Customer Needs and Solutions

, Volume 5, Issue 1–2, pp 82–92 | Cite as

Advancing Non-compensatory Choice Models in Marketing

  • Anocha Aribarg
  • Thomas Otter
  • Daniel ZantedeschiEmail author
  • Greg M. Allenby
  • Taylor Bentley
  • David J. Curry
  • Marc Dotson
  • Ty Henderson
  • Elisabeth Honka
  • Rajeev Kohli
  • Kamel Jedidi
  • Stephan Seiler
  • Xin (Shane) Wang
Research Article


The extant choice literature has proposed different non-compensatory rules as a more realistic description of consumers’ choice than a standard compensatory model. Some research has further suggested a two-stage sequential decision process of non-compensatory consideration and then compensatory choice, where the determinants of each stage may differ. Some aspects of non-compensatory choice modeling are under-studied. In this article, we hope to advance the understanding of non-compensatory choice models with the following aims: (a) providing an overview of existing representations for non-compensatory choice decisions, (b) discussing how such choice decisions can manifest from the economic search theoretical perspective, (c) exploring the empirical identification of non-compensatory decisions using different data, and (d) presenting applications of non-compensatory choice models in novel domains.


Non-compensatory Lexicographic Elimination by aspects Search Identification Error theory Direct utility 


  1. 1.
    Agarwal MK, Green PE (1991) Adaptive conjoint analysis versus selfexplicated models: some empirical results. In: International Journal of Research in Marketing 8(2):141–146Google Scholar
  2. 2.
    Allenby GM, Ginter JL (1995) Using extremes to design products and segment markets. In: Journal of Marketing Research 32(4):392–403Google Scholar
  3. 3.
    Andrews R, Manrai AK (1998) Feature-Based Elimination: Model and Empirical Comparison. In: European Journal of Operational Research 111(2):248–267Google Scholar
  4. 4.
    Andrews RL, Manrai AK (1999) MDS maps for product attributes and market response: an application to scanner panel data. In: Marketing Science 18(4):584–604Google Scholar
  5. 5.
    Andrews RL, Srinivasan TC (1995) Studying consideration effects in empirical choice models using scanner panel data. In: Journal of Marketing Research 32(1):30–41Google Scholar
  6. 6.
    Aribarg A, Arora N, Henderson T, Kim Y (2014) Private label imitation of a national brand: implications for consumer choice and law. In: Journal of Marketing Research 51(6):657–675Google Scholar
  7. 7.
    Aribarg A, Burson K, Larrick RP (2017) Tipping the scale: discriminability. Effect on derived attribute importance. In: Journal of Marketing Research 54(2):279–292Google Scholar
  8. 8.
    Batsell RR, Polking JR (1985) A new class of market share models. In: Marketing Science 4(3):177–197Google Scholar
  9. 9.
    Batsell RR, Polking JC, Cramer RD, Miller CM (2003) Useful mathematical relationships embedded in Tversky’s elimination by aspects model. In: Journal of Mathematical Psychology 47(5):538–544Google Scholar
  10. 10.
    Baye MR, Rupert Gattu J, Kattuman P, Morgan J (2009) Clicks, discontinuities, and firm demand online. In: Journal of Economics & Management Strategy 18(4):935–975Google Scholar
  11. 11.
    Ben-Akiva M, Boccara B (1995) Discrete choice models with latent choice sets. In: International Journal of Research in Marketing 12(1):9–24Google Scholar
  12. 12.
    Bentley T, Seetharaman PB (2017) Identifying unobserved similarity: estimating a fully flexible EBA model with standard marketing data. In: Working paper. University of, Texas at AustinGoogle Scholar
  13. 13.
    Berry ST, Haile PA (2009) Nonparametric identification of multinomial choice demand models with heterogeneous consumers. In: Working paper 15276. National Bureau of Economic ResearchGoogle Scholar
  14. 14.
    Bettman JR, Luce MF, Payne JW (1988) Constructive consumer choice processes. In: Journal of Consumer Research 25(3):187–217Google Scholar
  15. 15.
    Bronnenberg BJ, Vanhonacker WR (1996) Limited choice sets, local price response and implied measures of price competition. In: Journal of Marketing Research 33(2):163–173Google Scholar
  16. 16.
    Burdett K, Judd KL (1983) Equilibrium price dispersion. In: Econometrica 51(4):955–969Google Scholar
  17. 17.
    Busemeyer JR, Forsyth B, Nozawa G (1988) Comparisons of elimination by aspects and suppression of aspects choice models based on choice response time. In: Journal of Mathematical Psychology 32(3):341–349Google Scholar
  18. 18.
    Chandukala SR, Kim J, Otter T, Rossi PE, Allenby GM (2008) Choice models in marketing: economic assumptions, challenges and trends. In: Foundations and Trends in Marketing 2(2):97–184Google Scholar
  19. 19.
    Chen Y, Yang S (2007) Estimating disaggregate models using aggregate data through augmentation of individual choice. In: Journal of Marketing Research 44(4):613–621Google Scholar
  20. 20.
    Chen Y, Yao S (2016) Search with refinement. In: Management Science Article in AdvanceGoogle Scholar
  21. 21.
    Chiang J, Chib S, Narasimhan C (1998) Markov chain Monte Carlo and models of consideration set and parameter heterogeneity. In: Journal of Econometrics 89(1–2):223–248Google Scholar
  22. 22.
    Clithero JA, Rangel A (2015) Combining response times and choice data using a neuroeconomic model of the decision process improves out-of-sample predictions. In: unpublished, California Institute of Technology (2015).Google Scholar
  23. 23.
    Coombs CH (1951) Mathematical models in Psychological scaling. In: Journal of the American Statistical Association 46(256):480–489Google Scholar
  24. 24.
    Curry D, Wang X (2017) Commentary on “Benefit-based conjoint analysis” by Kim et al. 2016. In: Working paper University of Cincinnati. Lindner College of Business, Department of Marketing, pp 1–37Google Scholar
  25. 25.
    Dawes RM (1964) Social selection based on multidimensional criteria. In: Journal of Abnormal and Social Psychology 68:104–109Google Scholar
  26. 26.
    Dehmamy K, Otter T (2017) Consideration versus utility when choice is discrete continuous. In: Working paper Goethe UniversityGoogle Scholar
  27. 27.
    Desai KK, Hoyer WD (2000) Descriptive characteristics of memory-based consideration sets: influence of usage occasion frequency and usage location familiarity. In: Journal of Consumer Research 27(3):309–323Google Scholar
  28. 28.
    Fader PS, McAlister L (1990) An elimination by aspects model of consumer response to promotion calibrated on UPC scanner data. In: Journal of Marketing Research 27(3):322–332Google Scholar
  29. 29.
    Fishburn PC (1974) Exceptional paper-lexicographic orders, utilities and decision rules: a survey. In: Management Science 20(11):1442–1471Google Scholar
  30. 30.
    Gaissmaier W, Fific M, Rieskamp J (2011) Analyzing response times to understand decision processes. In: A handbook of process tracing methods for decision research: a critical review and user’s guide. Ed. by Schulte-Mecklenbeck, M., Küh- berger, A., and Ranyard, R. New York: Psychology Press, pp. 141–162.Google Scholar
  31. 31.
    Gensch DH (1987) A two-stage disaggregate attribute choice model. In: Marketing Science 6(3):223–239Google Scholar
  32. 32.
    Gensch DH, Ghose S (1992) Elimination by Dimensions. In: Journal of Marketing Research 29(4):417–429Google Scholar
  33. 33.
    Gigerenzer G, Goldstein DG (1996) Reasoning the fast and frugal way: models of bounded rationality. In: Psychological review 103(4):650–669Google Scholar
  34. 34.
    Gilbride TJ, Allenby GM (2004) A choice model with conjunctive, disjunctive, and compensatory screening rules. In: Marketing Science 23(3):391–406Google Scholar
  35. 35.
    Gilbride TJ, Allenby GM (2006) Estimating heterogeneous EBA and economic screening rule choice models. In: Marketing Science 25(5):494–509Google Scholar
  36. 36.
    Gilbride TJ, Currim IS, Mintz O, Siddarth S (2016) A model for inferring market preferences from online retail product information matrices. In: Journal of Retailing 92(4):470–485Google Scholar
  37. 37.
    Goeree MS (2008) Limited information and advertising in the U.S. personal computer industry. In: Econometrica 76(5):1017–1074Google Scholar
  38. 38.
    Hagerty MR, Aaker DA (1984) A normative model of consumer information processing, In: Marketing Science 84 (3):227–246Google Scholar
  39. 39.
    Hauser JR, Wernerfelt B (1990) An evaluation cost model of consideration sets. In: Journal of Consumer Research 16(4):393–408Google Scholar
  40. 40.
    Haviv A (2015) Does purchase without search explain counter-cyclic pricing? In: Simon School of Business working paperGoogle Scholar
  41. 41.
    Heckman JJ, Vytlacil E (2005) Structural equations, treatment effects, and econometric policy evaluation. In: Econometrica 73(3):669–738Google Scholar
  42. 42.
    Honka E (2014) Quantifying search and switching costs in the US auto insurance industry. In: The RAND Journal of Economics 45(4):847–884Google Scholar
  43. 43.
    Honka E, Chintagunta PK (2017) Simultaneous or sequential? Search strategies in the US auto insurance industry. In: Marketing Science 36(1)21-42Google Scholar
  44. 44.
    Jedidi K, Kohli R (2005) Probabilistic subset-conjunctive models for heterogeneous consumers. In: Journal of Marketing Research 42(4):483–494Google Scholar
  45. 45.
    Jedidi K, Kohli R, DeSarbo WS (1996) Consideration sets in conjoint analysis. In: Journal of Marketing Research 33(3):364–372Google Scholar
  46. 46.
    Johnson EJ, Payne JW, Bettman JR (1988) Information displays and preference reversals. In: Organizational Behavior and Human Decision Processes 42(1):1–21Google Scholar
  47. 47.
    Johnson EJ, Meyer RJ, Ghose S (1989) When choice models fail: compensatory models in negatively correlated environments. In: Journal of Marketing Research 26(3):255–270Google Scholar
  48. 48.
    Kim JB, Albuquerque P, Bronnenberg BJ (2010) Online demand under limited consumer search. In: Marketing Science 29(6):1001–1023Google Scholar
  49. 49.
    Kim DS, Bailey R, Hardt N, Allenby GM (2017) Benefit-based conjoint analysis. In: Marketing Science 36(1):54–69Google Scholar
  50. 50.
    Kohli R, Jedidi K (2015) Error theory for elimination by aspects. In: Operations Research 63(3):512–526Google Scholar
  51. 51.
    Kohli R, Jedidi K (2017) Relations between EBA and nested logit models. In: Operations Research ForthcomingGoogle Scholar
  52. 52.
    Manrai AK, Sinha P (1989) Elimination-By-Cutoffs. In: Marketing Science 8(2):133–152Google Scholar
  53. 53.
    Marley AAJ, Colonius H (1992) The “horse race” random utility model for choice probabilities and reaction times, and its compering risks interpretation. In: Journal of Mathematical Psychology 36(1):1–20Google Scholar
  54. 54.
    McFadden D (1973) University of California, Berkeley and Institute of Urban & Regional Development, Conditional logit analysis of qualitative choice behavior. Calif.: Institute of Urban and Regional Development, University of California, BerkeleyGoogle Scholar
  55. 55.
    McFadden D (1981) Econometric models of probabilistic choice. In: Manski C, Mc Fadden D (eds) Structural analysis of discrete data with econometric applications. MIT Press, Cambridge, MA, pp. 198–272Google Scholar
  56. 56.
    Mehta N, Rajiv S, Srinivasan K (2003) Price uncertainty and consumer search: a structural model of consideration set formation. In: Marketing Science 22(1):58–84Google Scholar
  57. 57.
    Meyer RJ, Sathi A (1985) A multiattribute model of consumer choice during product learning. In: Marketing Science 4(1):41–61Google Scholar
  58. 58.
    Montgomery H, Svenson O (1976) On decision rules and information processing strategies for choices among multiattribute alternatives. In: Scandinavian Journal of Psychology 17(1):283–291Google Scholar
  59. 59.
    Nedungadi P (1990) Recall and consumer consideration sets: influencing choice without altering brand evaluations. In: Journal of Consumer Research 17(3):263–276Google Scholar
  60. 60.
    Nierop V, Erjen BB, Wedel M, Frances PH (2010) Retrieving unobserved consideration sets from household panel data. In: Journal of Marketing Research 47(1):63–74Google Scholar
  61. 61.
    Otter T, Allenby GM, van Zandt T (2008) An integrated model of discrete choice and response time. In: Journal of Marketing Research 45(5):593–607Google Scholar
  62. 62.
    Payne JW, Bettman JR, Johnson EJ (1988) Adaptive strategy selection in decision making. In: Journal of Experimental Psychology: Learning, Memory, and Cognition 14(3):534Google Scholar
  63. 63.
    Pires T (2016) Costly search and consideration sets in storable good markets. In: Quantitative Marketing and Economics 14(3):157–193Google Scholar
  64. 64.
    Ratchford BT (2009) Consumer search behavior and its effect on markets. Now Publishers Inc isbn: 1601982003Google Scholar
  65. 65.
    Reiss PC (2011) Structural workshop paper—descriptive, structural, and experimental empirical methods in marketing research. In: Marketing Science 30(6):950–964Google Scholar
  66. 66.
    Roberts JH, Lattin JM (1991) Development and testing of a model of consideration set composition. In: Journal of Marketing Research:429–440Google Scholar
  67. 67.
    Rothschild M (1974) Searching for the lowest price when the distribution of prices is unknown. In: Journal of Political Economy 82(4):689–711Google Scholar
  68. 68.
    Rotondo J (1986) Technical note—Price as an aspect of choice in EBA. In: Marketing Science 5(4):391–402Google Scholar
  69. 69.
    Russo JE, Leclerc F (1994) An eye-fixation analysis of choice processes for consumer nondurables. In: Journal of Consumer Research 21(2):274–290Google Scholar
  70. 70.
    De los Santos B, Hortacsu A, Wildenbeest MR (2012) Testing models of consumer search using data on web browsing and purchasing behavior. In: American Economic Review 102(6):2955–2980Google Scholar
  71. 71.
    Seiler S (2013) The impact of search costs on consumer behavior: a dynamic approach. In: Quantitative Marketing and Economics 11(2):155–203Google Scholar
  72. 72.
    Seiler S, Pinna F (2016) Consumer search: evidence from path-tracking data. In: Marketing Science Article in AdvanceGoogle Scholar
  73. 73.
    Shugan SM (1980) The cost of thinking. In: Journal of Consumer Research 7(2):99–111Google Scholar
  74. 74.
    Siddarth S, Bucklin RE, Morrison DG (1995) Making the cut: modeling and analyzing choice set restriction in scanner panel data. In: Journal of Marketing Research 32(3):255–266Google Scholar
  75. 75.
    Simon HA (1955) A behavioral model of rational choice. In: The Quarterly Journal of Economics 69(1):99–118Google Scholar
  76. 76.
    Stigler GJ (1961) The economics of information. In: The Journal of Political Economy 69(3):213–225Google Scholar
  77. 77.
    Stüttgen P, Boatwright P, Monroe RT (2012) A satisficing choice model. In: Marketing Science 31(6):878–899Google Scholar
  78. 78.
    Toubia O, Hauser JR, Simester DI (2004) Polyhedral methods for adaptive choice-based conjoint analysis. In: Journal of Marketing Research 41(1):116–131Google Scholar
  79. 79.
    Tversky A, Sattah S (1979) Preference trees. In: Psychological Review 86:542–573Google Scholar
  80. 80.
    Townsend JT (1990) Serial vs. parallel processing: sometimes they look like Tweedledum and Tweedledee but they can (and should) be distinguished. In: Psychological Science 1(1):46–54Google Scholar
  81. 81.
    Tversky A (1972) Choice by elimination. In: Journal of mathematical psychology 9(4):341–367Google Scholar
  82. 82.
    Urban GL, Johnson PL, Hauser JR (1984) Testing competitive market structures. In: Marketing Science 3(2):83–112Google Scholar
  83. 83.
    Wang X (2017) Uncovering unobserved heterogeneity using mixtures of neutral networks. In: Working paper. Ivey School of BusinessGoogle Scholar
  84. 84.
    Weitzman ML (1979) Optimal search for the best alternative. In: Econometrica 47(3):641–654Google Scholar
  85. 85.
    Wolpin KI (2013) The limits of inference without theory. MIT Press, CambridgeGoogle Scholar
  86. 86.
    Yang L, Toubia O, De Jong MG (2015) A bounded rationality model of information search and choice in preference measurement. In: Journal of Marketing Research 52(April):166–183Google Scholar
  87. 87.
    Yee M, Dahan E, Houser JR, Orlin J (2007) Greedoid-based noncompensatory inference. In: Marketing Science 26(4):532–549Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Anocha Aribarg
    • 1
  • Thomas Otter
    • 2
  • Daniel Zantedeschi
    • 3
    Email author
  • Greg M. Allenby
    • 3
  • Taylor Bentley
    • 4
  • David J. Curry
    • 5
  • Marc Dotson
    • 6
  • Ty Henderson
    • 4
  • Elisabeth Honka
    • 7
  • Rajeev Kohli
    • 8
  • Kamel Jedidi
    • 8
  • Stephan Seiler
    • 9
  • Xin (Shane) Wang
    • 10
  1. 1.Ross School of BusinessUniversity of MichiganAnn ArborUSA
  2. 2.Goethe UniversityFrankfurt am MainGermany
  3. 3.Fisher College of BusinessThe Ohio State UniversityColumbusUSA
  4. 4.McCombs School of BusinessThe University of Texas at AustinAustinUSA
  5. 5.Lindner College of BusinessUniversity of CincinnatiCincinnatiUSA
  6. 6.Marriott School of ManagementBrigham Young UniversityProvoUSA
  7. 7.Anderson School of ManagementUniversity of California Los AngelesLos AngelesUSA
  8. 8.Graduate School of BusinessColumbia UniversityNew YorkUSA
  9. 9.Graduate School of BusinessStanford UniversityStanfordUSA
  10. 10.Ivey Business SchoolWestern UniversityLondonCanada

Personalised recommendations