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
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
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.
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.
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.
b = Regression coefficient, SE: Standard Error
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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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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.
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).
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.
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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DOI: https://doi.org/10.1007/s12525-016-0224-3