外文文献翻译网络团购中的不确定性需求细分-中英对照

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1、题 目：我国网络团购的发展现状与问题探究一、外文原文标题： Segme nti ng un certa in dema nd in group-bu ying auctions原文：Dema nd un certa inty is a key factor in a seller-makidgcpsiocessfor products sold through on li ne auct ions. We explore dema nd un certa inty in group-bu ying aucti ons in terms of the exte nt of low-valuati

2、 on dema nd and high-valuati on dema nd. We focus on the an alysis of a mon opolistic group-bu ying retailer that sells products to con sumers who express differe nt product valuati ons. We also examine the performanee of a group-buying seller who faces competitive posted-price sellers in a market f

3、or the sale of the same products, under similar assumpti ons about un certa in dema nd. Based on a Nash equilibrium an alysis of bidder strategies for both of these seller-side competition structures, we are able to characterize the groupbuying auction bidders dominant strategies. We obtained a numb

4、er of interesting findings. Group-buying is likely to be more effective in settings where there is larger low-valuatio n dema nd tha n high-valuati on dema nd. The structure of dema nd matters. This finding has releva nee to the marketplace for new cameras, n ext-ge nerati on microprocessors and com

5、puters, and other high-valuati on goods, which are unlikely to be as effectively sold in group-buying markets. We obta ined additi onal results for the case of con ti nu ous dema nd, and find that there is a basis for the seller to improve reve nues via effective group-bu ying aucti on price curve d

6、esig n.Keywords: Con sumer behavior, bidd ing strategy, dema nd un certa in ty, econo mic an alysis, electr onic markets, group-bu ying auct ions, market mecha ni sm, posted-price mecha ni sm, simulati on, un certa inty risk.The developme nt of adva need IT makes it possible to use no vel bus in ess

7、 models to han dle bus in ess problems in new and inno vative ways. With the growth of the Intern et, a nu mber of new electro nic auct ion mecha ni sms have emerged, and aucti ons are gen erally known to create higher expected seller reve nue tha n posted-prices whe nthe cost of running an auction

8、is minimal or costless (Wang 1993). Some of the new mechanisms we have seen include the online Yankee and Dutch auctions, and the “ nam-eyourown-price ” and “-itb- nuyow” mechanisms. An example is eBay Dutchs auction for the sale of multiple items of the same description. Another of these new electr

9、onic market mechanisms that we have observedis the group-buying auction, a homogeneous multi-unit auction (Mitchell 2002, Li et al. 2004).Internet-based sellers and digital intermediaries have adopted this market mechanism on sites such as LetsBuyI () and eWinW ( ). These sites offer transaction-mak

10、ing mechanisms that are different from traditional auctions. In traditional auctions, bidders compete against one another to be the winner.In group-buying auctions, however, bidders have an incentiveto aggregate their bids so that the seller or digital intermediary offers a lower price at which they

11、 all can buy the desired goods (Horn et al. 2000). McCabe et al. (1991) have explored multi-unit Vickrey auctions in experimental research, however, they did not consider the possibility of stochastic bidder arrival or demand uncertainty.This paper is the first to examine the impacts of demand uncer

12、tainty on the performance on online group-buying auctions. Based on a Nash equilibrium analysis of symmedtroicmainadnt strategies. Webidder strategies for a monopolist seller and a competitive seller, we are able to characterize the groupb- uying auction bidders find that group-buying is likely to b

13、e more effective in settings where there is larger low-valuation demand than high-valuation demand. Thus, the structure of demand at different level of willingness-to-pay by consumers matters. This has relevance to the marketplace for new cameras, next-generation microprocessors and computers, and o

14、ther high-valuation goods. We obtained additional results for the case of continuous demand valuations, and found that there is a basis for the seller to improve revenues based on the effective design of the group-buying auction price curve design. THEORYThe model for the group-buying auction mechan

15、ism with uncertain bidder arrival that we will develop spans three streams of literature: demand uncertainty, consumer behavior and related mechanism design issues; auction economics and mechanismdesign theory; and current theoretical knowledge abou the operation of group-buying auctions from the IS

16、 and electronic commerce literature.Demand Uncertainty, Consumer Behavior and Mechanism DesignDemand uncertainties typically are composed of consumer demand environment uncertainty (or uncertainty about the aggregate level of consumer demand) and randomnessof demand in the marketplace (reflected in

17、brief temporal changesand demand shocks that are not expected to persist). Consumer uncertaintaybout demand in the marketplace can occur based on the valuation of products, and whether consumers are willing to pay higher or lower prices. It may also occur on the basis of demand levels, especially th

18、e number of the consumers in the market. Finally, there are temporal considerations, which involve whether a consumer wishes to buy now, or whether they may be sampling quality and pricing with the intention of buying later. We distinguish between different demand level environments. In addition, it

19、 is possible that these consumer demand environments may co-exist, as is often the case when firms make strategies for price discrimination. This prompts a seller to consider setting more than one price level, as we often see in real-world retailing, as well as group-buying auctions.Dana (2001) poin

20、ted out that when a monopoly seller faces uncertainty about the consumer demand environment, it usually will not be in his best interest to set uniform prices for all consumers. The author studied a scenario in which there were more buyers associatedwith high demand and fewer buyers associatedwith l

21、ow demand. In the author s proposed price mechanism, the seller setspraice curve instead of a single price, so as to be able to offer different prices depending on the different demand conditions that appear to obtain in the marketplace. It may be useful in such settings to employ an automated price

22、searching mechanism, which is demonstrated to be more robust to the uncertain demand than a uniform price mechanism will, relative to expected profits. Unlike Dana s (2001) work though, we will study isnewttihnigcshthere are fewer buyers who exhibit demand at higher prices and more buyers who exhibi

23、t demand at lower prices. This is a useful way to characterize group-buying, since most participating consumers truly are price-sensitive, and this is what makes group-buying auction interesting to them.Nocke and Peitz (2007) have studied rationing as a tool that a monopolist to optimize its sales p

24、olicy in the presence of uncertain demand. The authors examined three different selling policies that they argue are potentially optimal in their environment: uniform pricing, clearance sales, and introductory offers. A uniform pricing policy involves no seller price discrimination, though consumers

25、 are likely to exhibit different levels of willingness-to-pay when they are permitted to express themselves through purchases atdifferent price levels. A current example of uniform pricing policy is iTunes (), which has been offering 99 0 per song pricing. The consumer has to deal with very little u

26、ncertainty in the process, and this may be a good approach when the seller wants to“train ” consumers to develop specifbuying habits (as seems to have been the case with the online purchase of digital music in the past few years).Nocke and Peitz (2007) characterized aclearance sales policy as chargi

27、ng a high price initially, but then lowering the price and offering the remaining goods to low value consumers, as is often seen in departmentstore sales policy. Consumers with a high valuation for the sale goods may decide to buy at the high price, since the endogenous probability of rationing by t

28、he seller is higher at the lower price. Apropos to this, consumers who buy late at low prices typically find that it is difficult to find the styles, colors and sizes that they want, and they may have more difficulty to coordinate the purchase of matching items (e.g., matching colors and styles of c

29、lothing). Introductory offers consist of selling a limited quantity of items at a low price initially in the market, and then raising price. A variant occurs when the seller offers a lower price for the first purchase of goods or services that typically involve multiple purchasesby the consumer (e.g

30、., book club memberships and cell phone services). Consumers who place a high valuation on a sale item rationed initially at the lower price may find it optimal to buy the goods at the higher price. Introductory offers may dominate uniform pricing, but are never optimal if the seller uses clearance

31、sales.In uncertain markets, buyers will have private information . Che and Gale (2000) pointed out that when consumers have private information about their budgetconstraints and their valuation of saleistems, so a monopolist s optimal pricing strategyis to offer amenu of lotteries on the likelihood

32、of consumer purchases of its products at different prices. Another approach is intertemporal price discrimination . By offering different prices with different probabilities for the consumer to obtain the good, the monopolist can profitably segment consumers even though valuation segments alone are

33、not profitable.Even when the seller can effectively identify the consumer demand level in the marketplace, due to stochastic factors in the market environment, it still may be difficult for the seller to effectively predict demand. As a result, the seller may try to improve its demand forecast by ut

34、ilizing market signals that may be observed when sales occur. However, there are likely to be some stochastic differences betweenthe predicted demandby the seller and therealized demandin the marketplace (Kauffman and Mohtadi 2004). Lo and Wu (2003) pointed out that a typical seller faces different

35、types of risks, and among these,a key factor is forecast error, the difference between the forecast and the actual levels of demand. Dirim and Roundy (2002) quantified forecast errors based on a scheme that estimates the variance and correlation of forecast errors and models the evolution of forecas

36、ts over time.2.2. Some Properties of Group-Buying Auction MechanismSome of the key characteristics associated with group-buying auction mechanism design are present in the literature. The group-buying auction mechanism is fundamentally different from the typical quantity discount mechanism (Dolan 19

37、87, Weng 1995, Corbett and DeGroote 2000) that is often used in consumer and business-to-business procurement settings.First, group-buying closing prices typically decline monotonically in the total purchase quantities of participating buyers, and not just based on an individuabluyer s purchase quan

38、tities. So a group-buying auction does not lead to price discrimination among different buyers and every buyer will be charged the same closing price.Second, in group-buying auctions, imperfect information may have an impact on performance and make the final auction price uncertain. Group-buying is

39、not the same as what happens with corporate shopping clubs or affinity group-based buying though.With these other mechanisms, consumers will beassociated with one another in some way, and be able to obtain quantity discounts as a result. Another variant of the quantity discount mechanism occurs on t

40、he Internet with shopping clubs and-buy“ingpo”werWeb sites. NetM (),Buyer s Advantage(), and Online Choice () are examples that we have recently observed in the marketplace. With uncertainty about the ultimate number of the bidders who will participate, interested consumers may not know whether they

41、 can get the products, or what the closing price will be when they make a bid. This may even occur when they bid the lowest price on the group-buying price curve.Third, in the quantity discount mechanism, to achieve a discount the buyer must order more than the threshold number of items required. In

42、 group-buying, the buyer can get the discount by ordering more herself or persuading other bidders to order more, as we saw with the “Te-lal -Friend ”link at Lets-Buy I for co-buying (and at the active group-donation site, F,).A final consideration in some group-buying auctions is that a buyer may b

43、e able to choose her own bidding price, which makes this kind of auction similar to an open outcry auction. In practice, many buyers will only be willing to state a low bid price, unless they can rely on the design of the mechanism to faithfully handle information about their actual reservation pric

44、e. Group-buying auctions have a key, but paradoxical feature: to reach a lower price and higher sale quantity bucket, the consumer may need to enter the auction at a higher price and lower sales quantity bucket (Chen et al. 2009). 出处：J. Che n, R.J. Kauffma n, Y. Liu, X. Song.Segmenting uncertain dem

45、and in group-buying auctionsR. Electronic Commerce Research and Applications 2009,3(001).、翻译文章标题： 网络团购中的不确定性需求细分 译文： 不确定性需求，是卖家通过网络拍卖形式销售产品的决策过程中的一个 关键因素。 我们从低价需求和高价需求的角度出发， 在团购拍卖中探索不确定性 需求。我们关注于一个垄断性团购零售商的分析， 即向那些需要不同产品价值的 消费者销售产品。 同时，我们也检测团购买方在市场上会有何种表现， 当团购买 方面对具有竞争力标价的卖方时， 卖方在类似于不确定性需求的假设下销售同种 产

46、品。以一个纳什投标者对这些卖方竞争结构的均衡分析策略为依据， 我们能够 描述出一种团购拍卖出价者占优策略。 我们得到了许多有趣的发现。 低价需求中 的团购很可能比在高价需求中的更有效。 需求的结构问题。 这项发现和新型相机、 下一代卫星处理器、 计算机和其他那些看似不太可能在团购市场上有效销售的高 价产品有关。 我们为了案例的连续需求能够有额外结果， 发现有一个以团购拍卖 有效价格为基础的可以改善卖方收入的设计曲线。关键词: 消费者行为，投标策略，需求不确定性，经济分析，电子市场，团 购拍卖，市场机制，价前机制，模拟，不确定风险。IT产业先进的发展，令用创新商业模式处理业务问题成为可能。随着网

47、络的日益发展， 许多新的电子拍卖机制出现了， 当一项拍卖的成本流失最小或无成本 的时候，拍卖通常被理解为是为了设定比标价更高的预期卖方收入 ( 王1993) 。我 们见过的一些新机制包括美国在线、荷兰式拍卖、自命名和“现在买”机制，有 一个易趣的荷兰式拍卖的例子为销售多个同类产品的描述。 我们观察了另一 种电子市场机制：团购拍卖，一个同质多单位拍卖 ( 米切尔2002，李等。 2004) 。 基于网络的卖家和数字媒介已经接受了这种市场机制例如“LetsBuyIt ”( www.) 和“ eWinW ” ( ) ，这些网站提供不同于 传统拍卖的“转换市场”机制。在传统的拍卖中，竞拍的人为了成为胜

48、利者，彼 此间进行抗争。然而，在团购拍卖中，投标者用一个诱因来集合他们的出标，以 至于卖方或数码调解人提供一个更低的价格， 使他们全都能买到自己想要的产品 ( 赫恩以及其他人。 2000) 。麦长基以及其他人 ( 1991) 已经在实验研究探索了多数 单位的成功拍卖。本篇论文是第一个探索不确定性需求在网络团购拍卖模式中的影响的论文。 以一个纳什投标者为一个专营商卖方和一个竞争卖方的均衡分析战略为依据， 我 们能描述一个团购拍卖出价者对称占优的策略。 我们发现网站上低价需求的团购 很可能比高价需求的更具有效果。 从而，产生了不同的消费者愿付水平需求结构 问题。这和新型相机，下一代微处理器， 计算

49、机和其他高价值的产品市场有关系。 我们为了案例的连续需求能够有额外结果， 发现有一个以团购拍卖有效价格为基 础的可以改善卖方收入的设计曲线。理论这个模型是有关于团购拍卖机制中的需求不确定者的， 我们将涉及到三方面 的文献跨度。不确定性需求， 消费者行为和涉及到机制设计问题； 拍卖经济学和 机制设计理论；当前 IS 和电子商务文学中的团购拍卖运行理论知识。 需求不确定性，消费者行为和机制设计需求不确定性通常由消费需求环境的不确定性 (或对消费需求总体水平的不 确定性)和市场随机性需求(简言之，就是反映典型的持续变动和需求震动)组 成的。消费的不确定性是关于以产品计价为依据的的市场需求， 主要看消

50、费者是 否愿意付出更高或更低的价格。 它也可能发生在以需求水平为依据尤其是市场消 费者数目上。 最终，有消费者是否愿意现在购买的暂时性考虑， 或他们在购买后 的质量和价格是否可抽样的长期考虑。我们在不同的需求水平环境中进行区分。 此外，这些消费需求环境很可能共存， 正如工厂制定价格歧视策略也是常有的事 情。这就使卖家考虑设定不止一个价格水平，正如我们经常在现实中看到零售， 和团购拍卖一样。达纳( 2001) 指出当一个垄断卖方不确定消费需求环境时， 通常不会对所有消 费者设定一个统一的价格来求自身利益最大化。 作者研究了一种情形， 这种情形 中有和高需求相关的较多买家和低需求相关的较少买家。

51、在作者提出的价格机制 中，卖方设定一个价格曲线而非单一价格， 这样能根据在市场中出现的不同需求 情况来提供不同的价格。 它可以在这种环境中利用一个自动觅价机制， 相对于期 望的利润而言， 需求不确定性已经被论证了比一个统一价格机制需求更坚固。 然 而不像达纳 ( 2001) 研究的那样，我们将会在较高价的少量买家的展会需求、 和在 较低价的多数买家的展会需求的情况下进行研究。 这有一个有用的方式来描述团 购，多数参与的消费者对价格非常敏感， 这是团购拍卖让他们感兴趣的原因所在。诺奇和派茨 ( 2007) 研究了作为专营商不确定性需求的一种工具， 限量供应用 以优化其销售政策。 作者在销售环境中

52、考察了三个不同销售政策， 并试图找出哪 一种是最佳的选择：统一定价，清仓减价销售，厂家供应。一个统一的定价策略 不包括卖方价格歧视， 虽然消费者在被允许通过不同价格水平购买表达自己的时 候，很可能显现出不同的愿意支付水平。 当前一个统一定价策略的例子就是苹果 公司开发的与iPod搭配的在电脑上使用的音乐播放器和音乐管理器 iTunes(),它就是一直提供每歌曲990的价格。消费者不得不在过 程中处理非常小的不确定性， 当卖方想培养消费者开发具体的购买习惯时， 这也 许是一个好的途径,(就是在过去的几年中经常见到的从网上下载音乐) 。诺奇和派茨 ( 2007)阐述了一个清晰的销售政策, 最初是制

53、定高价, 但是之后 又降低价格,给低价消费者提供剩余产品经常在百货公司销售政策中见到的 那样。高价值的消费者可能决定在销售产品高价时就购买, 虽然卖家限量供应内 在可能性的价格比低价稍稍高一些。就这点而言,稍后在低价上购买的消费者, 通常发现找到自己想要的风格、 颜色和样式比较困难, 他们可能在协调匹配的收 购商品上有更多的困难(例如：衣服的颜色搭配和样式风格) 。导言的提供是由 最初市场上低价的限量产品组成的, 然后提高价格。 当卖方在初次购买产品和服 务提供更低价格时,变化就发生了,这通常涉及到消费者的多重购买(例如：预 定俱乐部会员资格和手机服务) 。 消费者在一个最初低价定量配给的规模

54、产品上 设定一个高价, 发现这在高价购买产品上可能是理想的。 引言的提供可能主导统 一价格,但是如果卖家清晰地销售,效果就绝对不会理想了。在不确定的市场上, 买方会有私人信息。 澈和盖尔(2000)指出消费者有关于 他们的预算限制和他们的计价项目价值的私人信息。 所以一个专营商的最佳价格 策略是提供一个抽奖的名单, 类似于消费者购买它的不同价位的产品。 另一个方 式涉及到差别取价。 通过为消费者提供不同概论的价格使其获得益处, 专营商有 利地区分消费者,即使价值区分不仅仅是有利可图的。尽管当卖家能够在市场上有效地鉴定消费者需求水平, 但是由于市场环境的 随机因素, 有效预测需求对卖方而言可能仍

55、旧是困难的。 因此,卖方可能试图通 过利用市场信号来改善对消费者需求的预测, 这样在销售的时候可能观察被观察 到。然而, 卖方有可能在一些随机差异之间预测消费者需求, 并在市场上实现这 种需求(考夫曼,莫泰迪, 2004)。罗,吴 (2003)指出一个典型的卖方面对不同 类型的风险，在这些风险中， 一个关键因素在于预测误差， 预测间的差异和需求 的实际水平。 德拉姆和朗德 ( 2002) 以一个策划为依据来量化预测错误， 估算预测 误差的关联性并随着时间的推移建立起预测演化模型。 团购拍卖机制的一些可能性文献中呈现出了和团购拍卖机制设计相关联的一些关键性特征。 团购拍卖机 制和典型数量折扣机制

56、根本不同 (多兰1987，翁1995，科尔韦特，德格罗特 2000) ， 前者经常在消费者和企业对企业电子商务采购环境中被使用。首先，团购结算价格通常在总共参与者的需求总量上单调地下降， 而非单纯 地只依据买方的购买量。 这么一个团购拍卖不会在不同的消费者之间引发价格歧 视，每一个消费者将会支付统一的最终价。其次，在团购拍卖中， 不完全信息可能会在产品性能上有影响， 并制作最终 拍卖不确定价格。 但是团购和公司购物俱乐部或类似的团体购买不同。 同其他的 一些机制， 消费者将会在某种程度上相互关联， 并最终能够获得数量折扣。 另一 种数量折扣机制的变化发生在网络上，例如：购物俱乐部、 “购买力”

57、网站、网 络营销网站(market.con)，买方优势(www.buyersadvan )和在线 选择 ( ) ，这些都是我们最近在市场上观察的一些网站。 同 最终参与投标的人数不确定性有关， 感兴趣的消费者可能不知道他们是否能够得 到产品，或者当他们出标时会是什么样的临界价格。 这可能就可能发生在他们在 团购价格曲线的最低价格上出价的时候。再者，在数量折扣机制上， 为了获得折扣， 买方的数量必须超过要求团购的 最低限。在团购中， 买家能够通过自己订购更多产品或劝其他人订购刚多产品来 获得折扣，正如我们在“大家买”网站上看到的一个用“邀请好友”的链接来壮 大买方团体一样(在团购网站上也有类似情况)。在某些团购拍卖中， 最后要考虑的是一个买方可能选择他自己报的价格 有些类似公开喊叫拍卖了。 实际上， 许多购买者不仅愿意规定一个低的价格， 而 且他们能依赖机制设计， 详实地掌握他们实际预定的价格信息。 团购拍卖有一个 关键但自相矛盾的特征： 即为了得到一个更低的价格和一个更高的销售数额， 消 费者可能需要在高价低量的时候进入拍卖出处： 电子商务考证和应用