The Focal Multinomial Logit Model: Threshold Effects on Consumer Choice, Assortment, Pricing and Estimation

References

• Ahumada A, Ülkü L (2018) Luce rule with limited consideration. Math. Soc. Sci. 93:52–56.Crossref, Google Scholar
• Aouad A, Farias V, Levi R (2021) Assortment optimization under consider-then-choose choice models. Management Sci. 67(6):3368–3386.Link, Google Scholar
• Bartlett PL, Mendelson S (2003) Rademacher and Gaussian complexities: Risk bounds and structural results. J. Machine Learn. Res. 3:463–482.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.Link, Google Scholar
• Blanchet J, Gallego G, Goyal V (2016) A Markov chain approximation to choice modeling. Oper. Res. 64(4):886–905.Link, Google Scholar
• Blumer A, Ehrenfeucht A, Haussler D, Warmuth MK (1989) Learnability and the Vapnik-Chervonenkis dimension. J. ACM 36(4):929–965.Crossref, Google Scholar
• Bodea T, Ferguson M, Garrow L (2009) Data set—Choice-based revenue management: Data from a major hotel chain. Manufacturing Service Oper. Management 11(2):356–361.Link, Google Scholar
• Bonami P, Biegler LT, Conn AR, Cornuéjols G, Grossmann IE, Laird CD, Lee J, et al. (2008) An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optim. 5(2):186–204.Crossref, Google Scholar
• Cavanaugh JE, Neath AA (2019) The Akaike information criterion: Background, derivation, properties, application, interpretation, and refinements. WIREs Comput. Statist. 11(3):e1460.Crossref, Google Scholar
• Chen YC, Mišić VV (2022) Decision forest: A nonparametric approach to modeling irrational choice. Management Sci. 68(10):7090–7111.Link, Google Scholar
• Coey C, Lubin M, Vielma J (2020) Outer approximation with conic certificates for mixed-integer convex problems. Math. Programming Comput. 12:1–45.Crossref, Google Scholar
• Dean M (2009) Status Quo Bias in Large and Small Choice Sets (New York University, New York).Google Scholar
• Désir A, Goyal V, Zhang J (2022) Capacitated assortment optimization: Hardness and approximation. Oper. Res. 70(2):893–904.Link, Google Scholar
• Dong L, Kouvelis P, Tian Z (2009) Dynamic pricing and inventory control of substitute products. Manufacturing Service Oper. Management 11(2):317–339.Link, Google Scholar
• Dong J, Simsek AS, Topaloglu H (2019) Pricing problems under the Markov chain choice model. Production Oper. Management 28(1):157–175.Crossref, Google Scholar
• Duran MA, Grossmann IE (1986) An outer-approximation algorithm for a class of mixed-integer nonlinear programs. Math. Programming 36(3):307–339.Crossref, Google Scholar
• Echenique F, Saito K (2019) General Luce model. Econom. Theory 68:811–826.Crossref, Google Scholar
• Feldman J, Davis JM, Paul A (2018) Assortment optimization and pricing under a nonparametric tree choice model. Manufacturing Service Oper. Management 20(3):550–565.Link, Google Scholar
• Gallego G, Topaloglu H (2019) Revenue Management and Pricing Analytics (Springer, Berlin).Crossref, Google Scholar
• Goutam K, Goyal V, Soret A (2019) A generalized Markov chain model to capture dynamic preferences and choice overload. Preprint, submitted November 15, https://doi.org/10.48550/arXiv.1911.06716.Google Scholar
• Grant M (2025) Understanding the halo effect: Definition, history, and impact. Investopedia (November 12), https://www.investopedia.com/terms/h/halo-effect.asp.Google Scholar
• Hansen BE (2000) Sample splitting and threshold estimation. Econometrica 68(3):575–603.Crossref, Google Scholar
• Hauser JR (2014) Consideration-set heuristics. J. Bus. Res. 67(8):1688–1699.Crossref, Google Scholar
• Hedgcock WM, Rao RS, Chen HA (2016) Choosing to choose: The effects of decoys and prior choice on deferral. Management Sci. 62(10):2952–2976.Link, Google Scholar
• Jagabathula S, Rusmevichientong P (2019) The limit of rationality in choice modeling: Formulation, computation, and implications. Management Sci. 65(5):2196–2215.Abstract, Google Scholar
• Jagabathulam S, Mitrofanov D, Vulcano G (2024) Demand estimation under uncertain consideration sets. Oper. Res. 72(1):19–42.Link, Google Scholar
• Jena SD, Lodi A, Sole C (2022) On the estimation of discrete choice models to capture irrational customer behaviors. INFORMS J. Comput. 34(3):1606–1625.Link, Google Scholar
• Kivetz R, Netzer O, Srinivasan V (2004) Alternative models for capturing the compromise effect. J. Marketing Res. 41(3):237–257.Crossref, Google Scholar
• Kosorok MR (2008) Introduction to Empirical Processes and Semiparametric Inference, Springer Series in Statistics (Springer, New York).Crossref, Google Scholar
• Kovach M, Tserenjigmid G (2022) The focal Luce model. Amer. Econom. J. Microeconom. 14(3):378–413.Crossref, Google Scholar
• Lee J, Chu W (2022) The effect of adding focal-goal similar versus dissimilar attributes on convergence product purchase decision: The role of relational and item-specific elaboration style. J. Consumer Behav. 21(2):296–309.Crossref, Google Scholar
• Li H, Huh WT (2011) Pricing multiple products with the multinomial logit and nested logit models: Concavity and implications. Manufacturing Service Oper. Management 13(4):549–563.Link, Google Scholar
• Li G, Rusmevichientong P, Topaloglu H (2015) The d-level nested logit model: Assortment and price optimization problems. Oper. Res. 63(2):325–342.Link, Google Scholar
• Luce RD (1959) Individual Choice Behavior: A Theoretical Analysis (Wiley, New York).Google Scholar
• Ma Z, Gill T, Jiang Y (2015) Core versus peripheral innovations: The effect of innovation locus on consumer adoption of new products. J. Marketing Res. 52(3):309–324.Crossref, Google Scholar
• Maragheh RY, Chronopoulou A, Davis JM (2018) A customer choice model with halo effect. Preprint, submitted May 4, https://doi.org/10.48550/arXiv.1805.01603.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
• Newey WK, McFadden D (1994) Large sample estimation and hypothesis testing. Handbook Econometrics 4:2111–2245.Crossref, Google 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.Crossref, Google Scholar
• Scheibehenne B, Greifeneder R, Todd PM (2010) Can there ever be too many options? A meta-analytic review of choice overload. J. Consumer Res. 37(3):409–425.Crossref, Google Scholar
• Simon HA (1955) A behavioral model of rational choice. Quart. J. Econom. 69(1):99–118.Crossref, Google Scholar
• Şimşek AS, Topaloglu H (2018) Technical note—An expectation-maximization algorithm to estimate the parameters of the Markov chain choice model. Oper. Res. 66(3):748–760.Link, Google Scholar
• Talluri K, van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Management Sci. 51(1):15–33.Link, Google Scholar
• van der Vaart A (1998) Asymptotic Statistics (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Wang R (2022) The threshold effects on consumer choice and pricing decisions. Manufacturing Service Oper. Management 24(1):448–466.Link, Google Scholar
• Wang R, Sahin O (2018) The impact of consumer search cost on assortment planning and pricing. Management Sci. 64(8):3649–3666.Link, Google Scholar
• Wang R, Zhao Z, Ke C (2022) Modeling consumer choice and optimizing assortment under the threshold multinomial logit model. Working paper, Johns Hopkins University, Baltimore.Google Scholar
• Yu P (2012) Likelihood estimation and inference in threshold regression. J. Econom. 167(1):274–294.Crossref, Google Scholar
• Zhang H, Rusmevichientong P, Topaloglu H (2020) Assortment optimization under the paired combinatorial logit model. Oper. Res. 68(3):741–761.Link, Google Scholar