Skip to main content
Springer Nature Link
Log in

Profit earning and monetary loss bidding in online entertainment shopping: the impacts of bidding patterns and characteristics

  • Research Paper
  • Published:
Electronic Markets Aims and scope Submit manuscript

Abstract

Online entertainment shopping has emerged as an innovative business model, integrating features of electronic commerce, auctions, games, and lotteries. Prior literature has rarely provided an understanding of the effects of electronic market factors on players’ bidding performance in entertainment shopping. We attempt to fill this research gap by analyzing how players’ bidding patterns and characteristics can affect bidding performance. An empirical study with 5650 players’ participation data collected from a leading entertainment shopping website is conducted. Results confirm that players’ bidding performance, including profit earning and monetary loss bidding, is strongly associated with bidding patterns and characteristics. Based on empirical findings, players loyal to the website contribute more profit to the website. The website should pay more attention to loyal players and strategically limit players that are good at bidding, in order to avoid losing and winning polarizations. Furthermore, players with different product preferences have different weights for profit and entertainment, and player preferences can be transformed into monetary value for the website.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. In order to relieve the burden of sunk cost, some websites offer special purchase options, e.g., a “buy-it-now” option, through which players can buy the auctioned product by paying the price difference between the retail price and its sunk cost, to encourage players to participate in the auction. We do not consider such options in this study.

  2. Wang et al. (2015) recruited two well-trained post-graduates that independently scrutinized wording descriptions, and disregarded samples in which the two post-graduates had inconsistent perceptions. Van Der Heide et al. (2013) trained five coders for coding product photograph types and established inter-coder reliability through comparing coding results between double-coded products. Following this spirit, we firstly presented definitions of “aggression signal” and “signal name” to the students, and then coded the variable based on three trained post-graduates’ independent coding results mentioned in the main text.

References

  • Adam, M. T. P., Krämer, J., Jähnig, C., Seifert, S., & Weinhardt, C. (2011). Understanding auction fever: A framework for emotional bidding. Electronic Markets, 21(3), 197–207.

    Article  Google Scholar 

  • Al-Gahtani, S. S., & King, M. (1999). Attitudes, satisfaction and usage: Factors contributing to each in the acceptance of information technology. Behaviour & Information Technology, 18(4), 277–297.

    Article  Google Scholar 

  • Anderson, R. E., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A contingency framework. Psychology and Marketing, 20, 123–138.

    Article  Google Scholar 

  • Ariely, D., & Simonson, I. (2003). Buying, bidding, playing, or competing? Value assessment and decision dynamics in online auctions. Journal of Consumer Psychology, 13, 113–123.

    Article  Google Scholar 

  • Arrow, K. J. (1958). Utilities, attitudes, choices: a review note. Econometrica, 26(1), 1–23.

    Article  Google Scholar 

  • Astonkar, M. R., & Buchade, A. (2015). Analytics: Building mobile game-users insights using parameter. International Journal of Computer Science and Mobile Computing, 4(6), 844–853.

    Google Scholar 

  • Augenblick, N. (2016). The sunk-cost fallacy in penny auctions. Review of Economic Studies, 83, 58–86.

    Article  Google Scholar 

  • Bao, Y., Bao, Y., & Sheng, S. (2011). Motivating purchase of private brands: Effects of store image, product signatureness, and quality variation. Journal of Business Research, 64(2), 220–226.

    Article  Google Scholar 

  • Byers, J. W., Mitzenmacher, M., & Zervas, G. (2010). Information asymmetries in penny auctions: How Swoopo makes bank (pp. 1–12). Cambridge: In Proceedings of the 11th ACM Conference on Electronic Commerce.

    Google Scholar 

  • Calinski, R. B., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communication in Statistics, 3, 1–27.

    Google Scholar 

  • Chan, K. H., Cheung, M. K., Shi, N., & Lee, K. O. (2015). Gender differences in satisfaction with Facebook users. Industrial Management & Data Systems, 115(1), 182–206.

    Article  Google Scholar 

  • Chen, J., Chen, X., Kauffman, R. J., & Song, X. (2009). Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions. Electronic Commerce Research and Applications, 8, 191–202.

    Article  Google Scholar 

  • Chen, Y., Katuščák, P., & Ozdenoren, E. (2013). Why can’t a woman bid more like a man? Games and Economic Behavior, 77, 181–213.

    Article  Google Scholar 

  • Choi, D., & Kim, J. (2004). Why people continue to play online games: In search of critical design factors to increase customer loyalty to online contents. Cyberpsychology & Behavior, 7, 11–24.

    Article  Google Scholar 

  • Chung, J. (2012). A general consumer preference model for experience products: application to internet recommendation services. Journal of Marketing Research, 49(3), 289–305.

    Article  Google Scholar 

  • Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67.

    Article  Google Scholar 

  • Cooper, D. J., & Fang, H. (2008). Understanding overbidding in second price auctions: An experimental study. The Economic Journal, 118(532), 1572–1595.

    Article  Google Scholar 

  • Currim, I. S., Mintz, O., & Siddarth, S. (2015). Information accessed or information available? The impact on consumer preference inferred at a durable product e-commerce website. Journal of International Marketing, 29, 11–25.

    Google Scholar 

  • Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227.

    Article  Google Scholar 

  • Deci, E. L., & Ryan, R. M. (1987). Accessibility and stability of predictors in the theory of planned behaviour. Journal of Personality and Social Psychology, 63(5), 754–765.

    Google Scholar 

  • Erdem, T., Keane, M. P., & Sun, B. (2008). A dynamic model of brand choice when price and advertising signal product quality. Marketing Science, 27(6), 1111–1125.

    Article  Google Scholar 

  • Escartin-Sorjonen, L. (2012). DealDash – Turning shopping into a game. The Huffington Post, June 14.

  • Feng, C., Fay, S., & Sivakumar, K. (2016). Overbidding in electronic auctions: Factors influencing the propensity to overbid and the magnitude of overbidding. Journal of the Academy of Marketing Science, 44(2), 241–260.

    Article  Google Scholar 

  • Flanagin, A. J., & Metzger, M. J. (2013). Trusting expert – versus user-generated rating online: The role of information volume, valence, and consumer characteristics. Computers in Human Behavior, 29, 1626–1634.

    Article  Google Scholar 

  • Ghose, A., & Han, S. P. (2011). A dynamic structural model of user learning on the mobile Internet. Working Paper. Leonard N. Stern School of Business, New York University. Available at http://people.stern.nyu.edu/aghose/research.html.

  • Gonçalves, R. (2008). Irrationality in English auctions. Journal of Economic Behavior & Organization, 67, 180–192.

    Article  Google Scholar 

  • Goodman, J. (2012). Reputations in bidding fee auctions. Working Paper.

  • Harridge-March, S. (2006). Can the building of trust overcome consumer perceived risk online? Marketing Intelligence & Planning, 24(7), 746–761.

    Article  Google Scholar 

  • Hasan, B. (2016). Perceived irritation in online shopping: the impact of website design characteristics. Computers in Human Behavior, 54, 224–230.

    Article  Google Scholar 

  • Heyman, J. E., Orhun, Y., & Ariely, D. (2004). Auction fever: The effect of opponents and quasi-endowment on product valuations. Journal of Interactive Marketing, 18(4), 7–21.

    Article  Google Scholar 

  • Hinnosaar, T. (2010). Penny auctions are unpredictable. Working Paper. Department of Economics, Northwestern University. Available at http://toomas.hinnosaar.net/.

  • Hsu, C. L., & Lu, H. P. (2007). Consumer behavior in online game communities: A motivational factor perspective. Computers in Human Behavior, 23(3), 1642–1659.

    Article  Google Scholar 

  • Iyengar, R., Ansari, A., & Gupta, S. (2007). A model of consumer learning for service quality and usage (pp. 529–544). XLIV: Journal of Marketing Research.

    Google Scholar 

  • Joo, M., Mazumdar, T., & Raj, S. P. (2012). Bidding strategies and consumer savings in NYOP auctions. Journal of Retailing, 88(1), 180–188.

    Article  Google Scholar 

  • Kakhbod, A. (2013). Pay-to-bid auctions: to bid or not to bid. Operations Research Letters, 41, 462–467.

    Article  Google Scholar 

  • Kieschnick, R., & McCullough, B. D. (2003). Regression analysis of variates observed on (0,1): Percentages, proportions and fractions. Statistical Modelling, 3, 193–213.

    Article  Google Scholar 

  • Kim, J., & Haridakis, P. M. (2009). The role of Internet user characteristics and motives in explaining three dimensions of Internet addiction. Journal of Computer-Mediated Communication, 14 (4), 988–1015.

  • Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 544–564.

  • Ku, G. (2000). Auctions and auction fever: explanations from competitive arousal and framing. Kellogg. Journal of Organizational Behavior, 1–32.

  • Lee, J., Lee, Y., & Lee, Y. (2012). Do customization programs of e-commerce companies lead to better relationship with consumers? Electronic Commerce Research and Applications, 11(3), 262–274.

    Article  Google Scholar 

  • Li, S., Srinivasan, K., & Sun, B. (2009). Internet auction features as quality signals. Journal of Marketing, 73, 75–92.

    Article  Google Scholar 

  • Lu, H., & Lin, J. C. (2003). Predicting customer behavior in the market-space: A study of Rayport and Sviokla’s framework. Information Management, 40, 1–10.

    Article  Google Scholar 

  • McGee, P. (2013). Bidding in private-value auctions with uncertain values. Games and Economic Behavior, 82, 312–326.

    Article  Google Scholar 

  • Murnighan, J. K. (2002). A very extreme case of the dollar auction. Journal of Management Education, 26, 56–69.

    Article  Google Scholar 

  • Narayanan, S., & Manchanda, P. (2009). Heterogeneous learning and the targeting of marketing communication for new products. Marketing Science, 28(3), 424–441.

    Article  Google Scholar 

  • Neugebauer, T., & Selten, R. (2006). Individual behavior of first-price auctions: The important of information feedback in computerized experimental markets. Games and Economic Behavior, 54(1), 183–204.

    Article  Google Scholar 

  • Ospina, R., & Ferrair, S. L. P. (2010). Inflated beta distributions. Statistical Papers, 51(1), 111–126.

    Article  Google Scholar 

  • Ospina, R., & Ferrair, S. L. P. (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics and Data Analysis, 56(6), 1609–1623.

    Article  Google Scholar 

  • Park, S. C., Keil, M., Kim, J. U., & Bock, G. (2012). Understanding overbidding behavior in C2C auctions: A escalation theory perspective. European Journal of Information Systems, 21, 643–663.

    Article  Google Scholar 

  • Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information Systems Research, 15(1), 37–59.

    Article  Google Scholar 

  • Platt, B. C., Price, J., & Tpaaen, H. (2013). The role of risk preferences in pay-to-bid auctions. Management Science, 59(9), 2117–2134.

    Article  Google Scholar 

  • Punj, G. (2015). The relationship between consumer characteristics and willingness to pay for general online content: implications for content providers considering subscription-based business models. Marketing Letters, 26, 175–186.

    Article  Google Scholar 

  • Reichheld, F. F., & Schefter, P. (2000). E-loyalty. Harvard Business Review, 78, 105–113.

    Google Scholar 

  • Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics, 20, 53–65.

    Article  Google Scholar 

  • Slovic, P. (1995). The construction of preference. American Psychologist, 50(5), 364–371.

    Article  Google Scholar 

  • Smithson, M., & Verkuilen, J. (2006). A better lemon squeezer? Maximum likelihood regression with beta-distributed dependent variables. Psychological Methods, 11(1), 54–71.

    Article  Google Scholar 

  • Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374.

    Article  Google Scholar 

  • Teng, C. I., & Chen, W. W. (2014). Team participation and online gamers loyalty. Electronic Commerce Research and Applications, 13(1), 24–31.

    Article  Google Scholar 

  • Van Der Heide, B., Johnson, B. K., & Vang, M. H. (2013). The effects of product photographs and reputation systems on consumer behavior and product cost on eBay. Computers in Human Behavior, 29, 570–576.

    Article  Google Scholar 

  • Vance, A., Elie-Dit-Cosaque, C., & Straub, D. W. (2008). Examining trust in information technology artifacts: The effects of system quality and culture. Journal of Management Information Systems, 24(4), 73–100.

    Article  Google Scholar 

  • Vellerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. Advance Experiment. Social Psychology, 29, 271–360.

    Google Scholar 

  • Walczak, S., Gregg, D. G., & Berrenberg, J. L. (2006). Market decision making for online auction sellers: Profit maximization or socialization. Journal of Electronic Commerce Research, 7(4), 199–220.

    Google Scholar 

  • Waldfogel, J., & Chen, L. (2006). Does information undermine brand? Information intermediary use and preference for branded web retailers. Journal of Industrial Economics, 54(4), 425–449.

    Article  Google Scholar 

  • Walther, J. B., & Parks, M. R. (2002). Cues filtered out, cues filtered in: Computer-mediated communication and relationships. In M. L. Knapp & J. A. Daly (Eds.), In Handbook of interpersonal communication (3rd ed., pp. 529–563). Thousand Oaks: CA, Sage.

    Google Scholar 

  • Wang, Z., & Xu, M. (2012). Learning and strategic sophistication in games: The case of penny auctions on the internet. Working Paper. Department of Economics, Georgetown University. Available at http://student.georgetown.edu/mx7/research.html.

  • Wang, C., Jin, X., Zhou, Z., Fang, Y., Lee, M. K. O., & Hua, Z. (2015). Effect of perceived media capability on status updates in microblogs. Electronic Commerce Research and Applications, 14, 181–191.

    Article  Google Scholar 

  • Whitmore, A., Agarwal, A., & Xu, L. (2015). The internet of things – A survey of topics and trends. Information Systems Frontiers, 17(2), 261–274.

    Article  Google Scholar 

  • Zacharia, G., & Maes, P. (2000). Trust management through reputation mechanisms. Applied Artificial Intelligence, 14, 881–907.

    Article  Google Scholar 

  • Zhang, Z., Lee, K. O., Cheung, M. K., & Chen, H. (2009). Understanding the role of gender in bloggers’ switching behavior. Decision Support Systems, 47(4), 540–546.

    Article  Google Scholar 

  • Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133–148.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the editor as well as the reviewers for providing helpful feedbacks on the refinement of the paper. Jin Li is grateful for support from the Chinese Fundamental Research Funds for the central universities under grants No. JB160611 and No. XJS16026.

Author information

Authors and Affiliations

  1. School of Economics and Management, Xidian University, Xi’an, China

    Jin Li

  2. Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR

    Kwok Fai Tso

  3. School of Public Policy and Management, Tsinghua University, Beijing, China

    Fangtao Liu

Authors
  1. Jin Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kwok Fai Tso
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Fangtao Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Jin Li.

Additional information

Responsible Editor: Charles W. Steinfield

Appendix

Appendix

Figure 6 shows the website’s daily profit obtained from each group member. The line for Paid Member (the dotted-black one) was almost completely matched with the sample of All Members (the solid-grey one). The line for Free Member (the solid-red one) was almost the same as the abscissa axis, with a few points less than 0, as some Free Member might achieve a large savings by winning a product through free auctions. For example, on the 26th day, the website lost $27 to Free Member and earned $608 from Paid Member. Hence, the total website’s profit was $581 on that day.

Fig. 1
figure 1

Research model

Full size image
Fig. 2
figure 2

Daily active players

Full size image
Fig. 3
figure 3

Examples of players' bidding distributions

Full size image
Fig. 4
figure 4

Average values for the CHPF, DBI, and SV criteria of each k

Full size image
Fig. 5
figure 5

Auction participation proportion for each cluster

Full size image
Fig. 6
figure 6

Website’s daily profitr

Full size image

While the group of 4778 Free Member had no monetary loss in auction bidding and contributed no positive monetary profit to the website, it was inappropriate for hypothesis testing related to “monetary loss bidding” and “profit contribution”. Empirical analysis for hypothesis 2 (i.e., players sending aggressive signals through signal names obtain more profit from entertainment shopping websites) with the Free Member dataset can be conducted. For the sample of Free Member, we compared the mean player profit for the group with an aggressive signal name (mean = 0.98, s.d. = 3.176) and the group without aggressive signal names (mean = 0.38, s.d. = 5.819) through a t-test with a t statistic of 1.067. The results indicate no significant difference between the two groups and neither support H2.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Tso, K.F. & Liu, F. Profit earning and monetary loss bidding in online entertainment shopping: the impacts of bidding patterns and characteristics. Electron Markets 27, 77–90 (2017). https://doi.org/10.1007/s12525-016-0235-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12525-016-0235-0

Keywords

Jel Classification