Cooperation among bidders in traditional auctions is typically forbidden. This is because it is viewed as being harmful to the interests of sellers, who hope to obtain fair prices for their sale items. It also may be harmful to other bidders who are not able to take advantage of any cooperation that is occurring. In online group-buying auctions, in contrast to traditional auctions, cooperation results in higher welfare, leading to market expansion that benefits buyers and sellers, as well as the auction intermediary. This has not been well understood in prior research, however. In this article, we show how the online group-buying auction mechanism on the Internet can be effectively enhanced to produce higher welfare for the participants. The key to achieving this, we find, is for the auction intermediary to provide a means for bidders to cooperate, so as to collectively express greater demand. Such cooperation, it turns out, permits the group-buying auction mechanism to dominate the fixed-price mechanism from the seller’s point of view under some circumstances. Through an analytical modeling analysis, we offer insights into how sellers can set their group-buying auction price curves more effectively, so as to take advantage of bidder cooperation to improve auction performance. We further argue that the goal of the auction intermediary should be to offer an information sharing mechanism to facilitate bidding ring formation, as a means to maximize the value of this market mechanism.
Introduction
In the past, transaction costs in physical auctions prohibited many people from participating. However, the Internet has made possible much lower cost online auctions, and consequently their popularity has boomed. Although electronic auction markets like eBay (www.ebay.com) and Taobao (www.taobao.com.cn) are the most well known, another interesting market mechanism that has been tried out is the online group-buying auction. Online group-buying permits individual buyers to obtain the same discounts as retailers who buy in volume, as discussed in the mechanism patent filed by Van Horn and Gustafsson (2000). The popularity of the traditional quantity discount, which has been studied by Monahan, 1984, Lee and Meir, 1986, Kohli and Heungsoo, 1989 and others, has contributed to the interest in group-buying, a variant of quantity discounting for e-business. Our current interest centers on how buyer cooperation works in the context of online group-buying auctions.
Two Internet sellers, Mercata.com and Mobshop.com, were once considered to be the leaders in online group-buying. However, when Mercata shut down in 2001, and Mobshop changed its business strategy to emphasize B2B software, many online group-buying Web sites failed outright or reoriented their business models and no longer used this market mechanism (Kauffman and Wang 2008). Kauffman and Wang (2002) point out that limited sales volume prevented Mercata from realizing low prices, which ultimately drove consumers elsewhere. Posted-price buying permitted consumers to lock in a purchase, avoiding purchasing uncertainty, which is now understood to be a critical purchasing value driver (Ho et al. in press). The prevalence of “always low price” sellers in many locations across the United States on the Internet (e.g., Target.com, BestBuy.com and Walmart.com) created further pressures. Also, consumer impulse buying was inhibited.
Of late, however, online group-buying has been making inroads again in the marketplace. We see this occurring on three continents: Europe, North America and Asia.
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Europe: In Europe, for example, LetsBuyIt.com reopened for group-buying auction business after a two-year hiatus, and has continued to leverage a group-buying approach as a pricing mechanism for the goods it sells, until another recent operational hiatus in 2008 (see Textbox 1).
LetsBuyIt.com
LetsBuyIt.com has attracted widespread publicity as Europe’s first Internet-based cooperative buying company. Formed in Sweden in January 1999, the company launched its first Web site in April of the same year. By the end of 1999, it had expanded its coverage to include 11 European countries, with the ability to offer more than 80,000 different products and services, ranging from computer hardware to travel packages to Christmas trees. With Internet shopping undergoing an explosion worldwide, LetsBuyIt.com has built a very successful business in an extremely short time. Currently, the company estimates that its Web site attracts more than 100,000 visitors each day. This figure makes it Europe’s largest e-commerce site in terms of customer and visitor numbers. – Excerpted from Fleming.Blogspot.com, February 12, 2008
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North America: Online Choice in the United States, another online group-buying service for goods and services (e.g., home insurance, electricity and long distance telephone), emphasized buying pools and buyer–supplier price negotiation to a greater extent than the implementation of an auction mechanism. There are quite a few other group-buying auction firms that have operated with innovative and different business models (e.g., ActBig, 52MarketPlace.com, baZaare, Pointspeed, Etrana, VolumeBuy, Shop2gether.com, PeoplePC, eWanted.com, and iWant) (Cahlin, 2001, Free Press Release, 2005). Fig. 1 shows two current examples, Pudgin (www.pudgin.com), a group-buying and reverse auction site for business-to-business procurement, and eSwarm (www.eswarm.com), a business-to-consumer group-buying site that announced its launch in 2008.
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Asia: Meanwhile in Asia, online group-buying auctions have achieved an entirely different level of acceptance and success in the market in China (Areddy, 2006). Some of the leading names there include ( at www.taobao.com), Liba ( at www.liba.com), TeamBuy (at www.teambuy.com.cn), and Alibaba Group Buying ( at page.china.alibabe.com/shtml/about/ali_group1.shtml). See Fig. 2. Another development in China is the use of group-buying auctions for the sale and purchase of products that are in high demand but too expensive for most people to buy from the original manufacturers (Tang, 2008).
The most recent technological innovations in group-buying market mechanisms seem to emphasize the unification of electronic and physical world operations, and providing consumers with the means to process auction information (Davis 2006). These include the current sale item prices in the auction, current and predicted consumer participation levels, the willingness of other consumers to participate, and the projected final auction price for the sale item. The Christian Science Monitor (Montlake 2007) has written that the newest innovations in online group-buying emphasize the use of technology to enhance buyer communications, so consumers can band together and approach sellers for discounts on consumer electronics, home durable goods, and automobiles. Granados et al. (2008) have noted the existence of price and product opacity in Internet-based selling as a strategic choice on the part of the sellers. So online group-buying also may offer consumers the benefits of a mechanism design that will permit consumers to learn from one another over time about market demand and appropriate prices. They similarly may benefit from being able to share information with one another in order to coordinate their bidding and buying activities.
Traditional auction models can be used to analyze the group-buying auction. By considering the seller’s expected revenue, and using the well-known revenue equivalent principle, Vickrey (1961) proved that the four most popular auctions – first-price sealed-bid auction, second-price sealed-bid auction, Dutch auction and English auction – are equivalent under some assumptions. Myerson (1979) proved the revelation principle; that is, given any feasible auction mechanism, there exists an equivalent feasible direct revelation mechanism that gives to the seller and all bidders the same expected utilities as in the given mechanism. Based on this, Myerson (1981) presented characteristics of an optimal auction. In these auction forms, bidders’ competition determined the trading price, and sellers did not participate in the process of price-setting. Group-buying auctions are a variant of the seller’s price double auction, where the seller’s strategy is also important in determining the trading price. McAfee (1992) establishes a simple direct revelation double auction that combines two sealed-bid second price auctions. The details of these auction studies can be found in Klemperer’s (1999) survey.
This article aims to answer several research questions. What is the beneficial role of bidder cooperation in the online group-buying auction? How can we model this to characterize its mechanics? What kinds of auction mechanism refinements lead to high group-buying auction value? How can we determine the extent to which they do? Does the group-buying auction with cooperation outperform the more standard fixed-price mechanism? If so, to what extent? We will extend Chen et al.’s (2007) modeling approach to evaluate a new context: to assess consumer welfare in the presence of bidding collusion and coordination, when an online group-buying auction mechanism is used.
The rest of this article is organized as follows. Section 2 lays out what we know about the online group-buying auction market mechanism, including the performance characteristics of this market mechanism relative to others, and why it is important to reassess what we know based on a more careful consideration of the role of bidder information sharing and coordination in traditional auction markets also. Section 3 presents the analytical basis for the online group-buying auction model that we apply in this article. In Section 4, we will discuss eight properties of online group-buying auctions that form an additional theoretical basis for their analysis. Chen et al. (2002) previously developed four of them, and we developed four of them for the specific purpose of supporting the bidder cooperation analysis in this research. In Section 5, we analyze the welfare associated with cooperation in the online group-buying auction through a mechanism called a bidding ring, and show why no bidders – both cooperating and unaware bidders – are made no worse off. We also compare the relative performance of the online group-buying auction mechanism with bidder cooperation to the fixed-price mechanism for selling. Section 6 answers the question of how bidding ring members should optimize the bids that they make. Section 7 concludes with a discussion of our contributions, some limitations of our approach, and directions for follow-on research.
Section snippets
Prior research
There are relatively few studies of online group-buying auctions that have been conducted to date. Chen et al. (2002) studied bidders’ behavior in online group-buying and proved that, under the assumption of independent private value, the mechanism is incentive-compatible for bidders. This is consistent with McAfee’s (1992) conclusion about the seller’s price double auction. Kauffman and Wang (2001) conducted an experimental study of an online group-buying auction in which they analyzed the
Characterizing the online group-buying auction mechanism
To begin the development of our modeling approach to evaluate the impact of bidder cooperation in the online group-buying auction mechanism, we first discuss some key definitions and modeling notation, as well as a rule that governs how bidder participation up to the present results in the realization of a current price for the sale item. Thereafter, we present and describe the modeling assumptions, and state the basic mechanics of how bidders formulate their bidding strategy.
Mechanism properties for online group-buying with cooperation
Before we study online group-buying bidding cooperation in detail, it is appropriate to identify some of the online group-buying auction’s key properties. Four relevant properties have been proven earlier by Chen et al. (2002). These properties compare the possible result of different bids, which are helpful for the bidders to choose the optimal bidding price. In this section, we distinguish between the pre-established properties, and several new ones that enable us to obtain the main results
The online group-buying auction with bidder cooperation
The eight properties that we presented are helpful in providing a theoretical basis to guide online group-buying auction participants in the optimization of their bidding strategies. In this section, we will study a special group of cooperating traders that we will refer to as a bidding ring.
How should bidding ring members bid?
We have shown that bidder cooperation is beneficial for both bidders and the seller, and that online group-buying auctions with cooperation weakly dominate fixed-price mechanisms. However, without more analysis, we are still unclear about the ring’s optimal bidding strategy. This depends on the bids that are made before the bidding ring forms, when bids by the bidding ring occur (relative to the progress of the auction), and the bids that occur after the bidding ring is formed. However, we will
Conclusions
In this research, we analyzed an interesting form of online auction, the online group-buying auction with bidder cooperation. We discussed some of the key properties of these auctions for the bidder cooperation context, and revealed a number of theoretical findings regarding bidder cooperation and bidding ring strategy. We have drawn four distinct conclusions. First, with cooperation, all bidders will bid no lower than they would without cooperation, based on the modeling approach and
Acknowledgements
The work of Jian Chen and his colleagues was partly supported by the National Science Foundation of China, under grants 70890082, 70518002 and 70621061. Rob Kauffman thanks the Center for Contemporary Management Research of Tsinghua University’s School of Business and Economics, as well as the MIS Research Center of the University of Minnesota, and the Center for Advancing Business through Information Technology at Arizona State University for providing support. An earlier version of this
Online group buying platforms (OGBPs) as a new form of e-commerce, offer a new channel for vendors to provide discounts on assorted services through promotion, and attract new customers with the opportunity to experience their services. This paper is conducted based on genuine issues observed in OGBPs where a vendor decides to advertise its service by offering a discount coupon on an OGBP. Accordingly, we propose a fuzzy, dual-channel supply chain, with one OGBP and one vendor who sells her/his service through both offline and online channels. Considering the cognitive uncertainty of parameters in a supply chain, all parameters of the problem are specified as fuzzy variables based on experts’ opinions. This paper develops a demand function of online customers depending on online and offline prices, sales period on the OGBP and vendor's credibility. The vendor's credibility is also affected by service level and advertisements. Furthermore, in the competitive OGB market, two game structures, i.e., a centralized model and a decentralized model called as OGBP-leader Stackelberg, are considered under different refunding and revenue sharing scenarios of unredeemed coupons. By comparing these scenarios, the purpose of this paper is to examine the optimal approach for OGBP’s pricing strategy and vendor’s decisions. Expected value models are developed to evaluate how members decide commission price, online price, service quality, and sales period of a service in each scenario. According to the results, the vendor or OGBP that provide a full refund to the customers maximizes the expected profits compared to No-refund policy; sharing the entire revenue from the coupons sales with the vendor increases the commission price. This increases the online price and reduces the quality, sales period and expected profits; the vendor's generosity, meaning providing a refund to customers and earning revenue from redeemed coupons, is in favor of both the vendor and OGBP; and we investigate the impact of quality and credibility factors under a fuzzy environment on optimal profits.
Social couponing is a growing promotional phenomenon in the service industry. However, since the conversion rate of distributed coupons into coupons redeemed for purchase is relatively low, there is a need to understand the redemption decisions of consumers. Lower conversion rates lead businesses to lose both customers and profits. Previous studies have typically focused on social couponing from a business perspective, without exploring factors from the customer's end. The current study explores the factors influencing customers' decision to redeem coupons and highlights the interrelationships between the factors. Data were collected from 353 online customers on their redemption experiences during their food purchases. Structural equation modeling was performed to examine the significance of the factors and establish the predictability of customers' redemption decisions. We then explored different machine learners to identify the best-fitting models for customers' redemption decisions. Results showed that the prediction accuracy of the decision-tree-based models was the highest. These models delineate the role of influencers in various redemption aspects and validate the mediation effects of perceived risk, deal proneness, referral, and consumption frequency. The study also highlights future research areas in the social couponing domain.
Kauffman (2001) and Yuan (2004) pointed out that consumers forming shopping groups can increase their influence and obtain more discounts. In traditional group-buying, members are composed of close relatives and friends who get together to buy the same kind of products to avail of a discount (Chen et al., 2009). The initial group buying model was mainly built with close social relations as the link, but scholars found that such models have significant geographical boundaries, which is not conducive to the expansion of its scale (Jing and Xie, 2011; Chen et al., 2015; Li et al., 2020a).
The consumption stickiness relationship established by community group buying is a typical complex social network. The network structure, member size, interaction frequency and purchase quantity are determined by the effective communication of customer perceived service quality (PSQ). This paper introduces the social reinforcement effect to construct a G-SCIR model of PSQ propagation in the community group-buying, and improves the calculation method of propagation probability and node status attributes. We use numerical simulation methods to explore how the social reinforcement effect can promote the spread of PSQ and achieve a steady state by influencing recovered nodes. The simulation results show that the G-SCIR model proposed in the paper has better stability and higher coverage than the traditional SCIR model. The change trend of the attributes of each node in the network is also closer to the real one, which can effectively simulate the propagation process and evolution characteristics of PSQ in the context of community group-buying. Meanwhile, the paper verifies that the steady state of PSQ propagation is usually affected by its initial conversion probability of community group-buying, its upper limit and social reinforcement factor, and there are significant Markov properties in the propagation process of PSQ. The findings may further enrich the community business and online group buying theory, and provide theoretical reference and practical guidance for community group-buying enterprises to optimize market layout and formulate scientific communication strategies.
Electronic commerce has changed traditional business trading behaviors, since consumers can easily consume through the Internet e-commerce platform. The Internet provides numerous products and services, but consumers find it is hard to choose their favorite ones. The consumer-to-business (C2B) is popular in recent years and it has become one of the best choices for customers forming a group to buy products. Thus, more and more consumers participate in online group buying. However, consumers anticipate to different prices when buying products and demand service. Thus, applying an intelligent agent into negotiation can effectively decrease efforts spent on collecting buyer information, transaction costs, and negotiation with sellers.
This study proposed a system that applies an intelligent agent into the C2B e-commerce process, and evaluates the system through an experiment. Additionally, a questionnaire is used to investigate the benefits of the proposed intelligent agent systems. Analytical results show that the proposed intelligent system can increase user satisfaction, reduce performance risk, and raise perceived fairness, but nothing help on perceived value. It implied that the system still needs efforts and time to promote in nowadays commerce. If people can understand its value from finding the information they need, it must grant the more perceived value. Additionally, this system is not only applicable to C2B, but it can extend to other e-commerce models, because the agent can help the negotiation between the sellers and buyers.
2017, Electronic Commerce Research and Applications
Citation Excerpt :
Anand and Aron (2003) revealed that the dynamic group-buying pricing mechanism outperforms fixed-pricing mechanisms when the seller faces an uncertain market. Chen et al. (2009) analyzed a bidder cooperation’s effect on group-buying, and they found that cooperation can improve profits for both sellers and bidders, which differs from traditional auctions. The dynamic group-buying mechanism gradually became obsolete due to its three main drawbacks, as outlined by Kauffman and Wang (2001): (1) the business model is too complex for common consumers; (2) the group-buying auction cycle is too long and hinders impulse buying; and (3) the transaction volume is too low.
In this paper, we study the popular group-buying model in which a seller offers a discount on group-buying websites to attract new customers coming to experience his/her service. We analyze the conditions under which a seller could benefit from the group-buying strategy, in addition to discussing the optimal decisions concerning service quality and online price. We find that only when the website scale is sufficiently large will the seller benefit from adopting the group-buying strategy. We also consider the customers’ substitution effect, that is, the existing offline customers turn to an online channel when the seller offers a discount on the group-buying website. When the website scale is relatively small and the substitution rate is high, the seller cannot benefit from group-buying. The seller should set a service quality higher than the base service quality when he/she cooperates with large group-buying websites. Moreover, compared to purely offline businesses, the seller will set a higher quality level if adopting a group-buying strategy.