.We test the proposed policies against the observed strategies utilized by online auctioneers and present empirical evidence in favor of the former.

4.Setting Near-Optimal Bid

Increments

In§2we demonstrated that the number of differ-ent equilibrium points of multiunit auctions grows exponentially with the lot size N.We also brought to attention that not all of these equilibria are eco-nomically ef?cient.Our objective in this section is to provide strategic directions to online auctioneers so as to ensure that they are guaranteed at least the lower bound on the revenue.Drawing upon our analytical and empirical?ndings of§§2and3,respectively,we base such a strategy upon deciding an appropriate bid increment that partitions the distribution space of the individuals’valuations in an ef?cient fashion.Such a strategy maximizes the likelihood of realizing higher-revenue-yielding bid structures,as outlined in§2. For such a strategy to be operationalized in prac-tice,it requires a priori a partial knowledge of the distribution of consumer valuations,an estimate of the population size interested in a given auction,and the lot size(which is under the auctioneer’s control). While in traditional markets it may be dif?cult to obtain even partial insights into consumers’valua-tions,the emerging electronic marketplace is differ-ent.Estimates of current valuations can be obtained from alternative channels such as posted-price cat-alogs that sell exactly the same goods,as well as from other exogenous sources of information,such as price-comparison https://www.wendangku.net/doc/ba2827009.html,ing data collection tools such as ours,online auctioneers can systematically track each individual’s actions and create a histori-cal repository of https://www.wendangku.net/doc/ba2827009.html, already is using such a technique to signal a maximum suggested bid,which gives us further reason to believe that the above-mentioned parameters can be estimated at a cost that is lower than the potential bene?t from opti-mizing the bid increment.

The strategy itself is conservative,ensuring that auctioneer’s revenues are at least as great as the lower bound N V? .By construction,at least the lower bound would be achieved,provided there are N+1 or more individuals with valuations greater than or equal to the feasible bid level corresponding to the marginal valuation.Thus,we base our strategy on partitioning the distribution space so as to maximize the likelihood of having N+1 or more individuals with valuations∈ V? V? +k and none with valuations≥ V? +k .Our strategy is to compute an upper bound for the bid increment beyond which the likelihood of not even realizing the lower bound on revenue increases.

Let P denote the total population interested in a given auction.Assume that individuals’valuations are independent and can be drawn from a distribu-tion with a distribution function F · .Let V max be the maximum possible valuation for the object being auc-tioned amongst the population of consumers.Recall that N represents the lot size of the auction. Theorem1.The upper bound on the bid increment, k max,can be de?ned as k max=V max?x?,where x?= F?1 1? N+1 /P .Exceeding k max has a strict ynegative impact on the auctioneer’s revenue.

Proof.By construction,to guarantee at least the lower bound N V? on revenue being obtained,we need at least N+1valuations∈ V? V max .Given a ?nite population P we choose to partition the distri-bution such that the expected number of bidders with valuations∈ V? V max is N+1 .Clearly,N out of these N+1 bidders make up the set W of winners. As per Proposition1,because there are no feasible bid levels in[V? V max],the auctioneer’s expected revenue is N V? .

Figure3shows what happens if k is increased to k max,where >1.This implies that the feasible bids are wider apart.In such a case the bid immedi-ately lower than the lowest valuation of consumers in set W shifts from B m to B m as shown in Figure 3.This implies that the expected number of bidders with valuations∈ B m V max is≥ N+1 .Following Proposition1again,since there is no feasible bid ∈ B m V max N winners will win at lower level B m, which results in the auctioneer’s revenue being NB m≤NB m because B m

Corollary to Theorem1.Because Proposition1in-dicates that there are on

l

ytwo possib

l

e bid

l

eve

l

s in equi-librium,the expected value of B m=V max?k max. Proposition5.The optimal bid increment is strictly less than k max,the supper bound on the bid increment. Proof.We can prove this by contradiction.Let k?= k max/ , >1,be an additional partition of the distri-bution space[V? V max].We denote the upper half

Figure3Increasing k to k max >1 Permits More than(N+1)Valuations∈ V? V max]

of this distribution space as F u · and the lower half as F l · .By de?nition,setting k>k max implies that B m is the last feasible bid level.If on the other hand k< k max and as long as there are at least N+1consumers in the interval[V? V max],there exists a likelihood that the(N+1)th consumer falls in the upper half of the distribution space F u .Thus,this consumer will get another chance in the form of an additional feasi-ble bid level,to enter the winning list of bidders.The process will converge to equilibrium when there are only N consumers left in the above interval. The above theorem and proposition suggest that continued reduction in bid increment can have a pos-itive impact on the auctioneer’s revenue.We refute that argument by noting that after a certain level of reduction in the bid increment,the time cost of conducting such an auction increases inordinately. Instead,we direct our attention towards deriving a practically useful recommendation to auctioneers that can help them set their bid increment on a day-to-day basis.

4.1.Recipe for Designing MUPEA

The necessary ingredients are(i)knowledge of the lot size,(ii)knowledge of the distribution of the con-sumers’valuation,and(iii)an estimate of the popu-lation’s size.

Step1is to calculate k max,the upper bound on the bid increment.We can compute k max based on Theorem1.Note that this implies that the expected number of consumers with valuations∈ B m= V max?k max V max is N+1.

In Step2,because Proposition5states that the opti-mal bid increment is strictly less than k max,we need to determine the desired bid increment k?

Example3.Let ≥1be a multiplier such that B m is the next feasible bid after B m.In other words,k= B m?B m.With a uniform distribution of valuations in [B m V max]for a single item,the auctioneer’s expected revenue is

1/ 1?F B m B m F B m ?F B m

+ B m 1?F B m (6) Differentiating(6)with respect to and setting to zero we obtain

?=1+ 1?F B m / B m f B m (6a)

For a uniform distribution we obtain

?B m=B m+ V max? B m/V max / 1/V max (6b) which simpli?es to

?B m= B m+V max /2 (6c) Because

k?= ?B m?B m (6d) k?=k max/2is the optimal bid increment in case of uniform distribution and a single item.

The numerical example above illustrates an inter-esting property that k?equally divides the probability mass between V max and B m.As a rule of thumb,this seems to be an easy to use approximation,one that we rec-ommend.In the next subsection,we provide empirical evidence in support of this strategy.

Step3for the auctioneers is to decide on the rate of convergence to equilibrium,which can otherwise be thought of as duration of the auction.This in turn will determine r,the auction starting point.We explain these three steps by means of an example. Example4.Consider the case of an auction where individuals’valuations for N=12items were drawn from a uniform distribution V=$52 V max=$112 . Assume that the auctioneer set is r,the auction open-ing bid=$7,and that the bid increment k was set at$5.Assume that the estimate of the population for this auction,P,was55.

Recall that k?=k max/2where k max=V max?x? and x?=F?1 1? N+1 /P .We obtain k?=0 5 112?52 ?12/55 =6 55.Thus,the auctioneer did not violate the rule of thumb,which requires it to set the bid incre-ment k below k?.If,on the other hand,the population estimate was larger,say75,then k?would be4.8,in which case the auctioneer’s k=5would be in viola-tion of the rule of thumb.

Also,there would have to be at least nine rounds of bidding(or108bids)for the auction to reach equilib-rium(assuming everyone behaved like participators). For a24-hour auction this implies an arrival rate of4.5 bids per hour.If the auctioneer wanted to conduct this auction in six hours,than she could expect to have approximately three rounds(ceiling(4.5*6/12))of bid-ding before equilibrium and hence would need to set the opening bid at$37.This,of course,assumes that the arrival rate would be the same.

In this subsection,we presented a practical approach to designing MUPEA based on our ana-lytical and empirical?ndings.The strategy revolves around determining the correct bid increment that maximizes the likelihood of the desirable(high-revenue)bid structures presented in§2.The other two control factors,namely the lot size and the auction starting point are relevant too,but only in helping determine the correct bid increment.In the next sub-section we validate our approach empirically.

4.2.Empirical Validation of the Upper Bound

on Bid Increment

To determine which distribution best?ts the con-sumers’valuations,we?rst revisited our data to exclude the evaluators from the population count and their effect on AOR calculation.This was done because,by de?nition,evaluators are not affected by the bid increment k.We found that the distri-bution function of the individuals’valuations was ?at around and after the marginal valuation,leading us to assume that it was uniformly distributed(see Appendix A).Using the results from Theorem1and Proposition4,we calculated the bid increment using the heuristic decision rule k?=k max/2and compared it to the actual bid increment k by measuring the gap =k??k.Our hypothesis of interest was: Hypothesis.Setting the bid increment at k u≥k max/2 is a dominant strategyfor revenue-maximizing auctioneers over setting the bid increment at k l

We split the data set into two parts based upon the sign of .Negative values of indicated that the auctioneer had used a bid increment higher than k?,and vice versa.We empirically tested the validity of our upper bound by conducting a means test to compare the revenues from the positive and negative data.Table4presents the results of the t-test,which indicate that auctioneer’s revenues were signi?cantly lower when the upper bound was violated(the neg-ative data set).

The empirical evidence presented in Table5clearly suggests that online auctioneers paid a heavy price

Table5Auctioneer’s Revenues Are Signi?cantly Lower when the Upper Bound Is Violated

t-Test:Two-sample assuming unequal variances

K>k max/2K

Df68

t stat?2 29076

P T?t one-tail0 0125415

for misjudging the values of the bid increment. It should be reemphasized that the multiunit nature of these auctions only ampli?es any misjudgment on their part,as the consequences are N-fold.Auction-eers can do better by setting up technologies that help them learn about the nature of the distribu-tion of consumers’https://www.wendangku.net/doc/ba2827009.html,ing such knowledge they can ensure that they do not set bid increments in excess of k?=k max/2,where k max=V max?x?and x?= F?1 1? N+1 /P .

5.Conclusion and Directions for

Future Research

We deal with the important issues concerning the design of business-to-consumer multiunit online auc-tions.Rather than pursuing the traditional approach of maximizing expected revenues of auctioneers assuming distributional characteristics of consumers’valuations,we derive a structural characterization of the multiple equilibria,using the observable charac-teristics of such auctions.We observe that while much of the literature has largely ignored the sequential and discrete nature of such auctions,it does indeed play a signi?cant role in the revenue realization process. Our primary?nding is that amongst the many con-trol factors that are available to online auctioneers, the bid increment is an important factor.We provide both analytical and empirical evidence regarding the importance of bid increment in revenue generation. We also identify three different bidding strategies employed by consumers engaging in online auctions and show that these strategies are in?uenced by the bid increment.In particular,the strategy adopted by the evaluators re?ects the fact that the Internet makes auctions accessible to novice bidders and the availability of data makes electronic auction markets a perfect venue for testing the behavior of unin-formed bidders.We show how the bidder-strategy mix changes with bid https://www.wendangku.net/doc/ba2827009.html,rger bid incre-ments(associated with larger magnitude auctions) attract a greater percentage of rational participators, a?nding consistent with Lucking-Reiley and List (2000).We?nd that for Yankee auctions the bidding-strategy mix does not have a signi?cant impact on the auctioneer’s rents.Current work in examining the ?ner details of bidder behavior is being conducted by Easley and Tenorio(2002)on jump-bidding,a hybrid strategy between evaluatory and participatory bid-ding,and by Roth and Ockenfels(2000)on late bid-ding,termed as sniping.

With a motive of providing concrete and eas-ily usable strategic guidance to B2C auctioneers we derive an upper bound to the bid increment that should never be violated by auctioneers.Further, using this upper bound we suggest a rule of thumb for setting bid increment.Our empirical analysis pro-vides support for using this heuristic decision rule. In online settings the consumers cannot only simul-taneously participate in various auctions,they could simultaneously participate in various auction types as well as other dynamic and static pricing mechanisms such as quantity discounting and posted pricing, respectively.Readers will be interested in knowing how consumer gains would change in competing between these various mercantile processes.

Acknowledgments

This research was supported in part by the Treibick Electronic Com-merce Initiative(TECI),the Department of Operations and Infor-mation Management,School of Business,University of Connecticut, Storrs,and NSF CAREER grant#IIS-0092780(but does not neces-sarily re?ect the views of the NSF).

Appendix Estimation of Tail-End Consumer Valuations:A Case for the Uniform Distribution We use the maximum bid placed by a bidder,winning or los-ing,to be a proxy for the consumer’s valuation.In our auctions,a bidder is not allowed to bid between two successive feasible bid levels.Therefore,a person that has bid,say,$103,may actually have a true valuation of$108(at$108,person may or may not bid because she is indifferent)given that the minimum bid increment

is $5.Therefore,if the j th bidder’s highest bid was Bj ,then his true valuation may be anywhere between B j and B j +k .Observe that this is a conservative estimate.We are working with revealed pref-erence,assuming people are voting with their dollars.We are likely never to know the true valuations of the bidders,but B j +k is a conservative estimate of the valuation,and is valid across all three strategies.

The following is a representative demand curve of an actual auction,drawn by taking the number of people who bid at a given level or higher.The curve represents only the last eight bid-ding cycles because our assertion is that valuations are distributed ?atly in the tail.Note that a uniform value distribution results in a downward-sloping straight line.The curve in this ?gure is a straight line,with acceptable distortions in real data.Therefore,our assertion regarding the ?at tail is empirically

veri?ed.

01020304050607080300

350

400

450

500

Bids ($)

N u m b e r o f P e o p l e

For the same auction,we used a simple linear regression model of the form:(no.of bidders who bid >=bid level)= + ?bid level + ,and obtained a =0 41607???and an R 2of 99.53%.Sub-sequently,we deployed the above-mentioned procedure for all the auctions that we tracked and found very good support for the valu-ations to belong to a uniform distribution.The table below presents the R 2values of tests performed on a percentile basis.As is evident,more than 94%of the auctions had an R 2of 90and above and close to 60%of the auction had a ?t with an R 2of 96and above.R 2Percent above

R 2Percent above

88100 0%9659.3%9094 2%9836.0%9283 7%99

15.1%

94

72 1%

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Accepted by Chris Kemerer;received February 2,2000.This paper was with the authors 7months for 2revisions.

多媒体演示文稿的设计与制作

---------------------------------------------------------------最新资料推荐------------------------------------------------------ 多媒体演示文稿的设计与制作 多媒体演示文稿的设计与制作( 初级)策勒县策勒乡托帕学校图尔荪江麦提尼亚孜通过对多媒体演示文稿的设计与制作(初级)课程的学习,我已经掌握了多媒体演示文稿的设计与制作基本知识及制作方法,收获颇多,现就自己的学习谈谈学习体会.一、知识点： 1、创建演示文稿；2、插入多媒体资源；3、多媒体资源的搭配； 4、播放和调用文稿。 二、应用1、PowerPoint 中有多种创建演示文稿的方法，对于一个初学者想要快速制作一个演示文稿可以根据内容提示向导创建演示文稿。 内容提示向导是创建演示文稿最快捷的一种方式，在内容提示向导的引导下，不仅能帮助使用者完成演示文稿相关格式的设置，而且还帮助使用者输入演示文稿的主要内容。 2、在多媒体演示文稿的页面中插入有关的文本、图片等多媒体资源需以下几个步骤： 选择要插入的多媒体资源；调整插入对象的位置和大小；3、（1）配色方案： 配色方案就是由多媒体演示文稿软件预先设计的能够应用于幻灯片中的背景、文本和标题等对象的一套均衡搭配的颜色。 通过配色方案，使多媒体演示文稿色彩绚丽，多呈现的内容更加生动，进行配色时需完成以下几个步骤： 1 / 3

选择配色方案；应用配色方案；（2）利用版式搭配多媒体资源：版式是PowerPoint2003 软件中的一种常规排版的格式，通过幻灯片版式的应用可以对文字、图片等等更加合理简洁完成布局，通常PowerPoint2003 中已经内置文字版式、内容版式等版式类型供使用者使用，利用版式可以轻松完成幻灯片制作和运用。 运用版式搭配多媒体资源需要以下几个步骤： 选择版式；应用版式；（3）、图形组合： 图形组合是 PowerPoint 软件中的一种图形处理功能，可以将多个独立的形状组合成一个图形对象，然后对组合后的图形对象进行移动、修改大小等操作，操作步骤如下： 选择图形；组合图形；4、播放和调用文稿： （1）、自定义播放： 由于一个演示文稿中可能有很多张幻灯片，有些时候我们不需要全部播放出来，这时就需要对演示文稿中的幻灯片设置自定义播放。 自定义播放演示文稿需以下几步： 选择要播放的演示文稿；设置自定义播放；（2）、打包演示文稿：演示文稿制作完成后，往往不是在一台计算机上播放，有时会出现演示文稿中所插入的视音频等资源不能顺利播放的情况。 如张老师把在家做好的演示文稿拿到教室播放，在排除连线、播放软件问题等因素后，演示文稿中插入的资源仍不能播放，请教计算机老师后，计算机老师建议可以通过以下两种方式解决：打包演示文稿；用 U 盘把 PowerPoint 中的所有资源拷到教室重

多媒体演示文稿的设计与制作学习心得体会

多媒体演示文稿的设计与制作学习心得体会 杨保政 作为一名小学数学年教师，我对教学媒体和资源总是充满了兴趣。在上课的时候，我更喜欢利用多媒体，来引导学生学习新知识。但有的时候上课的效果却不尽如人意。这次能参加全员培训中我学到制作演示文稿的时候，清新的ppt 演示，实用的制作技巧，让我眼前一亮，制作攻略更是让我热血沸腾，我终于认识到了我以前为什么很用心的制作PPT，但是效果却不好的原因了，那就是没有人会对着密密麻麻的知识点感兴趣的，不由得想到了初中时候的自己，和他们不是一样的吗？ 在本次培训中制作演示文档的部分，我对它进行了简单的总结： 攻略一：少即是多：每页一个主题；巧用备注栏；字少图大；提炼关键词句。呆板无趣的知识点会让学生们昏昏欲睡，如果将知识点精炼再加上图片会提升学生学习的兴趣，而且也减轻了学生的负担，让他们在快乐中获取知识。甚至在PPT中我可以恰当使用高桥法，醒目的字眼跃然眼帘，再不用老师来反复强调这是重点啊重点啊！ 攻略二：：换位思考：文字不小于24号；及时回顾总结；文字和背景反差鲜明；从学生的角度来思考一堂课的教授方

法，没有那么多过目不忘的学生，怎么讲课才能使学生印象深刻呢？看来我要在这方面多下功夫了。 攻略三：逻辑清晰：顺序播放；逻辑主线简明；格式一致；思想要点图表化。 攻略四：形象表达：适当运用全图型PPT；图表图形化；精心设计封面和目录；用声音烘托气氛。一幅好图胜过一千句话，无关的美景干扰主题；过多的插图分散注意；过于复杂的画面增加认知负荷；插图与背景混杂 攻略五：动静结合：控制长度；加快速度；明确目的；聚焦内容 在本次学习中，有一句话令我印象深刻，一堂课是否精彩，关键是教师而不是工具！是啊，无论ppt做得多么华丽，内容是多么深刻。但是一堂课的精彩与否，还是得靠教师来把握，路漫漫其修远兮，吾将上下而求索！

多媒体演示文稿的设计与制作学习心得体会

多媒体演示文稿的设计 与制作学习心得体会 This model paper was revised by LINDA on December 15, 2012.

多媒体演示文稿的设计与制作 学习心得体会 通过这次培训学习，使我进一步地掌握了制作和应用ppt等网络教学的知识和技能，增长了见识，理论水平、操作水平也有所提高。基本上掌握多媒体教学演示文稿的制作方法，主要有以下几个方面内容： （一）创建多媒体教学演示文稿； （二）编辑幻灯片； （三）编辑超级链接； （四）播放并调试幻灯片； （五）使用动画效果； 对我们教师来说，PowerPoint课件是最早接触的。利用PowerPoint可以创建出非常漂亮的幻灯片文稿，这些幻灯片中既可以有文字，还可以包含图画、表格、统计图表、组织结构图，甚至可以有声音、乐曲和动画效果，还可以为这些幻灯片设计出统一或不同的背景。利用PowerPoint可通过各种形式放映幻灯片，既可以在完全没有人工干预的情况下自动放映，也可以由使用者手工控制播放，可以令每张幻灯片从不同的角度，以不同的方式切入到屏幕上，使得放映效果生动有趣。这次网络研修，主要学习了Powerpoint基础操作、基本编辑；音、视频处理；演示文稿中动画的设置，设置不同的背景，艺术字与自选图形等。通过学习我对制作课件有了新的认识，制作课件既要讲究精美又要讲究实用。不同的制作软件具有不同的特点，在制作课件时，应根据需要选择合适的制作软件。制作课件是一个艰苦的创作过程，优秀的课件应融教育性、科学性、艺术

性、技术性于一体，这样才能最大限度地发挥学习者的潜能，强化教学效果，提高教学质量。 在这一次的学习中，我通过对每个章节的仔细学习，才知道平时经常用的ppt有如此强大的教学课件制作功能，可以说我之前所掌握的只是ppt课件制作功能的冰山一角。 在现代教育教学中多媒本技术在教育教学上的运用越来越多，多媒体以它更直观、更灵活、更易让学生理解的特点，使它成为许多教师教学方法的首选。而之前我只是对ppt课件的制作有一点认识，通过教师深入浅出的讲解和鲜活的实例，让我对ppt课件有了更深的认识，在今后的课件制作方面，我会把所学的制作技能运用其中，制作出更加实用、高效的教学课件。 通过学习，使我更加深刻地了解了多媒体课件制作的方法及技巧，认识到多媒体课件制作为教师专业化的成长提供了一个平台，同时也让我明确了本次学习的目标、内容、使自己由传统化教师向现代化教师发展。 张三

5.演示文稿设计与制作

第5章演示文稿设计与制作 第1节认识演示文稿第1课时（共2课时） 一、教学目标： 1、知识与技能： （1）掌握“ wps演示”的启动和退出方法 （2）了解“ wps演示”窗口的组成和使用 （3）初步掌握“ wps ”基本操作 2、过程与方法：通过观看、欣赏“ WPS演示”范例作品，激发学习兴趣，结合任务认识“WPS 演示”的窗口，掌握标题幻灯片的制作方法，在实践过程中达成技能的形成。 3、情感态度与价值观：知道“ WPS演示”是一种展示、汇报工具软件，知道能用“WPS 演示”制作一些作品来展示自己的风采、想法等，感受信息技术的魅力和价值。 二、教学重点： 知道演示文稿的编辑 三、教学难点： 演示文稿的编辑 四、教学方法： 任务探究，体验学习，实验学习 五、教学过程： （一）情境导入 同学们，大家好！今天老师带了件礼品给大家，想看看吗？看完后请你说一说看到了什 么？听到了什么？ 师向学生展示介绍学校的演示文稿。 刚才老师向大家展示的作品是一个演示文稿，它可以将文字、图片、视频和音乐等素材 整合起来。演示文稿在我们的生活中用处可大啦，如产品介绍、自我介绍、辅助教学等。制 作这样的作品，需要专业的软件，你知道有哪些软件可以制作演示文稿呢？今天向大家介绍一款专门用于制作演示文稿的软件一一“WPS演示”。 今天这节课我们就一起来认识“ WPS演示”软件。（板书：第5章第1节认识演示文稿）（二）、新授 自主学习： 1、一个完整的演示文稿一般由___________________________________________________ 构成。 2、演示文稿中包含的素材一般有_________________________________________________ 等。 3、演示文稿的设计包括__________________________ 。 合作探究： 1、任务一：新建演示文稿 学生自学，打开“ wps演示”窗口，新建一个“ wps演示”文档。 2、任务二：新建“封面标题页” 下面我们来新建第一页幻灯片。 单击右侧的“版式”按键，打开“幻灯片版式”任务空格，在“母版版式”中选择“空 白” 3、任务三：插入字标题 插入“中国元素”艺术字 4、任务四：插入背景图片 插入“中国元素背景 1 ”并设置“叠放次序”为“置于底层”

多媒体演示文稿的设计与制作

多媒体演示文稿的设计与制作 ——基于网络环境下任务驱动教学单元教学案例设计 山西省运城市康杰中学赵红冰 【课时安排】8课时 【年级】高一年级 【学习目标】 ◆知识与技能： ①掌握多媒体演示文稿中幻灯片的基本制作方法。 ②熟练掌握幻灯片的自定义动画、幻灯片切换、放映方式等设置。 ③掌握多种媒体的插入方法与超级链接设置。 ④能够对幻灯片进行打包并解包放映。 ⑤能够利用多种途径搜集表现主题所需要的多媒体素材，并能进行筛选规类。 ⑥能利用网络教学软件提交作业。 ◆过程与方法： ①通过作品的制作过程提高学生综合处理多种媒体技术的能力。 ②通过幻灯片版面的整体布局和设计以及背景、色彩的搭配提高学生的艺术表现力和审美能力。 ③通过创建超级链接培养学生对作品的控制能力和交互能力。 ◆情感态度与价值观： ①图文声像并茂，激发学生学习兴趣。 ②友好的交互环境，调动学生积极参与。 ③丰富的信息资源，扩大学生知识面。 ④超文本结构组织信息，提供多种学习路径。 【学习重点】 确定主题并围绕主题搜集、筛选、分类整理素材。 幻灯片版面的设计与布局。 【学习难点】 色彩的搭配与风格的统一、独特。 【学习平台】 基于互联网的多媒体网络教室. 【学习方法】 基于“任务驱动教学方法”下的自主、协作、探究、创新的学习方法。 一、任务设计 （一）、任务描述： 学习完PowerPoint办公软件，我们已了解了这是一个集多种媒体的演示性文稿，通过多媒体的组合可以对主题的表达更形象、生动、丰富多彩。请同学们利用已掌握的制作演示文稿的多种技术来表达一个主题，制作出图文并茂、形象生动的电子演示文稿。 （二）、任务要求： 1、主题要求 自由命题：主题鲜明、内容健康，富有个性。 可参考以下方向： 宣传科普知识或环保知识；介绍本地区旅游资源；介绍本校风貌；介绍本班情况；

多媒体演示文稿的设计与制作学习心得体会

多媒体演示文稿的设计与制作学习心得体会通过这次培训学习，使我进一步地掌握了制作和应用ppt等网络教学的知识和技能，增长了见识，理论水平、操作水平也有所提高。基本上掌握多媒体教学演示文稿的制作方法，这次培训学习心得体会如下： 一、知识点： 这次培训学习主要有以下几个方面内容： （一）创建演示文稿 （二）插入多媒体资源 （三）多媒体资源搭配 （四）播放和调试文稿 二、内容呈现： 1.创建课件页 （1）新建文稿 启动PowerPoint，在"新建演示文稿"对话框中选择"空演示文稿"。 （2）选择版式 默认的是“标题幻灯片”。课根据自己的需要进行选择； （3）输入文本 选择"插入"菜单中"文本框"中"文本框"命令后，在编辑区拖动鼠标，绘出文本框，然后输入相应文字或者粘贴上你所需要的文字。 （4）格式化文本 与其它字处理软件（如WORD）相似 （5）调整文本位置 通过调整文本框的位置来调整文本的位置。先选中要调整的文本框，使其边框上出现8个控制点，然后根据需要拖动控制点，文本框随之改变大小。当鼠标指针放在文本框边上的任何不是控制点的位置时，鼠标指针附带十字箭头，这时拖动鼠标可调整文本框的位置。 通过调整文本框的位置来调整文本的位置。先选中要调整的文本框，使其边框上出现8个控制点，然后根据需要拖动控制点，文本框随之改变大小。当鼠标指针放在文本框边上的任何不是控制点的位置时，鼠标指针附带十字 箭头，这时拖动鼠标可调整文本框的位置。

2、编排与修改 2.1 插入图片 （1）选择"插入"-"图片"，选取合适的图片，然后单击"插入"按钮。 2.2 选取模板 单击"格式"菜单中的"幻灯片设计…"命令，选择合适的模板，也可在幻灯片上单击右键，通过快捷菜单选择"幻灯片…"命令。 2.3 应用背景 如果不想对课件页添加模板，而只是希望有一个背景颜色或者是图片，可以单击"格式"菜单中的"背景"命令，在"背景"对话框中，打开下拉列表框，或单击"其他颜色…"选择合适的颜色，也可以选择"填充效果" 2．4影片、声音 执行“文件——插入——影片和声音”选择文件中的影片或者文件中的声音进行操作，为了防止课件到拷贝其他电脑无法获取文件，可将声音或影片文件与幻灯片文件放在同一文件夹下 三、学以致用： 1、PowerPoint中有多种创建演示文稿的方法，对于一个初学者想要快速制作一个演示文稿可以根据内容提示向导创建演示文稿。“内容提示向导”是创建演示文稿最快捷的一种方式，在“内容提示向导”的引导下，不仅能帮助使用者完成演示文稿相关格式的设置，而且还帮助使用者输入演示文稿的主要内容。 2、在多媒体演示文稿的页面中插入有关的文本、图片等多媒体资源需以下几个步骤：选择要插入的多媒体资源；调整插入对象的位置和大小； 3、（1）配色方案：配色方案就是由多媒体演示文稿软件预先设计的能够应用于幻灯片中的背景、文本和标题等对象的一套均衡搭配的颜色。通过配色方案，使多媒体演示文稿色彩绚丽，多呈现的内容更加生动，进行配色时需完成以下几个步骤：选择配色方案；应用配色方案；（2）利用版式搭配多媒体资源：版式是PowerPoint2003软件中的一种常规排版的格式，通过幻灯片版式的应用可以对文字、图片等等更加合理简洁完成布局，通常PowerPoint2003中已经内置文字版式、内容版式等版式类型供使用者使用，利用版式可以轻松完成幻灯片制作和运用。运用版式搭配多媒体资源需要以下几个步骤：选择版式；应用版式；（3）、图形组合：图形组合是PowerPoint软件中的一种图形处理功能，可以将多个独

(完整word版)多媒体演示文稿的设计与制作学习心得

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