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Consumer adoption of group-buying auctions: an experimental study

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Abstract

Internet-based group-buying auctions enable consumers to obtain volume discounts, but they face risk and trust issues that are not present in other e-retailing formats, which affects their adoption by consumers. Bidders experience uncertainty about the final auction price, and the risk of whether the auction will be completed. We evaluate textual comments and the number of bids made in an auction as drivers of a consumer’s perceived financial and psychological risks toward the group-buying auction mechanism and trust in the auction initiator. We use an Internet-based experimental test bed for online group-buying auctions and will report on one experiment that we conducted. Our results indicate that textual comments made by the participants about sellers in past auctions and existing bids affected a consumer’s perceived trust in the auction initiator and the financial risk of the mechanism. Positive textual comments and more bids appear to enhance perceived trust in the auction initiator and reduce financial risk, and other consumers are more willing to make bids as a result. Consumers continued to express concerns about the uncertainty of the final group-buying auction price though.

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Notes

  1. We use the term bid to represent consumer behavior that involves sending an order to a group-buying auction. This involves specifying a price at which the consumer is willing to buy an item. It can also refer to the simpler act of being willing to buy the sale item at whatever comes out as the final price of the group-buying auction. The term order also is used to represent this idea in group-buying auctions in Taiwan: a consumer who places an order is making a commitment to be a purchasing participant in a group-buying auction at the final price of the auction.

  2. The traditional auction mechanisms include English auction, Dutch auction, first-price sealed-bid auction, and Vickery auction. See McAfee and McMillan [50] for descriptions and comparisons of these auction types.

  3. The literature distinguishes between direct and indirect feedback and uses distinct terms for each. A network externality is used to identify the direct impacts of growth in a network, while the term network effect is used to indicate indirect impacts of network growth [35, 36].

  4. Alternate representations of our model may be possible. For example, some may view the perception of risk and trust as being highly correlated. When our trust in a given person or situation is low, we feel risk; and when our trust is high, we don't. Thus, these two constructs could be represented as opposite ends on a scale for a single construct. Group-buying auctions are challenging to research in this regard. Consumers may prefer group-buying auctions but may not bid if there is an initiator whom they don’t trust. By the same token, they may trust the initiator but still may not believe that participating in a group-buying auction will be beneficial. Thus, simply treating risk and trust as highly correlated may not be meaningful for the setting that we chose to study. As a result, we chose to use perceived financial risk and perceived psychological risk to measure consumers’ perceived risk with bidding in a group-buying auction. This enables us to evaluate consumer attitudes toward the mechanism itself. In contrast, perceived trust would measure consumer trust in the auction initiator. We did not investigate the relationship between perceived risk and perceived trust. The purpose of doing so is different and also is beyond the scope of this study.

  5. We see similar treatment of these issues in research that is published in some of the top consumer behavior and marketing journals (e.g., the Journal of Consumer Research and the Journal of Marketing Research). They also usually use MANOVA and ANOVA as the main analysis methodologies due to the experimental research designs that they implement, and rarely use SEM. The interested reader should see some of the following articles as examples: Andrade and Iyer [2], Fitzsimons et al. [19], Gorn et al. [25], Griskevicius et al. [27], Labroo et al. [41], Li [46], Srivastava and Chakravarti [59], Thomas and Morwitz [61] and Zauberman et al. [64].

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Acknowledgments

An earlier version of this article was presented at the 2009 Hawaii International Conference on Systems Science, where it was nominated for a best paper award. We thank the co-chairs of the Electronic Marketing Mini-Track, Ajit Kambil, Arnold Kamis, Marios Koufaris and Bruce Weinberg, and three anonymous reviewers for helpful comments. We also benefited from input from Jian Chen, Zhangxi Lin, Erik Rolland, Chris Westland, Bin Wang, Juliana Tsai, YenChun Chou, David Weber, Ting Li, Paul Steinbart, Julie Smith-David, Angsana Techatassanasoontorn, and the participants of the 2007 Symposium on Electronic Commerce in China, held at the Carlson School of Management of the University of Minnesota in August 2007. Rob Kauffman acknowledges the W. P. Carey Chair, the Center for Advancing Business through Information Technology at the W. P. Carey School of Business, National Sun Yat-Sen University, and the Shidler School of Business at the University of Hawaii for generous support. Hsiangchu Lai’s research was partially supported by the “Aim for the Top University Plan” of National Sun Yat-sen University and the Ministry of Education, Taiwan, Republic of China. All errors are the sole responsibility of the authors.

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Authors and Affiliations

  1. W. P. Carey School of Business, Arizona State University, Tempe, AZ, 85287, USA

    Robert J. Kauffman

  2. College of Management, National Sun Yat-sen University, No. 70, Lienhai Rd., Kaohsiung, 80424, Taiwan

    Hsiangchu Lai & Huang-Chi Lin

Authors
  1. Robert J. Kauffman
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  2. Hsiangchu Lai
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  3. Huang-Chi Lin
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Correspondence to Hsiangchu Lai.

Appendices

Appendix A: Group-buying auction sites in Taiwan and the US, 2010

See Figs. 4, 5, 6, 7 and 8.

Fig. 4
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iHergo (www.ihergo.com) in Taiwan (as of February 3, 2010)

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Fig. 5
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DinBenDon! (www.dinbendon.net) in Taiwan (as of February 3, 2010)

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Fig. 6
figure 6

Zag (www.zag.com) in the US (as of February 3, 2010)

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Fig. 7
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Groupon (www.groupon.com) in the US (as of February 3, 2010)

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Fig. 8
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Pikaba (www.pikaba.com) in the US (as of February 3, 2010)

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Fig. 9
figure 9

The consumer purchase strategy for a 128 MB iDog in a group-buying auction. Note: This screenshot of the group-buying experimental test bed includes the original list price of the iDog at NT$1,800. It also shows the complete group-buying price curve for 1–5 bids at NT$1,700 for each item all the way down to 21 or more bids at NT$1,360 for each item. Item prices are inclusive of shipping fees, and the auction was open between April 30 and May 9, about ten days. The lower-middle buttons permit the consumer to: make a bid immediately (購買); think some more about participating (再考慮看看); and, to decline to bid (放棄購買). The opportunity for a consumer to decline to bid in an auction is an important feature of this experimental test bed that makes it more like the real world. Source: Electronic Commerce and Negotiation Support Systems Group, National Sun Yat-Sen University, Kaohsiung, Taiwan, 2009

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Appendix B: The design of the experiment

See Tables 7, 8, 9, 10, 11, 12 and Fig. 9.

Table 7 Pretest scores for positive, minor negative, and major negative textual comments
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Table 8 The manipulation of the price curve
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Table 9 The manipulation of the number of existing bids
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Table 10 The manipulation of the number of textual comments
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Table 11 The research design
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Table 12 Descriptive statistics of the experimental subjects
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Appendix C

See Table 13.

Table 13 Summary of measurement items used in the study (in a 7-point Likert-type scale)
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Kauffman, R.J., Lai, H. & Lin, HC. Consumer adoption of group-buying auctions: an experimental study. Inf Technol Manag 11, 191–211 (2010). https://doi.org/10.1007/s10799-010-0068-z

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