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Think, feel, bid: the impact of environmental conditions on the role of bidders’ cognitive and affective processes in auction bidding

Abstract

Environmental conditions and the interplay of cognitive and affective processes both exert influences on bidding behavior. This paper brings the above together, considering how the (external) auction environment determines the impact of (internal) cognitive and affective processes on bidding behavior, assessed in comparison to the optimal bid. Two aspects of the auction environment were considered, namely auction dynamics (low: first-price sealed-bid auction, high: Dutch auction) and value uncertainty (low, high). In a laboratory experiment, we assess bidders’ cognitive workload and emotional arousal through physiological measurements. We find that higher auction dynamics increase the impact of emotional arousal on bid deviations, but not that of cognitive workload. Higher value uncertainty, conversely, increases the impact of cognitive workload on bid deviations, but not that of emotional arousal. Taken together, the auction environment is a critical factor in understanding the nature of the underlying decision process and its impact on bids.

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Notes

  1. 1.

    In 2000, the UK government completed its first spectrum auction, raising £22.5 billion for five third-generation (3G) mobile wireless licenses (Binmore & Klemperer, 2002). In January 2015, the US Federal Communications Commission raised a record-breaking US$44.9 billion in its wireless spectrum auctions, http://goo.gl/eX47rv

  2. 2.

    In our study design, we deliberately keep value uncertainty constant across the range of where the true value is drawn. Other possibilities would be to vary the percentage range of drawing the private signals around the actual values, which is beyond the scope of the current work.

  3. 3.

    For measurement of heart rate, Ag/AgCl electrodes were connected to the Bioplux (2007) sensor system, and data was transmitted via Bluetooth and stored on the participants’ PC. For the psychophysiological measures of brain activity, a 32 channel EEG device (Actichamp, Version 2, by Brain Products) was used to record the electrical activity in the cerebral cortex of the brain.

  4. 4.

    Note that in the case of Dutch auctions, the regret information is automatically known to the bidder when the auction ends. If the bidder won the auction, he/she was shown the second (hypothetical) highest bid, amongst the computer agents. In the case of losing, he or she could see the price at which the Dutch clock stopped, hence revealing the winning bid. In order to have comparable treatments, the regret information (for both winning and losing cases) was shown explicitly to the bidder for FPSB auctions, 5 s after the auction result.

  5. 5.

    b = Regression coefficient, SE: Standard Error

References

  1. Adam, M. T. P., Gamer, M., Krämer, J., & Weinhardt, C. (2011a). Measuring emotions in electronic markets, Proceedings of the International Conference on Information Systems (ICIS). China: Shanghai.

    Google Scholar 

  2. Adam, M. T. P., Krämer, J., Jähnig, C., Seifert, S., & Weinhardt, C. (2011b). Understanding auction fever: A framework for emotional bidding. Electronic Markets, 21(3), 197–207. doi:10.1007/s12525-011-0068-9.

    Article  Google Scholar 

  3. Adam, M. T. P., Krämer, J., & Weinhardt, C. (2012). Excitement up! Price down! Measuring emotions in Dutch auctions. International Journal of Electronic Commerce, 17(2), 7–39. doi:10.2753/JEC1086-4415170201.

    Article  Google Scholar 

  4. Adam, M. T. P., Krämer, J., & Müller, M. B. (2015). Auction fever! How time pressure and social competition Affect bidders’ arousal and bids in retail auctions. Journal of Retailing, 91(3), 468–485.

    Article  Google Scholar 

  5. Agarwal, J., & Malhotra, N. K. (2005). An integrated model of attitude and affect. Journal of Business Research, 58(4), 483–493.

    Article  Google Scholar 

  6. Airy, G., Mullen, T., & Yen, J. (2009). Market based adaptive resource allocation for distributed rescue teams, In Proceedings of the 6th Conference on Information Systems for Crisis Response and Management (ISCRAM). Sweden: Gothenburg.

    Google Scholar 

  7. Ariely, D., & Simonson, I. (2003). Buying, bidding, playing, or competing? Value assessment and decision dynamics in online auctions. Journal of Consumer Psychology, 13(1), 113–123.

    Article  Google Scholar 

  8. Ariely, D., Ockenfels, A., & Roth, A. E. (2005). An experimental analysis of ending rules in Internet auctions. The Rand Journal of Economics, 36(4), 890–907.

    Google Scholar 

  9. Astor, P. J., Adam, M. T. P., Jähnig, C. C., & Seifert, S. (2011). Measuring regret: Emotional aspects of auction design. ECIS 2011 Proceedings, Paper 88.

  10. Astor, P. J., Adam, M. T. P., Jerčić, P., Schaaff, K., & Weinhardt, C. (2014). Integrating biosignals into Information Systems: A NeuroIS tool for improving emotion regulation. Journal of Management Information Systems, 30(3), 247–278.

    Article  Google Scholar 

  11. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173.

  12. Binmore, K., & Klemperer, P. (2002). The biggest auction ever: the sale of the British 3G telecom licences. The Economic Journal, 112(478), C74–C96.

    Article  Google Scholar 

  13. Bioplux (2007). Wireless Biosignals, PLUX - Systems Available at: http://www.plux.info/

  14. Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116–131.

    Article  Google Scholar 

  15. Camerer, C. F., Ho, T. H., & Chong, J. K. (2004). A cognitive hierarchy model of games. The Quarterly Journal of Economics, 119(3), 861–898. doi:10.1162/0033553041502225.

    Article  Google Scholar 

  16. Casari, M., Ham, J. C., & Kagel, J. H. (2007). Selection Bias, Demographic Effects, and Ability Effects in Common Value Auction Experiments. American Economic Review, 97(4), 1278–1304.

    Article  Google Scholar 

  17. Charland, P., Allaire-Duquette, G., & Léger, P. M. (2014). Collecting neurophysiological data to investigate users’ cognitive states during game play. Journal on Computing, 2(3), 20–24.

    Google Scholar 

  18. Clemons, E. K., & Weber, B. W. (1996). Alternative securities trading systems: tests and regulatory implications of the adoption of technology. Information Systems Research, 7(2), 163–188.

    Article  Google Scholar 

  19. Cowley, E. (2013). Forgetting the anxiety: Gamblers’ reactions to outcome uncertainty. Journal of Business Research, 66(9), 1591–1597. doi:10.1016/j.jbusres.2012.12.002.

    Article  Google Scholar 

  20. De Visser, ,. E., & Parasuraman, R. (2011). Adaptive aiding of human-robot teaming: Effects of imperfect automation on performance, trust, and workload. Journal of Cognitive Engineering and Decision Making, 5(2), 209–231.

    Article  Google Scholar 

  21. Engelbrecht-Wiggans, R., & Katok, E. (2008). Regret and feedback information in first-price sealed-bid auctions. Management Science, 54(4), 808–819. doi:10.1287/mnsc.1070.0806.

    Article  Google Scholar 

  22. Eriksson, K., & Sharma, D. (2003). Modeling uncertainty in buyer–seller cooperation. Journal of Business Research, 56(12), 961–970. doi:10.1016/S0148-2963(01)00331-9.

    Article  Google Scholar 

  23. Feigh, K. M., Dorneich, M. C., & Hayes, C. C. (2012). Toward a characterization of adaptive systems: A framework for researchers and system designers. Human Factors: The Journal of the Human Factors and Ergonomics Society, 54(6), 1008–1024. doi:10.1177/0018720812443983.

    Article  Google Scholar 

  24. Fernández, J. M., Augusto, J. C., Trombino, G., Seepold, R., & Madrid, N. M. (2013). Self-aware trader: A new approach to safer trading. Journal of Universal Computer Science, 19(15), 2292–2319.

    Google Scholar 

  25. Goeree, J. K., & Offerman, T. (2002). Efficiency in auctions with private and common values: An experimental study. American Economic Review, 92(3), 625–643.

    Article  Google Scholar 

  26. Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348–362.

    Article  Google Scholar 

  27. Ham, J. C., & Kagel, J. H. (2006). Gender effects in private value auctions. Economics Letters, 92(3), 375–382.

    Article  Google Scholar 

  28. Hariharan, A., & Adam, M. T. P. (2015). Blended emotion detection for decision support. IEEE Transactions on Human-Machine Systems, 45(4), 510–517. doi:10.1109/THMS.2015.2418231.

    Article  Google Scholar 

  29. Hariharan, A., Adam, M. T. P., Dorner, V., Lux, E., Müller, M. B., Pfeiffer, J., & Weinhardt, C. (2015). Brownie: A platform for conducting NeuroIS experiments. Available at SSRN: http://ssrn.com/abstract=2639047

  30. Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload. Amsterdam: North Holland Press.

    Google Scholar 

  31. Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. American Economic Review, 92(5), 1644–1655. doi:10.1257/000282802762024700.

    Article  Google Scholar 

  32. Kagel, J. H., & Dyer, D. (1988). Learning in common value auctions. In R. Tietz, W. Albert, & R. Selten (Eds.), Bounded Rational Behavior in Experimental Games and Markets (pp. 184–197). Heidelberg: Springer.

    Chapter  Google Scholar 

  33. Kagel, J. H., & Levin, D. (1986). The winner’s curse and public information in common value auctions. American Economic Review, 76(5), 894–920.

    Google Scholar 

  34. Kagel, J. H., & Levin, D. (2002). Common Value Auctions and the Winners Curse. Princeton N.J: Princeton University Press. ISBN: 9781400830138

  35. Kagel, J. H., Levin, D., Battalio, R. C., & Meyer, D. J. (1989). First-price common value auctions: Bidder behavior and the “winner’s curse”. Economic Inquiry, 27(2), 241–258.

    Article  Google Scholar 

  36. Kambil, A., & van Heck, E. (2004). Introduction to ‘Innovative Auction Markets’ Special Issue. Electronic Markets, 14(3), 166–169. doi:10.1080/101967804200045083.

    Article  Google Scholar 

  37. Katok, E., & Kwasnica, A. M. (2008). Time is money: The effect of clock speed on seller’s revenue in Dutch auctions. Experimental Economics, 11(4), 344–357. doi:10.1007/s10683-007-9169-x.

    Article  Google Scholar 

  38. Kroemer, C., Bichler, M., & Goetzendorff, A. (2016). (Un)expected bidder behavior in spectrum auctions. Group Decision and Negotiation, 25(1), 31–63. doi:10.1007/s10726-015-9431-0.

    Article  Google Scholar 

  39. Ku, G., Malhotra, D., & Murnighan, J. K. (2005). Towards a competitive arousal model of decision-making: A study of auction fever in live and Internet auctions. Organizational Behavior and Human Decision Processes, 96(2), 89–103. doi:10.1016/j.obhdp.2004.10.001.

    Article  Google Scholar 

  40. Lieberman, M. D. (2007). Social cognitive neuroscience: A review of core processes. Annual Review of Psychology, 58(1), 259–289. doi:10.1146/annurev.psych.58.110405.085654.

    Article  Google Scholar 

  41. Loewenstein, G. (2000). Emotions in economic theory and economic behavior. The American Economic Review, 90(2), 426–432. doi:10.1257/aer.90.2.426.

    Article  Google Scholar 

  42. Mann, L., & Tan, C. (1993). The hassled decision maker: The effects of perceived time pressure on information processing in decision making. Australian Journal of Management, 18(2), 197–209.

    Article  Google Scholar 

  43. McAfee, R. P., & McMillan, J. (1987). Auctions and bidding. Journal of Economic Literature, 25(2), 699–738.

    Google Scholar 

  44. Möllenberg, A. (2004). Internet auctions in marketing: The consumer perspective. Electronic Markets, 14(4), 360–371. doi:10.1080/10196780412331311793.

    Article  Google Scholar 

  45. Mousavi, S., & Gigerenzer, G. (2014). Risk, uncertainty, and heuristics. Journal of Business Research, 67(8), 1671–1678. doi:10.1016/j.jbusres.2014.02.013.

    Article  Google Scholar 

  46. Muthitachareon, A., Barut, M., & Saeed, K. A. (2014). The role of uncertainty stemming from product monetary value in online auctions: The case of search goods. International Journal of Electronic Commerce, 19(1), 65–98.

    Article  Google Scholar 

  47. Ortiz de Guinea, A., Titah, R., & Léger, P.-M. (2013). Measure for measure: A two study multi-trait multi-method investigation of construct validity in IS research. Computers in Human Behavior, 29(3), 833–844.

    Article  Google Scholar 

  48. Pappas, I. O., Kourouthanassis, P. E., Giannakos, M. N., & Chrissikopoulos, V. (2014). Shiny happy people buying: the role of emotions on personalized e-shopping. Electronic Markets, 24(3), 193–206.

    Article  Google Scholar 

  49. Pope, A. T., Bogart, E. H., & Bartolome, D. S. (1995). Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 40(1), 187–195. doi:10.1016/0301-0511(95)05116-3.

    Article  Google Scholar 

  50. Punj, G., & Moore, R. (2009). Information search and consideration set formation in a web-based store environment. Journal of Business Research, 62(6), 644–650. doi:10.1016/j.jbusres.2007.04.013.

    Article  Google Scholar 

  51. Riedl, R., Davis, F. D., & Hevner, A. R. (2014). Towards a NeuroIS research methodology: Intensifying the discussion on methods, tools, and measurement. Journal of the AIS, 15(1), i–xxxv.

  52. Rothkopf, M. H., & Harstad, R. M. (1994). Modeling competitive bidding: A critical essay. Management Science, 40(3), 364–384.

    Article  Google Scholar 

  53. Shivappa, S. T., Trivedi, M. M., & Rao, B. D. (2010). Audiovisual information fusion in human–computer interfaces and intelligent environments: A survey. Proceedings of the IEEE, 98(10), 1692–1715.

    Article  Google Scholar 

  54. Slovic, P., Finucane, M. L., Peters, E., & MacGregor, D. G. (2007). The affect heuristic. European Journal of Operational Research, 177(3), 1333–1352. doi:10.1016/j.ejor.2005.04.006.

    Article  Google Scholar 

  55. Smith, V. L. (1976). Experimental economics: Induced value theory. American Economic Review, 66(2), 274–279.

    Google Scholar 

  56. Smits, M., & Janssen, R. (2008). Impact of electronic auctions on health care markets. Electronic Markets, 18(1), 19–29. doi:10.1080/10196780701797607.

    Article  Google Scholar 

  57. Teubner, T., Adam, M. T. P., & Riordan, R. (2015). The impact of computerized agents on immediate emotions, overall arousal and bidding behavior in electronic auctions. Journal of the AIS, 16(10), 838–879.

    Google Scholar 

  58. Turel, O., Serenko, A., & Giles, P. (2011). Integrating technology addiction and use: An empirical investigation of online auction users. MIS Quarterly, 35(4), 1043–1051.

    Google Scholar 

  59. Van den Bos, W., Li, J., Lau, T., Maskin, E., Cohen, J. D., Montague, P. R., et al. (2008). The value of victory: Social origins of the winner’s curse in common value auctions. Judgment and Decision Making, 3(7), 483–492.

    Google Scholar 

  60. Van den Bos, W., Talwar, A., & McClure, S. M. (2013). Neural correlates of reinforcement learning and social preferences in competitive bidding. Journal of Neuroscience, 33(5), 2137–2146.

    Article  Google Scholar 

  61. Yin, P.-L. (2006). Information dispersion and auction prices. Social Science Research Network (SSRN) Working Paper Series. doi:10.2139/ssrn.690201.

    Google Scholar 

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Acknowledgments

The authors thank Ewa Lux for helpful comments on an earlier version of this paper, and Balaji Venugopal for the supportive inputs throughout. Moreover, they thank Kai Fuong for his untiring help with conducting the experiment. Financial support by the Young Investigator Group "Emotions in Market," funded by Karlsruhe Institute of Technology, is gratefully acknowledged.

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Correspondence to Anuja Hariharan.

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Appendices

Appendix 1

Detailed procedure for emotional arousal from measured heart rates

To operationalize emotional arousal (EA), heart rates of all participants were measured before the experiment (baseline heart rate) and also before each bid. In order to make heart rates comparable across participants, we normalized the heart rates prior to the bid by dividing each value by a participant’s baseline heart rate. For each participant and auction, this yields 20 normalized heart rate values, in the time interval of 3 to 1 s directly prior to the bid (in slots of .1 s each). Figure A.1 depicts the normalized heart rates for the 4 treatments. The differences indicate how the heart rate levels varied between treatments. Since these differences are minute in magnitude, the normalized heart rate values have then been fed into the factor analysis, to represent the variation between the different experiment conditions, as principal factors. EA of a bid was then calculated by reducing the 20 normalized heart rate values to a single factor using Principal Component Analysis with promax rotation (Shivappa et al., 2010). The last second before bid submission (E2) is not included in the computation of the factor, since this period typically constitutes the preparation of the imminent bidding action (Teubner et al., 2015). The PCA yields a standardized variable with mean 0 and standard deviation 1 for each combination of participant and auction, as shown in Table 1. The actual statistics of EA do not exactly match 0 and 1, generated by the PCA process. The mean value is −.022, standard deviation is .98. The small aberration stems from the fact that, while combining the dataset of heart rate measures with EEG, some observations had to be dropped due to EEG measurement errors.

Fig. 5
figure5

Normalized heart rates at bid submission (t = 0)

Figure 5 shows the normalized heart rates for the 4 treatments relative to bid submission (E2). The increase in heart rate prior to bidding is observed to be higher for high auction dynamics (Dutch auctions) than low auction dynamics (FPSB auctions), whereas the differences for different levels of value uncertainty are less pronounced.

Detailed procedure for computing cognitive workload from measured EEG activity

We followed the procedure of Pope et al. (1995). The EEG data was sampled at 500 Hz with a bandpass filter from 1 Hz to 40 Hz. The computation of cognitive workload index was executed in two steps: (1) Computation of independent components and (2) Computation of spectral powers in the above frequency bands. Independent components were computed using fast Independent Component Analysis (fastICA) algorithm available in the EEGLab toolbox for 14 frontal channels for each subject, and a Matlab script was developed to automate the process across all participants. Correspondingly, 14 independent components resulted, which were artefact cleaned for each subject by eyeball inspection, to remove components with possible eye, muscle movements or electrical interference. Next, power spectra were calculated for 2 s windows before each event, across each treatment type for 6 events of interest during the auction as shown in Fig. 3. The EEG cognitive workload index mirrors the theoretical definition of cognitive workload, taking into account the absolute and relative power spectra from 1 to 30 Hz of EEG channel. The spectral powers of each of the frequency bands (Beta, Alpha, and Theta) are calculated on the independent components obtained from 14 frontal channels, and then cognitive workload is computed by the formula (Beta/(Alpha + Theta)) spectral power (Pope et al., 1995).

Fig. 6:
figure6

1) Perceived cognitive workload (PCW), 2) Cognitive workload index (CW)

Figure 6 depicts the perceived cognitive workload and measured cognitive workload (PCW and CW as defined in Table 1) and shows that both perceived and measured cognitive workload indices were higher for high than for low value uncertainty auctions. The dimension of physical demand has been omitted from the NASA TLX, since it is not relevant for the sedentary auction task. PCW was higher for Dutch than for FPSB auction, CW was higher for FPSB than Dutch auctions. However, the difference in means is only marginal, and not significant.

Appendix 2

Supplementary analysis: 3-way-interaction regressions

Table 4 shows the results of two regression models with 3-way-interaction regressions including value uncertainty, auction dynamics, emotional arousal, cognitive workload, and all respective interaction terms as independent variables. The results are consistent with the findings in the Table 3.

Table 4 Auction dynamics (AD) and value uncertainty (VU) moderating the influence of emotional arousal (EA) and cognitive workload (CW) on bid deviations
Table 5 Mediation analysis: Do emotional arousal and cognitive workload mediate the influence of auction dynamics and value uncertainty on bid deviations

Supplementary analysis: mediation analysis

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Hariharan, A., Adam, M.T.P., Teubner, T. et al. Think, feel, bid: the impact of environmental conditions on the role of bidders’ cognitive and affective processes in auction bidding. Electron Markets 26, 339–355 (2016). https://doi.org/10.1007/s12525-016-0224-3

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Keywords

  • Arousal
  • Workload
  • Bidding
  • Heart rate
  • EEG

JEL Classification

  • D44
  • D87
  • L81