Predictive Testing of Consumer Choice Models Not Subject to Independence of Irrelevant Alternatives

1982 ◽  
Vol 19 (2) ◽  
pp. 208 ◽  
Author(s):  
Imran S. Currim
Keyword(s):  
Consumer Choice ◽  
Predictive Testing ◽  
Choice Models ◽  
Independence Of Irrelevant Alternatives

Predictive Testing of Consumer Choice Models Not Subject to Independence of Irrelevant Alternatives

1982 ◽  
Vol 19 (2) ◽  
pp. 208-222 ◽  
Author(s):  
Imran S. Currim
Keyword(s):  
San Francisco ◽  
Consumer Choice ◽  
Predictive Testing ◽  
Probit Model ◽  
New Product Introduction ◽  
Product Introduction ◽  
Independence Property ◽  
Cross Sectional ◽  
Bay Area ◽  
Independence Of Irrelevant Alternatives

The probabilistic modeling of the relationship between objective or perceived characteristics of multiattribute alternatives and consumer choice is receiving increased attention in marketing and other disciplines. Many marketing applications use the Luce choice axiom, the LOGIT model, and the independent PROBIT model. All these formulations involve the “independence of irrelevant alternatives” assumption, which is not realistic in many consumer behavior contexts. The author suggests new product introduction situations in which the consequences of the assumption are not intuitively appealing. Models recently developed by transportation researchers and one extension developed by the author, all of which are not constrained by the independence restriction and may be applicable in modeling consumer choice, are described. In an empirical application, cross-sectional data on consumers’ perceptions of transportation modes on several characteristics and their choices of modes in the San Francisco Bay area are used to compare models with and without the independence property on diagnostic and predictive criteria.

Data pruning in consumer choice models

2006 ◽  
Vol 4 (3) ◽  
pp. 267-287 ◽  
Author(s):  
Elaine L. Zanutto ◽  
Eric T. Bradlow
Keyword(s):  
Consumer Choice ◽  
Choice Models ◽  
Data Pruning

Operations Management under Consumer Choice Models with Multiple Purchases

2020 ◽  
Author(s):  
Shujie Luan ◽  
Ruxian Wang ◽  
Xiaolin Xu ◽  
Weili Xue
Keyword(s):  
Operations Management ◽  
Consumer Choice ◽  
Choice Models

Consumer Choice Models and Estimation: A Review and Extension

2021 ◽  
Author(s):  
Qi Feng ◽  
J. George Shanthikumar ◽  
Mengying Xue
Keyword(s):  
Consumer Choice ◽  
Choice Models

Consumer Choice Models with Endogenous Network Effects

2017 ◽  
Vol 63 (11) ◽  
pp. 3944-3960 ◽  
Author(s):  
Ruxian Wang ◽  
Zizhuo Wang
Keyword(s):  
Consumer Choice ◽  
Network Effects ◽  
Choice Models

INFLUENCE OF OMITTED VARIABLES IN CONSUMER CHOICE MODELS ON ENGINEERING DESIGN OPTIMIZATION SOLUTIONS

2021 ◽  
pp. 1-20
Author(s):  
Waleed Gowharji ◽  
Kate Whitefoot
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Consumer Choice ◽  
Optimization Problems ◽  
Revealed Preference ◽  
Choice Models ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
Engineering Design Optimization ◽  
The Impact

Abstract This paper examines the impact of Omitted Variable Bias (OVB) within consumer choice models on engineering design optimization solutions. Engineering products often have a multitude of attributes that influence consumers' purchasing decisions, many of which are difficult to include in revealed-preference models due to a lack of data. Correlations among these omitted variables and product attributes included in the model can bias demand parameter estimates. However, engineering design optimization studies typically do not account for this bias. We examine the influence consumer-choice OVB can have on design optimization results. We first mathematically derive how OVB propagates into optimal design solutions and characterize properties of optimization problems that affect the magnitude of the resulting error in solutions. We then demonstrate the impact of OVB on optimal designs using an engineering optimization case study of automotive powertrain design. In the demonstration, we estimate two sets of choice models: one using only “typically observed” vehicle attributes commonly found in the literature, and one with an additional set of “typically unobserved” attributes gathered from Edmunds.com. We find that the model with omitted variables leads to, in some scenarios, substantial bias in parameter estimates (5-143%), which propagates up to 21% error in the optimal engine size.

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