Measuring the Factors Influencing Purchasing Decisions: Evidence From Cursor Tracking and Cognitive Modeling

Published Online:https://doi.org/10.1287/mnsc.2022.4598

References

  • Anderson NH (1973) Serial position curves in impresion formation. J. Exp. Psychol. 97(1):8–12.CrossrefGoogle Scholar
  • Ariely D, Loewenstein G, Prelec D (2003) ‘Coherent arbitrariness’: Stable demand curves without stable preferences. Q. J. Econom. 118(1):73–106.CrossrefGoogle Scholar
  • Armel C, Beaumel A, Rangel A (2008) Biasing simple choices by manipulating relative visual attention. Judgm. Decis. Mak. 3(5):396–403.CrossrefGoogle Scholar
  • Bartels DM, Urminsky O (2015) To know and to care: How awareness and valuation of the future jointly shape consumer spending. J. Consumer Res. 41:1469–1485.CrossrefGoogle Scholar
  • Becker G, DeGroot M, Marschak J (1964) Measuring utility by a single-response sequential method. Behav. Sci. 9:226–232.Google Scholar
  • Brainard D (1997) The psychophysics toolbox. Spatial Vision 10(4):433–436.Google Scholar
  • Britten KH, Shadlen MN, Newsome WT, Movshon JA (1992) The analysis of visual motion: A comparison of neuronal and psychophysical performance. J. Neurosci. 12(12):4745–4765.CrossrefGoogle Scholar
  • Bronnenberg BJ, Dubé JP, Moorthy S (2019) The economics of brands and branding. Dubé JP, Rossi PE, eds. The Handbook of the Economics of Marketing (Elsevier, Amsterdam).CrossrefGoogle Scholar
  • Bruine de Bruin W (2005) Save the last dance for me: Unwanted serial position effects in jury evaluations. Acta Psychol. (Amst.) 118(3):245–260.CrossrefGoogle Scholar
  • Buc Calderon C, Dewulf M, Gevers W, Verguts T (2017) Continuous track paths reveal additive evidence integration in multistep decision making. Proc. Natl. Acad. Sci. USA 114(40):10618–10623.CrossrefGoogle Scholar
  • Busemeyer JR, Diederich A (2002) Survey of decision field theory. Math. Social Sci. 43:345–370.CrossrefGoogle Scholar
  • Busemeyer JR, Townsend J (1993) Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychol. Rev. 100:432–459.CrossrefGoogle Scholar
  • Camerer CF (2015) The promise and success of laboratory–field generalizability in experimental economics: A critical reply to Levitt and List. Frechette, GR, Schotter, A, eds. Handbook of Experimental Economic Methodology (Oxford Scholarship Online, Oxford, UK).CrossrefGoogle Scholar
  • Camerer CF, Mobbs D (2017) Differences in behavior and brain activity during hypothetical and real choices. Trends Cogn. Sci. 21(1):46–56.CrossrefGoogle Scholar
  • Cavanagh JF, Wiecki TV, Kochar A, Frank MJ (2014) Eye tracking and pupillometry are indicators of dissociable latent decision processes. J. Exp. Psychol. Gen. 143(4):1476–1488.CrossrefGoogle Scholar
  • Chakravarti A, Grenville A, Morwitz VG, Tang J, Ulkumen G (2013) Malleable conjoint partworths: How the breadth of response scales alters price sensitivity. J. Consumer Psychol. 23:515–525.CrossrefGoogle Scholar
  • Chandon P, Hutchinson JW, Bradlow ET, Young SH (2009) Does in-store marketing work? Effects of the number and position of shelf facings on brand attention and evaluation at the point of purchase. J. Marketing 73(6):1–17.CrossrefGoogle Scholar
  • Chapman CS, Gallivan JP, Wood DK, Milne JL, Culham JC, Goodale MA (2010) Reaching for the unknown: Multiple target encoding and real-time decision-making in a rapid reach task. Cognition 116(2):168–176.CrossrefGoogle Scholar
  • Chen L, Pu P (2010) Eye-tracking study of user behavior in recommender interfaces. International Conference on User Modeling, Adaptation, and Personalization, 375–380.Google Scholar
  • Cheng J, Gonzalez-Vallejo C (2015) Action dynamics in intertemporal choice reveal different facets of decision process. J. Behav. Decis. Making 30(1):107–122.CrossrefGoogle Scholar
  • Chiong K, Shum M, Webb R, Chen R (2019) Forced Attention in the Field: Combining Choices and Response Times for Mobile Advertisements. Working paper.Google Scholar
  • Diederich A (1997) Dynamic stochastic models for decision making under time constraints. J. Math. Psych. 41(3):260–274.CrossrefGoogle Scholar
  • Diederich A, Trueblood JS (2018) A dynamic dual process model of risky decision making. Psychol. Rev. 125(2):270–292.CrossrefGoogle Scholar
  • Ding M (2007) An incentive-aligned mechanism for conjoint analysis. J. Marketing Res. 44(2):214–223.CrossrefGoogle Scholar
  • Ding M, Grewal R, Liechty J (2005) Incentive-aligned conjoint analysis. J. Marketing Res. 42(1):67–82.CrossrefGoogle Scholar
  • Ding M, Park Y-H, Bradlow ET (2009) Barter markets for conjoint analysis. Management Sci. 55(6):1003–1017.Google Scholar
  • Dotan D, Dehaene S (2013) How do we convert a number into a finger trajectory? Cognition 129(3):512–529.CrossrefGoogle Scholar
  • Dotan D, Meyniel F, Dehaene S (2018) On-line confidence monitoring during decision making. Cognition 171:112–121.CrossrefGoogle Scholar
  • Dotan D, Pinherio-Chagas P, Al Roumi F, Dehaene S (2019) Track it to crack it: Dissecting processing stages with finger-tracking. Trends Cogn. Sci. 23(12):1058–1070.CrossrefGoogle Scholar
  • Feldman JM, Lynch JG Jr (1988) Self-generated validity and other effects of measurement on belief, attitude, intention, and behavior. J. Appl. Psychol. 73(3):421–435.CrossrefGoogle Scholar
  • Fisher G (2017) An attentional drift diffusion model over binary-attribute choice. Cognition 168:34–45.Google Scholar
  • Freeman JB, Ambady N (2010) MouseTracker: Software for studying real-time mental processing using a computer mouse-tracking method. Behav. Res. Methods 42:226–241.CrossrefGoogle Scholar
  • Friedman J, Brown S, Finkbeiner M (2013) Linking cognitive and reaching trajectories via intermittent movement control. J. Math. Psych. 57(3-4):140–151.CrossrefGoogle Scholar
  • Frydman C, Krajbich I (2022) Using response times to infer others’ beliefs: An application to information cascades. Management Sci. 68(4):2377–3174.Google Scholar
  • Frydman C, Nave G (2017) Extrapolative beliefs in perceptual and economic decisions: Evidence of a common mechanism. Management Sci. 63(7):2340–2352.LinkGoogle Scholar
  • Fudenberg D, Strack P, Strzalecki T (2018) Speed, accuracy, and the optimal timing of choices. Amer. Econom. Rev. 108:3651–3684.CrossrefGoogle Scholar
  • Gallivan JP, Chapman CS (2014) Three-dimensional reach trajectories as a probe of real-time decision making between multiple competing targets. Front. Neurosci. 8:215.CrossrefGoogle Scholar
  • Genevsky A, Knutson B (2015) Neural affective mechanisms predict market-level microlending. Psychol. Sci. 26(9):1411–1422.CrossrefGoogle Scholar
  • Genevsky A, Yoon C, Knutson B (2017) When brain beats behavior: neuroforecasting crowdfunding outcomes. J. Neurosci. 37(36):8625–8634.CrossrefGoogle Scholar
  • Gold JI, Shadlen MN (2007) The neural basis of decision making. Annu. Rev. Neurosci. 30:535–574.CrossrefGoogle Scholar
  • Hare TA, Schultz W, Camerer CF, O’Doherty JP, Rangel A (2011) Transformation of stimulus value signals into motor commands during simple choice. Proc. Natl. Acad. Sci. USA 108(44):18120–18125.CrossrefGoogle Scholar
  • Häubl G, Dellaert BGC, Donkers B (2010) Tunnel vision: Local behavioral influences on consumer decisions in product search. Marketing Sci. 29(3):438–455.LinkGoogle Scholar
  • Heekeren HR, Marrett S, Bandettini PA, Ungerleider LG (2008) A general mechanism for perceptual decision-making in the human brain. Nature 431:859–862.CrossrefGoogle Scholar
  • Hendrick C, Costantini AF (1970) Effects of varying trait inconsistency and response requirements on the primacy effect in impression formation. J. Personality Soc. Psychol. 15(2):158–164.CrossrefGoogle Scholar
  • Huang MY, Kuo F (2011) An eye-tracking investigation of Internet consumers’ decision deliberateness. Internet Res. 21(5):541–561.CrossrefGoogle Scholar
  • Johnson EJ, Schkade DA (1989) Bias in utility assessments: further evidence and explanations. Management Sci. 35(4):406–424.LinkGoogle Scholar
  • Johnson EJ, Häubl G, Keinan A (2007) Aspects of endowment: A query theory of value construction. J. Exp. Psychol. Learn. Mem. Cogn. 33(3):461–474.CrossrefGoogle Scholar
  • Kahneman D, Frederick S (2002) Representativeness revisited: Attribute substitution in intuitive judgment. Gilovich T, Griffin D, Kahneman D, eds. Heuristics and Biases: The Psychology of Intuitive Judgment. (Cambridge University Press, New York), 49–81.CrossrefGoogle Scholar
  • Karmarkar UR, Yoon C (2016) Consumer neuroscience: Advances in understanding consumer psychology. Curr. Opin. Psychol. 10:160–165.CrossrefGoogle Scholar
  • Karmarkar UR, Shiv B, Knutson B (2015) Cost conscious? The neural and behavioral impact of price primacy on decision making. J. Marketing Res. 52:467–481.CrossrefGoogle Scholar
  • Kaul A, Wittink DR (1995) Empirical generalizations about the impact of advertising on price sensitivity and price. Marketing Sci. 14(3):G151–G160.LinkGoogle Scholar
  • Kieslich PJ, Henninger F, Wulff DU, Haslbeck JMB, Schulte-Mecklenbeck M (2019) Mouse-tracking: A practical guide to implementation and analysis. Schulte-Mecklenbeck M, Kühberger A, Johnson JG, eds. A Handbook of Process Tracing Methods. (Routledge, New York), 111–130.CrossrefGoogle Scholar
  • Konovalov A, Krajbich I (2016) Gaze data reveal distinct choice processes underlying model-based and model-free reinforcement learning. Nat. Commun. 7:12438.CrossrefGoogle Scholar
  • Krajbich I, Rangel A (2011) A multi-alternative drift diffusion model predicts the relationship between visual fixations and choice in value-based decisions. Proc. Natl. Acad. Sci. USA. 108:13853–13857.CrossrefGoogle Scholar
  • Krajbich I, Armel C, Rangel A (2010) Visual fixations and comparison of value in simple choice. Nat. Neurosci. 13:1292–1298.CrossrefGoogle Scholar
  • Li Y, Epley N (2009) When the best appears to be saved for last: Serial position effects on choice. J. Behav. Decis. Making. 22(4):378–389.CrossrefGoogle Scholar
  • Lichtenstein S, Slovic P (2006) The construction of preference: An overview. Lichtenstein S, Slovic P, eds. The Construction of Preference (Cambridge University Press, Cambridge, UK), 1–40.CrossrefGoogle Scholar
  • Lieberman MD (2003) Reflective and reflexive judgment processes: A social cognitive neuroscience approach. Forgas JP, Williams KR, von Hippel W, eds. Social Judgments: Implicit and Explicit Processes (Cambridge University Press, New York), 44–67.Google Scholar
  • Lim S, Penrod MT, Ha O, Bruce JM, Bruce AS (2018) Calorie labeling promotes dietary self-control by shifting the temporal dynamics of health- and taste-attribute integration in overweight individuals. Psychol. Sci. 29(3):447–462.CrossrefGoogle Scholar
  • Maier SU, Beharelle AR, Polania R, Ruff CC, Hare TA (2020) Dissociable mechanisms govern when and how strongly reward attributes affect decisions. Nat. Hum. Behav. 4:949–963.CrossrefGoogle Scholar
  • Mantonakis A, Rodero P, Lesschaeve I, Hastie R (2009) Order in choice effects of serial position on preferences. Psychol. Sci. 20(11):1309–1312.CrossrefGoogle Scholar
  • McKinstry C, Dale R, Spivey MJ (2008) Action dynamics reveal parallel competition in decision making. Psychol. Sci. 19(1):22–24.CrossrefGoogle Scholar
  • Mitra A, Lynch JG (1995) Toward a reconciliation of market power and information theories of advertising effects on price elasticity. J. Consumer Res. 21(4):644–659.CrossrefGoogle Scholar
  • Mrkva K, Westfall J, Van Boven J (2019) Attention drives emotion: Voluntary visual attention increases perceived emotional intensity. Psychol. Sci. 30(6):942–954.CrossrefGoogle Scholar
  • Orquin JL, Mueller Loose S (2013) Attention and choice: A review on eye movements in decision making. Acta Psychol. (Amst.). 144(1):190–206.CrossrefGoogle Scholar
  • Page M, Norris D (1998) The primacy model: A new model of immediate serial recall. Psychol. Rev. 105(4):761–781.CrossrefGoogle Scholar
  • Pärnamets P, Johansson P, Hall L, Balkenius C, Spivey MJ, Richardson DC (2015) Biasing moral decisions by exploiting the dynamics of eye gaze. Proc. Natl. Acad. Sci. USA 112(13):4170–4175.CrossrefGoogle Scholar
  • Plassmann H, O’Doherty JP, Shiv B, Rangel A (2008) Marketing actions can modulate neural representations of experienced pleasantness. Proc. Natl. Acad. Sci. USA 105(3):1050–1054.CrossrefGoogle Scholar
  • Plassmann H, Venkatraman V, Huettel S, Yoon C (2015) Consumer neuroscience: Applications, challenges, and possible solutions. J. Marketing Res. 52(4):427–435.CrossrefGoogle Scholar
  • Rangel A, Clithero JA (2014) The computation of stimulus values in simple choice. Glimcher P, Fehr E, eds. Neuroeconomics: Decision-Making and the Brain, 2nd ed. (Elsevier, Oxford, UK), 125–147.CrossrefGoogle Scholar
  • Ratcliff R (1978) A theory of memory retrieval. Psychol. Rev. 85:59–108.CrossrefGoogle Scholar
  • Ratcliff R, Smith P (2004) A comparison of sequential sampling models for two-choice reaction time. Psychol. Rev. 111:333–367.CrossrefGoogle Scholar
  • Ratcliff R, Cherian A, Segraves M (2003) A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions. J. Neurophysiol. 90:1392–1407.CrossrefGoogle Scholar
  • Ratcliff R, Smith PL, Brown SD, McKoon G (2016) Diffusion decision model: Current issues and history. Trends Cogn. Sci. 20(4):260–281.CrossrefGoogle Scholar
  • Reeck C, Wall D, Johnson EJ (2017) Search predicts and changes patience in intertemporal choice. Proc. Natl. Acad. Sci. USA 114(45):11890–11895.CrossrefGoogle Scholar
  • Roe RM, Busemeyer JR, Townsend JT (2001) Multialternative decision field theory: A dynamic connectionist model of decision making. Psychol. Rev. 108(2):370–392.CrossrefGoogle Scholar
  • Russo JE, Meloy MG, Medvec VH (1998) Predecisional distortion of product information. J. Marketing Res. 35:438–452.CrossrefGoogle Scholar
  • Scherbaum S, Dshemuchadse M, Fischer R, Goschke T (2010) How decisions evolve: The temporal dynamics of action selection. Cognition 115(3):407–416.CrossrefGoogle Scholar
  • Schneider W, Shiffrin RM (1977) Controlled and automatic human information processing: I. Detection, search, and attention. Psychol. Rev. 84(1):1–66.CrossrefGoogle Scholar
  • Schulte-Mecklenbeck M, Johnson JG, Bockenholt U, Goldstein DG, Russo JE, Sullivan NJ, Willemsen M (2017) Process-tracing methods in decision making: On growing up in the 70s. Curr. Dir. Psychol. Sci. 26(5):442–450.CrossrefGoogle Scholar
  • Shen L, Urminsky O (2013) Making sense of nonsense: The visual salience of units determines sensitivity to magnitude. Psychol. Sci. 24(3):297–304.CrossrefGoogle Scholar
  • Shi SW, Wedel M, Pieters FGM (2013) Information acquisition during online decision making: A model-based exploration using eye-tracking data. Management Sci. 59(5):1009–1026.LinkGoogle Scholar
  • Shimojo S, Simion S, Shimojo E, Sheier C (2003) Gaze bias both reflects and influences preference. Nat. Neurosci. 6:1317–1322.CrossrefGoogle Scholar
  • Simonson I (1989) Choice based on reasons: The case of attraction and compromise effects. J. Consum. Res. 16(2):158–174.CrossrefGoogle Scholar
  • Simonson I, Tversky A (1992) Choice in context: Tradeoff contrast and extremeness aversion. J. Marketing Res. 29(3):281–295.CrossrefGoogle Scholar
  • Slovic P (1995) The construction of preference. Am. Psychol. 50(5):364–371.CrossrefGoogle Scholar
  • Smith SM, Krajbich I (2018) Gaze amplifies value in decision making. Psychol. Sci. 30(1):116–128.CrossrefGoogle Scholar
  • Song JH, Nakayama K (2009) Hidden cognitive states revealed in choice reaching tasks. Trends Cogn. Sci. 13(8):360–366.CrossrefGoogle Scholar
  • Stewart N, Hermens F, Matthews WJ (2016) Eye movements in risky choice. J. Behav. Decis. Making 29:116–136.CrossrefGoogle Scholar
  • Stillman PE, Ferguson MJ (2019) Decisional conflict predicts impatience. J. Assoc. Consum. Res. 4(1):47–56.CrossrefGoogle Scholar
  • Stillman PE, Krajbich I, Ferguson MJ (2020) Using dynamic monitoring of choices to predict and understand risk preferences. Proc. Natl. Acad. Sci. USA 117(50):31738–31747.CrossrefGoogle Scholar
  • Stillman PE, Shen X, Ferguson MJ (2018) How mouse-tracking can advance social cognitive theory. Trends Cogn. Sci. 22(6):531–543.CrossrefGoogle Scholar
  • Sullivan NJ, Huettel SA (2021) Healthful choices depend on the latency and rate of information accumulation. Nat. Hum. Behav. 5(12):1698–1706.Google Scholar
  • Sullivan NJ, Hutcherson C, Harris A, Rangel A (2015) Dietary self-control is related to the speed with which attributes of healthfulness and tastiness are processed. Psychol. Sci. 26(2):122–134.CrossrefGoogle Scholar
  • Sütterlin B, Brunner TA, Opwis K (2008) Eye-tracking the cancellation and focus model for preference judgments. J. Exp. Soc. Psychol. 44(3):904–911.CrossrefGoogle Scholar
  • Tavares G, Perona P, Rangel A (2017) The attentional drift diffusion model of simple perceptual decision-making. Front. Neurosci. 11:468.CrossrefGoogle Scholar
  • Toubia O, De Jong MG, Stieger D, Fuller J (2012) Measuring consumer preferences using conjoint poker. Marketing Sci. 31(1):138–156.LinkGoogle Scholar
  • Towal R, Mormann M, Koch C (2013) Simultaneous modeling of visual saliency and value computation improves predictions of economic choice. Proc. Natl. Acad. Sci. USA. 110:3858–3867.CrossrefGoogle Scholar
  • Tully S, Meyvis T (2016) Questioning the end effect: Endings are not inherently over-weighted in retrospective evaluations of experiences. J. Exp. Psychol. Gen. 145(5):630–642.CrossrefGoogle Scholar
  • Tversky A, Kahneman D (1974) Judgment under uncertainty: Heuristics and biases. Science. 185(4157):1124–1131.CrossrefGoogle Scholar
  • Webb R (2019) The (neural) dynamics of stochastic choice. Management Sci. 65:230–255.LinkGoogle Scholar
  • Weber EU, Johnson EJ, Milch KF, Chang H, Brodscholl JC, Goldstein DG (2007) Asymmetric discounting in intertemporal choice: A query‐theory account. Psychol. Sci. 18:516–523.CrossrefGoogle Scholar
  • Wedel M, Pieters R (2000) Eye fixations on advertisements and memory for brands: A model and findings. Marketing Sci. 19(4):297–312.LinkGoogle Scholar
  • Willemsen MC, Johnson EJ (2011) Visiting the decision factory: Observing cognition with MouselabWEB and other information acquisition methods. Schulte-Mecklenbeck M, Kühberger A, Ranyard R, eds. A Handbook of Process Tracing Methods for Decision Making (Taylor & Francis, New York), 21–42.Google Scholar
  • Woodford M (2014) Stochastic choice: An optimizing neuroeconomic model. Amer. Econom. Rev. 104(5):495–500.CrossrefGoogle Scholar
  • Wyer RS, Srull RK (1986) Human cognition in its social context. Psychol. Rev. 93(3):322–359.CrossrefGoogle Scholar
  • Yang L (C), Toubia O, De Jong MG (2015) A bounded rationality model of information search and choice in preference measurement. J. Marketing Res. 52(2):166–183.CrossrefGoogle Scholar
  • Yang L (C), Toubia O, De Jong MG (2018) Attention, information processing, and choice in incentive-aligned choice experiments. J. Marketing Res. 55(6):783–800.CrossrefGoogle Scholar
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