Product Price, Quality and Service Decisions under Consumer Choice Models

2019 ◽  
Author(s):  
Ruxian Wang ◽  
Chenxu Ke ◽  
Shiliang Cui
Keyword(s):  
Consumer Choice ◽  
Choice Models ◽  
Product Price

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

scholarly journals Penerapan Algoritma ELECTRE pada Pemilihan Cream Pelembab Berdasarkan Konsumen

2020 ◽  
Vol 4 (1) ◽  
pp. 229
Author(s):  
Agus Perdana Windarto ◽  
Wida Prima Mustika
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Consumer Choice ◽  
Facial Skin ◽  
Product Price ◽  
Customer Commitment ◽  
Consumer Ratings ◽  
Electre Method ◽  
Selection Of

The aim of the study is to recommend the selection of a moisturizing cream using a decision support system ranking technique seen from consumer ratings. This research was conducted in the city of Pematangsiantar. Data obtained by observation to several stores to ensure the availability of moisturizing cream, interviews and observations to 250 consumers who were randomly conducted when they made a transaction to purchase a moisturizing cream. In this case, this research needs to be done considering that moisturizer is a drug used to make facial skin feel moist because with skin that feels moist will make women avoid various problems such as blackheads and acne. Keeping facial skin moist and oil free is not an easy thing for users to do. In addition, the number of moisturizing cream products that are currently making many women confused in choosing a moisturizing cream. Therefore researchers used a Decision Support System (SPK) with the ELECTRE algorithm in recommending the selection of a moisturizing cream based on consumer ratings. In this case the researchers used several assessment criteria including: product price (C1), side effects of usage (C2), product quality (C3), customer commitment (C4), customer trust (C5) and usage reaction (C6). While the alternatives used include: Citra Hazeline (A1), Fair & Lovely (A2), Garnier (A3), Olay (A4), Sariayu (A5) and Wardah (A6). The results of the assessment using the ELECTRE method are Fair & Lovely (A2) and Wardah (A6) as the best moisturizing cream recommendations based on consumer choice

Optimal and Competitive Assortments with Endogenous Pricing Under Hierarchical Consumer Choice Models

2011 ◽  
Vol 57 (9) ◽  
pp. 1546-1563 ◽  
Author(s):  
A. Gürhan Kök ◽  
Yi Xu
Keyword(s):  
Consumer Choice ◽  
Choice Models

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

A Control Function Approach to Endogeneity in Consumer Choice Models

2010 ◽  
Vol 47 (1) ◽  
pp. 3-13 ◽  
Author(s):  
Amil Petrin ◽  
Kenneth Train
Keyword(s):  
Consumer Choice ◽  
Control Function ◽  
Choice Models ◽  
Function Approach ◽  
Control Function Approach

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.

Sign in / Sign up

Export Citation Format

Share Document