Consider or Choose? The Role and Power of Consideration Sets

Published Online:https://doi.org/10.1287/mnsc.2023.00442

References

  • Akchen Y-C, Mišić VV (2021) Assortment optimization under the decision forest model. Preprint, submitted March 25, https://arxiv.org/abs/2103.14067.Google Scholar
  • Aouad A, Farias V, Levi R (2021) Assortment optimization under consider-then-choose choice models. Management Sci. 67(6):3368–3386.LinkGoogle Scholar
  • Aouad A, Farias V, Levi R, Segev D (2018) The approximability of assortment optimization under ranking preferences. Oper. Res. 66(6):1661–1669.LinkGoogle Scholar
  • Barberá S, Pattanaik PK (1986) Falmagne and the rationalizability of stochastic choices in terms of random orderings. Econometrica 54(3):707–715.CrossrefGoogle Scholar
  • Ben-Akiva M, Boccara B (1995) Discrete choice models with latent choice sets. Internat. J. Res. Marketing 12(1):9–24.CrossrefGoogle Scholar
  • Ben-Akiva ME, Lerman SR (1985) Discrete Choice Analysis: Theory and Application to Travel Demand, vol. 9 (MIT Press, Cambridge, MA).Google Scholar
  • Berbeglia G, Garassino A, Vulcano G (2022) A comparative empirical study of discrete choice models in retail operations. Management Sci. 68(6):4005–4023.LinkGoogle Scholar
  • Blanchet J, Gallego G, Goyal V (2016) A Markov chain approximation to choice modeling. Oper. Res. 64(4):886–905.LinkGoogle Scholar
  • Block HD, Marschak J (1959) Random orderings and stochastic theories of response. Technical report, Cowles Foundation for Research in Economics, Yale University, New Haven, CT.Google Scholar
  • Brisoux JE, Laroche M (1981) Evoked set formation and composition: An empirical investigation under a routinized response behavior situation. Adv. Consumer Res. 8(1).Google Scholar
  • Bronnenberg BJ, Kruger MW, Mela CF (2008) Database paper: The IRI marketing data set. Marketing Sci. 27(4):745–748.LinkGoogle Scholar
  • Charnes A, Cooper WW (1962) Programming with linear fractional functionals. Naval Res. Logist. Quart. 9(3–4):181–186.CrossrefGoogle Scholar
  • Chen Y-C, Mišić VV (2022) Decision forest: A nonparametric approach to modeling irrational choice. Management Sci. 68(10):7090–7111.Google Scholar
  • Chitla S, Jagabathula S, Mitrofanov D, Cohen M (2023) Customers’ multihoming behavior in ride-hailing: Empirical evidence from Uber and Lyft. Preprint, submitted November 2, http://dx.doi.org/10.2139/ssrn.4591826.Google Scholar
  • Désir A, Goyal V, Zhang J (2022) Capacitated assortment optimization: Hardness and approximation. Oper. Res. 70(2):893–904.LinkGoogle Scholar
  • Désir A, Goyal V, Jagabathula S, Segev D (2021) Mallows-smoothed distribution over rankings approach for modeling choice. Oper. Res. 69(4):1206–1227.LinkGoogle Scholar
  • Echenique F, Saito K (2019) General luce model. Econom. Theory 68(4):811–826.CrossrefGoogle Scholar
  • Falmagne J-C (1978) A representation theorem for finite random scale systems. J. Math. Psych. 18(1):52–72.CrossrefGoogle Scholar
  • Farias VF, Jagabathula S, Shah D (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.LinkGoogle Scholar
  • Feldman J, Paul A, Topaloglu H (2019) Assortment optimization with small consideration sets. Oper. Res. 67(5):1283–1299.LinkGoogle Scholar
  • Garey MR, Johnson DS (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness (W. H. Freeman, San Francisco).Google Scholar
  • Gensch DH (1987) A two-stage disaggregate attribute choice model. Marketing Sci. 6(3):223–239.LinkGoogle Scholar
  • Gilbride TJ, Allenby GM (2004) A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Sci. 23(3):391–406.LinkGoogle Scholar
  • Graham RL (1995) Handbook of Combinatorics (Elsevier, Amsterdam).Google Scholar
  • Håstad J (1999) Clique is hard to approximate within n1−ε. Acta Mathematica 182(1):105–142.Google Scholar
  • Hauser JR (1978) Testing the accuracy, usefulness, and significance of probabilistic choice models: An information-theoretic approach. Oper. Res. 26(3):406–421.LinkGoogle Scholar
  • Hauser JR (2014) Consideration-set heuristics. J. Bus. Res. 67(8):1688–1699.CrossrefGoogle Scholar
  • Hauser JR, Gaskin SP (1984) Application of the “defender” consumer model. Marketing Sci. 3(4):327–351.LinkGoogle Scholar
  • Hauser JR, Wernerfelt B (1990) An evaluation cost model of consideration sets. J. Consumer Res. 16(4):393–408.CrossrefGoogle Scholar
  • Hausman J, McFadden D (1984) Specification tests for the multinomial logit model. Econometrica 52(5):1219–1240.CrossrefGoogle Scholar
  • Hogarth RM, Karelaia N (2005) Simple models for multiattribute choice with many alternatives: When it does and does not pay to face trade-offs with binary attributes. Management Sci. 51(12):1860–1872.LinkGoogle Scholar
  • Howard JA, Sheth JN (1969) The Theory of Buyer Behavior (John Wiley & Sons, New York).Google Scholar
  • Hutchinson JMC, Gigerenzer G (2005) Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet. Behav. Processes 69(2):97–124.CrossrefGoogle Scholar
  • Iyengar SS, Lepper MR (2000) When choice is demotivating: Can one desire too much of a good thing? J. Personality Soc. Psych. 79(6):995.CrossrefGoogle Scholar
  • Jagabathula S, Mitrofanov D, Vulcano G (2024) Demand estimation under uncertain consideration sets. Oper. Res. 72(1):19–42.LinkGoogle Scholar
  • Jedidi K, Kohli R (2005) Probabilistic subset-conjunctive models for heterogeneous consumers. J. Marketing Res. 42(4):483–494.CrossrefGoogle Scholar
  • Keeney RL, Raiffa H, Meyer RF (1993) Decisions with Multiple Objectives: Preferences and Value Trade-Offs (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Kim Y, Knight B, Mitrofanov D, Xu Y (2024) Algorithm-enabled decision support and worker learning: A large-scale field experiment. Preprint, submitted October 12, https://doi.org/10.2139/ssrn.4976809.Google Scholar
  • Knight B, Mitrofanov D (2025) Disclosing low product availability: An online platform’s strategy for mitigating stockout risk. Management Sci., ePub ahead of print June 23, https://doi.org/10.1287/mnsc.2022.01808.LinkGoogle Scholar
  • Knight B, Mitrofanov D, Netessine S (2023) Human-algorithm collaboration in gig work: The role of experience, skill level, and task complexity. Preprint, submitted March 3, https://dx.doi.org/10.2139/ssrn.4372368.Google Scholar
  • Kok AG, Fisher ML, Vaidyanathan R (2008) Assortment planning: Review of literature and industry practice. Retail Supply Chain Management 122(1):99–153.CrossrefGoogle Scholar
  • Laroche M, Kim C, Matsui T (2003) Which decision heuristics are used in consideration set formation? J. Consumer Marketing 20(3):192–209.CrossrefGoogle Scholar
  • Luce RD (2012) Individual Choice Behavior: A Theoretical Analysis (Courier Corporation, North Chelmsford, MA).Google Scholar
  • Manzini P, Mariotti M (2014) Stochastic choice and consideration sets. Econometrica 82(3):1153–1176.CrossrefGoogle Scholar
  • McFadden D, Richter MK (1990) Stochastic rationality and revealed stochastic preference. Preferences, Uncertainty, and Optimality, Essays in Honor of Leo Hurwicz. (Westview Press, Boulder, CO), 161–186.Google Scholar
  • McFadden D (1973) Conditional logit analysis of qualitative choice behavior. Zarembka P, ed. Frontiers in Econometrics (Academic Press, New York), 105–142.Google Scholar
  • Miller GA (1956) The magic number seven plus or minus two: Some limits on our capacity for processing information. Psych. Rev. 63:91–97.CrossrefGoogle Scholar
  • Mitrofanov D, Topaloglu H, Wang Y (2024) Choice modeling, assortment optimization, and estimation when customers are non-rational: Multinomial logit model with non-parametric dominance. Preprint, submitted September 30, https://doi.org/10.2139/ssrn.4958971.Google Scholar
  • Pras B, Summers J (1975) A comparison of linear and nonlinear evaluation process models. J. Marketing Res. 12(3):276–281.CrossrefGoogle Scholar
  • Ratchford BT (1982) Cost-benefit models for explaining consumer choice and information seeking behavior. Management Sci. 28(2):197–212.LinkGoogle Scholar
  • Rieskamp J, Busemeyer JR, Mellers BA (2006) Extending the bounds of rationality: Evidence and theories of preferential choice. J. Econom. Literature 44(3):631–661.CrossrefGoogle Scholar
  • Roberts JH, Lattin JM (1991) Development and testing of a model of consideration set composition. J. Marketing Res. 28(4):429–440.CrossrefGoogle Scholar
  • Roberts JH, Lattin JM (1997) Consideration: Review of research and prospects for future insights. J. Marketing Res. 34(3):406–410.CrossrefGoogle Scholar
  • Rusmevichientong P, Shmoys D, Tong C, Topaloglu H (2014) Assortment optimization under the multinomial logit model with random choice parameters. Production Oper. Management 23(11):2023–2039.CrossrefGoogle Scholar
  • Şen A, Atamtürk A, Kaminsky P (2018) A conic integer optimization approach to the constrained assortment problem under the mixed multinomial logit model. Oper. Res. 66(4):994–1003.LinkGoogle Scholar
  • Sher I, Fox JT, Kim K il, Bajari P (2011) Partial identification of heterogeneity in preference orderings over discrete choices. NBER Working Paper No. 17346, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Shocker AD, Ben-Akiva M, Boccara B, Nedungadi P (1991) Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions. Marketing Lett. 2:181–197.CrossrefGoogle Scholar
  • Silk AJ, Urban GL (1978) Pre-test-market evaluation of new packaged goods: A model and measurement methodology. J. Marketing Res. 15(2):171–191.CrossrefGoogle Scholar
  • Simon HA (1955) A behavioral model of rational choice. Quart. J. Econom. 69(1):99–118.CrossrefGoogle Scholar
  • Şimşek AS, Topaloglu H (2018) An expectation-maximization algorithm to estimate the parameters of the Markov chain choice model. Oper. Res. 66(3):748–760.LinkGoogle Scholar
  • Swait J, Ben-Akiva M (1987) Incorporating random constraints in discrete models of choice set generation. Transportation Res. Part B Methodological 21(2):91–102.CrossrefGoogle Scholar
  • Talluri K, Van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50(1):15–33.LinkGoogle Scholar
  • Thurstone LL (1927) A law of comparative judgment. Psych. Rev. 34(4):273.CrossrefGoogle Scholar
  • Train KE (2009) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).Google Scholar
  • Tversky A (1972) Elimination by aspects: A theory of choice. Psych. Rev. 79(4):281.CrossrefGoogle Scholar
  • van Ryzin G, Vulcano G (2014) A market discovery algorithm to estimate a general class of nonparametric choice models. Management Sci. 61(2):281–300.LinkGoogle Scholar
  • van Ryzin G, Vulcano G (2017) An expectation-maximization method to estimate a rank-based choice model of demand. Oper. Res. 65(2):396–407.LinkGoogle Scholar
  • Wang R, Sahin O (2018) The impact of consumer search cost on assortment planning and pricing. Management Sci. 64(8):3649–3666.LinkGoogle Scholar
  • Wright P, Barbour F (1977) Phased decision strategies: Sequels to initial screening. Starr MK, Zeleny M, eds. Multiple Criteria Decision Making: TIMS Studies in the Management Science, vol. 6 (North-Holland, Amsterdam), 91–109.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.