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When the eyes say buy: visual fixations during hypothetical consumer choice improve prediction of actual purchases

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Abstract

Consumers typically overstate their intentions to purchase products, compared to actual rates of purchases, a pattern called “hypothetical bias”. In laboratory choice experiments, we measure participants’ visual attention using mousetracking or eye-tracking, while they make hypothetical as well as real purchase decisions. We find that participants spent more time looking both at price and product image prior to making a real “buy” decision than making a real “don’t buy” decision. We demonstrate that including such information about visual attention improves prediction of real buy decisions. This improvement is evident, although small in magnitude, using mousetracking data, but is not evident using eye-tracking data.

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Notes

  1. There is only one study directly comparing results from both measures on a common task (Lohse and Johnson 1996).

  2. Only male subjects were recruited, because it is desirable to have a set of consumer goods for which preferences of the subjects are not too different.

  3. Two additional subjects participated in Experiment M, but their data were excluded from the analysis (online appendix A.3).

  4. By design, participants faced exactly the same product-price pairs in the hypothetical and surprise real blocks. In the absence of hypothetical bias, subjects should make the same decisions between these two blocks. Note that subjects responded to prices to some extent even in the hypothetical block (see online appendix B.1).

  5. We also examine “spatial gaze distribution maps” using gaze data from Experiment E, and obtain qualitatively similar results (online appendix B.3).

  6. There was no clear-cut way to measure latency as defined above in Experiment E unless subjects actually closed their eyes after the last fixation until the decision submission. Hence, we used the total duration of the gaze at blank areas instead.

References

  • Berns, G. S., Bell, E., Capra, C. M., Prietula, M. J., Moore, S., Anderson, B., et al. (2012). The price of your soul: Neural evidence for the non-utilitarian representation of sacred values. Philosophical Transactions of the Royal Society B: Biological Sciences, 367, 754–762.

    Article  Google Scholar 

  • Brocas, I., Carrillo, J. D., Wang, S. W., & Camerer, C. F. (2014). Imperfect choice or imperfect attention? Understanding strategic thinking in private information games. Review of Economic Studies, 81, 944–970.

    Article  Google Scholar 

  • Camerer, C. F., Johnson, E., Rymon, T., & Sen, S. (1993). Cognition and framing in sequential bargaining for gains and losses. In K. Binmore, A. Kirman, & P. Tani (Eds.), Frontiers of game theory (pp. 27–47). Cambridge: MIT Press.

    Google Scholar 

  • Carson, R. T., & Hanemann, W. M. (2005). Contingent valuation. In K.-G. Mäler & J. Vincent (Eds.), Handbook of environmental economics (Vol. 2, pp. 821–936). New York: Elsevier.

    Google Scholar 

  • Costa-Gomes, M., Crawford, V. P., & Broseta, B. (2001). Cognition and behavior in normal-form games: An experimental study. Econometrica, 69, 1193–1235.

    Article  Google Scholar 

  • Cummings, R. G., Harrison, G. W., & Rutström, E. E. (1995). Homegrown values and hypothetical surveys: Is the dichotomous choice approach incentive-compatible? American Economic Review, 85, 260–266.

    Google Scholar 

  • FeldmanHall, O., Mobbs, D., Evans, D., Hiscox, L., Navrady, L., & Dalgleish, T. (2012). What we say and what we do: The relationship between real and hypothetical moral choices. Cognition, 123, 434–441.

    Article  Google Scholar 

  • Gabaix, X., Laibson, D., Moloche, G., & Weinberg, S. (2006). Costly information acquisition: Experimental analysis of a boundedly rational model. American Economic Review, 96, 1043–1068.

    Article  Google Scholar 

  • Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54, 3–19.

    Article  Google Scholar 

  • Jamieson, L. F., & Bass, F. M. (1989). Adjusting stated intention measures to predict trial purchase of new products: A comparison of models and methods. Journal of Marketing Research, 26, 336–345.

    Article  Google Scholar 

  • Johnson, E. J., Camerer, C. F., Sen, S., & Rymon, T. (2002). Detecting failures of backward induction: Monitoring information search in sequential bargaining. Journal of Economic Theory, 104, 16–47.

    Article  Google Scholar 

  • Knoepfle, D. T., Wang, J. T.-Y., & Camerer, C. F. (2009). Studying learning in games using eye-tracking. Journal of the European Economic Association, 7, 388–398.

    Article  Google Scholar 

  • Konovalov, A., & Krajbich, I. (2017). Revealed indifference: Using response times to infer preferences. Available at SSRN: https://doi.org/10.2139/ssrn.3024233.

  • Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13, 1292–1298.

    Article  Google Scholar 

  • Krajbich, I., Lu, D., Camerer, C., & Rangel, A. (2012). The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology, 3, 193.

    Article  Google Scholar 

  • Krajbich, I., & Rangel, A. (2011). Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proceedings of the National Academy of Sciences, 108, 13852–13857.

    Article  Google Scholar 

  • List, J. A., & Gallet, C. A. (2001). What experimental protocol influence disparities between actual and hypothetical stated values? Environmental and Resource Economics, 20, 241–254.

    Article  Google Scholar 

  • Little, J., & Berrens, R. (2004). Explaining disparities between actual and hypothetical stated values: Further investigation using meta-analysis. Economics Bulletin, 3, 1–13.

    Google Scholar 

  • Lohse, G. L., & Johnson, E. J. (1996). A comparison of two process tracing methods for choice tasks. Organizational Behavior and Human Decision Processes, 68, 28–43.

    Article  Google Scholar 

  • Murphy, J. J., Allen, P. G., Stevens, T. H., & Weatherhead, D. (2005). A meta-analysis of hypothetical bias in stated preference valuation. Environmental and Resource Economics, 30, 313–325.

    Article  Google Scholar 

  • Reutskaja, E., Nagel, R., Camerer, C. F., & Rangel, A. (2011). Search dynamics in consumer choice under time pressure: An eye-tracking study. American Economic Review, 101, 900–926.

    Article  Google Scholar 

  • Shogren, J. F. (2005). Experimental methods and valuation. In K.-G. Maler & J. Vincent (Eds.), Handbook of environmental economics (Vol. 2, pp. 969–1027). New York: Elsevier.

    Google Scholar 

  • Silk, A. J., & Urban, G. L. (1978). Pre-test-market evaluation of new packaged goods: A model and measurement methodology. Journal of Marketing Research, 15, 171–191.

    Article  Google Scholar 

  • Wang, J. T.-Y., Spezio, M., & Camerer, C. F. (2010). Pinocchio’s pupil: Using eyetracking and pupil dilation to understand truth telling and deception in sender–receiver games. American Economic Review, 100, 984–1007.

    Article  Google Scholar 

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Correspondence to Taisuke Imai.

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This paper is based on part of Ph.D. dissertation of Taisuke Imai. Authors thank Daw-An Wu and Rahul Bhui for their help with data collection and analysis. This work was supported by the Gordon and Betty Moore Foundation. Imai acknowledges financial support from the Nakajima Foundation and Deutsche Forschungsgemeinschaft through CRC TRR 190.

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Imai, T., Kang, M.J. & Camerer, C.F. When the eyes say buy: visual fixations during hypothetical consumer choice improve prediction of actual purchases. J Econ Sci Assoc 5, 112–122 (2019). https://doi.org/10.1007/s40881-019-00071-3

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  • DOI: https://doi.org/10.1007/s40881-019-00071-3

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