IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v117y2020icp44-57.html
   My bibliography  Save this article

An integrated modelling approach examining the influence of goals, habit and learning on choice using visual attention data

Author

Listed:
  • Blake, Miranda R.
  • Dubey, Subodh
  • Swait, Joffre
  • Lancsar, Emily
  • Ghijben, Peter

Abstract

Previous economics literature has explored the role of visual attention on choice in isolation without accounting for other influences such as habits and goals or learning effects, nor their interrelationship. In this paper, we: (i) develop a novel joint framework to explore the relationship between visual attention, observed heterogeneity from stated habits and goals, and choice outcomes while accounting for shorter- and longer-term learning effects; and (ii) investigate whether accounting for these relationships improves model predictive power and behavioral insights. The empirical analysis used an eye-tracked discrete choice experiment on sugar-sweetened beverage purchasing (n = 152 adults with 20 choice tasks). Results suggest that habits, goals, and shorter-term learning are key drivers of information acquisition whereas cumulative choices (longer-term learning) affect subsequent choice outcome. Importantly, ignoring the joint relationship between habits, visual attention and choice may exaggerate the role of visual attention, leading to incorrect behavioral insights and reduced prediction accuracy.

Suggested Citation

  • Blake, Miranda R. & Dubey, Subodh & Swait, Joffre & Lancsar, Emily & Ghijben, Peter, 2020. "An integrated modelling approach examining the influence of goals, habit and learning on choice using visual attention data," Journal of Business Research, Elsevier, vol. 117(C), pages 44-57.
    • Handle: RePEc:eee:jbrese:v:117:y:2020:i:c:p:44-57
    DOI: 10.1016/j.jbusres.2020.04.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296320302642
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbusres.2020.04.040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kelvin Balcombe & Iain Fraser & Eugene McSorley, 2015. "Visual Attention and Attribute Attendance in Multi‐Attribute Choice Experiments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 447-467, April.
    2. Ellen J Van Loo & Rodolfo M NaygaJr & Danny Campbell & Han-Seok Seo & Wim Verbeke, 2018. "Using eye tracking to account for attribute non-attendance in choice experiments," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(3), pages 333-365.
    3. Blake, Miranda R. & Lancsar, Emily & Peeters, Anna & Backholer, Kathryn, 2019. "Sugar-sweetened beverage price elasticities in a hypothetical convenience store," Social Science & Medicine, Elsevier, vol. 225(C), pages 98-107.
    4. Mandy Ryan & Nicolas Krucien & Frouke Hermens, 2018. "The eyes have it: Using eye tracking to inform information processing strategies in multi‐attributes choices," Health Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 709-721, April.
    5. Xavier Gabaix & David Laibson & Guillermo Moloche & Stephen Weinberg, 2006. "Costly Information Acquisition: Experimental Analysis of a Boundedly Rational Model," American Economic Review, American Economic Association, vol. 96(4), pages 1043-1068, September.
    6. Ben-Elia, Eran & Shiftan, Yoram, 2010. "Which road do I take? A learning-based model of route-choice behavior with real-time information," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 249-264, May.
    7. Erdem, Tulin & Broniarczyk, Susan & Charavarti, Dipankar & Kapferer, Jean-Noel & Keane, Michael & Roberts, John & Steenkamp, Jan-Benedict & Swait, Joffre & Zettelmeyer, Florian, 1999. "Brand Equity, Consumer Learning and Choice," MPRA Paper 53022, University Library of Munich, Germany.
    8. Patil, Priyadarshan N. & Dubey, Subodh K. & Pinjari, Abdul R. & Cherchi, Elisabetta & Daziano, Ricardo & Bhat, Chandra R., 2017. "Simulation evaluation of emerging estimation techniques for multinomial probit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 9-20.
    9. Ran, Tao & Yue, Chengyan & Rihn, Alicia, 2015. "Are Grocery Shoppers of Households with Weight-Concerned Members Willing to Pay More for Nutritional Information on Food?," Journal of Food Distribution Research, Food Distribution Research Society, vol. 46(3), pages 1-18, November.
    10. Peter Craig, 2008. "A new reconstruction of multivariate normal orthant probabilities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 227-243, February.
    11. Bhat, Chandra R., 2015. "A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 50-77.
    12. Kenneth Train ., 2000. "Halton Sequences for Mixed Logit," Economics Working Papers E00-278, University of California at Berkeley.
    13. Tversky, Amos & Thaler, Richard H, 1990. "Anomalies: Preference Reversals," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 201-211, Spring.
    14. Hajivassiliou, Vassilis & McFadden, Daniel & Ruud, Paul, 1996. "Simulation of multivariate normal rectangle probabilities and their derivatives theoretical and computational results," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 85-134.
    15. Bunch, David S., 1991. "Estimability in the Multinomial Probit Model," University of California Transportation Center, Working Papers qt1gf1t128, University of California Transportation Center.
    16. Elena Reutskaja & Rosemarie Nagel & Colin F. Camerer & Antonio Rangel, 2011. "Search Dynamics in Consumer Choice under Time Pressure: An Eye-Tracking Study," American Economic Review, American Economic Association, vol. 101(2), pages 900-926, April.
    17. Bhat, Chandra R. & Pinjari, Abdul R. & Dubey, Subodh K. & Hamdi, Amin S., 2016. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 240-263.
    18. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    19. Cristiano Varin & Paolo Vidoni, 2005. "A note on composite likelihood inference and model selection," Biometrika, Biometrika Trust, vol. 92(3), pages 519-528, September.
    20. Valenzuela, Ana & Raghubir, Priya & Mitakakis, Chrissy, 2013. "Shelf space schemas: Myth or reality?," Journal of Business Research, Elsevier, vol. 66(7), pages 881-888.
    21. Krucien, Nicolas & Ryan, Mandy & Hermens, Frouke, 2017. "Visual attention in multi-attributes choices: What can eye-tracking tell us?," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 251-267.
    22. Colin F. Camerer & Teck-Hua Ho & Juin-Kuan Chong, 2004. "A Cognitive Hierarchy Model of Games," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(3), pages 861-898.
    23. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    24. Bhat, Chandra R. & Dubey, Subodh K., 2014. "A new estimation approach to integrate latent psychological constructs in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 68-85.
    25. Bunch, David S., 1991. "Estimability in the multinomial probit model," Transportation Research Part B: Methodological, Elsevier, vol. 25(1), pages 1-12, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ladeira, Wagner Junior & de Oliveira Santini, Fernando & Pinto, Diego Costa, 2022. "Clockwise versus counterclockwise turning bias: Moderation effects of foot traffic and cognitive experience on visual attention," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dubey, Subodh & Bansal, Prateek & Daziano, Ricardo A. & Guerra, Erick, 2020. "A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 114-141.
    2. Subodh Dubey & Prateek Bansal & Ricardo A. Daziano & Erick Guerra, 2019. "A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel," Papers 1904.08332, arXiv.org, revised Jan 2020.
    3. Erlend Dancke Sandorf & Danny Campbell, 2019. "Accommodating satisficing behaviour in stated choice experiments," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 46(1), pages 133-162.
    4. Dubey, Subodh & Sharma, Ishant & Mishra, Sabyasachee & Cats, Oded & Bansal, Prateek, 2022. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 63-95.
    5. Yegoryan, Narine & Guhl, Daniel & Klapper, Daniel, 2018. "Inferring Attribute Non-Attendance Using Eye Tracking in Choice-Based Conjoint Analysis," Rationality and Competition Discussion Paper Series 111, CRC TRR 190 Rationality and Competition.
    6. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.
    7. Tinessa, Fiore, 2021. "Closed-form random utility models with mixture distributions of random utilities: Exploring finite mixtures of qGEV models," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 262-288.
    8. Yegoryan, Narine & Guhl, Daniel & Klapper, Daniel, 2020. "Inferring attribute non-attendance using eye tracking in choice-based conjoint analysis," Journal of Business Research, Elsevier, vol. 111(C), pages 290-304.
    9. Bhat, Chandra R. & Mondal, Aupal, 2022. "A New Flexible Generalized Heterogeneous Data Model (GHDM) with an Application to Examine the Effect of High Density Neighborhood Living on Bicycling Frequency," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 244-266.
    10. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    11. Logar, Ivana & Brouwer, Roy & Campbell, Danny, 2020. "Does attribute order influence attribute-information processing in discrete choice experiments?," Resource and Energy Economics, Elsevier, vol. 60(C).
    12. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    13. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    14. Michael Scheidler & Reinhard Hujer & Joachim Grammig, 2005. "Discrete choice modelling in airline network management," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 467-486.
    15. Jeffrey E. Harris & Mariana Gerstenblüth & Patricia Triunfo, 2018. "Smokers’ Rational Lexicographic Preferences for Cigarette Package Warnings: A Discrete Choice Experiment with Eye Tracking," Documentos de Trabajo (working papers) 0218, Department of Economics - dECON.
    16. Edenbrandt, Anna Kristina & Lagerkvist, Carl-Johan & Lüken, Malte & Orquin, Jacob L., 2022. "Seen but not considered? Awareness and consideration in choice analysis," Journal of choice modelling, Elsevier, vol. 45(C).
    17. Ziegler, Andreas, 2001. "Simulated z-tests in multinomial probit models," ZEW Discussion Papers 01-53, ZEW - Leibniz Centre for European Economic Research.
    18. Rennings, Klaus & Ziegler, Andreas & Zwick, Thomas, 2001. "Employment changes in environmentally innovative firms," ZEW Discussion Papers 01-46, ZEW - Leibniz Centre for European Economic Research.
    19. Cohen, Michael, 2010. "A Structured Covariance Probit Demand Model," Research Reports 149970, University of Connecticut, Food Marketing Policy Center.
    20. GRAMMIG, Joachim & HUJER, Reinhard & SCHEIDLER, Michael, 2001. "The econometrics of airline network management," LIDAM Discussion Papers CORE 2001055, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

    More about this item

    Keywords

    Eye-tracking; Habit; Sugar-sweetened beverage; Choice; Preference; Joint-modelling;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:117:y:2020:i:c:p:44-57. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.